THE IMPACT OF ORGANIZATIONAL DOWNSIZING ON CORPORATE

AND STRATEGIC BUSINESS UNIT ECONOMIC PERFORMANCE:

AN EMPIRICAL INVESTIGATION OF THE ROLE

OF WORK FORCE REDUCTION

by

CATHERINE A. DURAN, B.S., M.S., M.B.A.

A DISSERTATION

IN

BUSINESS ADMINISTRATION

Submitted to the Graduate Faculty of Texas Tech University in

Partial Fulfillment of the Requirements for

the Degree of

DOCTOR OF PHILOSOPHY

Approved

Chairperson of the Committee

Accepted

Dedtn of the Graduate School

May, 1998

Copyright 1998, Catherine A. Duran

ACKNOWLEDGMENTS

I would like to thank all the members of ray committee.

Dr. Robert L. Phillips, Dr. Roy D. Howell, Dr. Grant T.

Savage, and Dr. Carlton J. Whitehead for their advice and

never-ending support during the course of my doctoral

program. I am especially grateful to my major professor. Dr.

Phillips, whose knowledge and expertise guided me throughout

my work. I have learned so much from him, not only for this

study, but also for my professional development. I would

also like to especially thank Dr. Howell for giving me his

valuable time on the statistical analysis for this study.

I would like to express deep appreciation to vinitia

Mathews, whose understanding and encouragement were paramount

to this endeavor; and to John Ryan, without whose technical

expertise, programming knowledge and good humor, I could not

have completed this study.

Thanks to my parents for their lasting belief in me, and

for providing so much help in so many ways, without whom this

accomplishment would not be possible. I want to especially

thank my husband, Steve, for his continued support,

encouragement and patience throughout this work, again

without whom I could not have been successful. Finally, I

would like to thank my son, Robert, for keeping me happy and

providing the best reason of all for this achievement.

11

TABLE OF CONTENTS

ACKNOWLEDGMENTS ii

ABSTRACT V

LIST OF TABLES vii

CHAPTER

I. INTRODUCTION 1

Significance of the Study 7

Organization of the Dissertation 8

II. LITERATURE REVIEW 9

Organizational Size 12

Decline in Organizations 16

Organizational Size and Decline 28

Corporate Restructuring 29

Divestment 33

Downsizing Practices 35

Gaps in the Reviewed Literature 42

III. RATIONALE 45

The Point at the End of the Cornucopia 45

Research Strategy 48

IV. METHODOLOGY 49

Data 49

Performance Measures 51

Downsizing Measures 53

Control Measures 54

111

Method of Analysis 57

Empirical Model 58

V. DATA ANALYSIS 60

Descriptive Statistics 60

Time Series Cross-sectional Regression Analysis 63

Base Year Performance as Independent Variables 71

Pooled Cross-sectional Regression Results 7 3

VI. DISCUSSION AND CONCLUSIONS 128

Conclusions 130

Implications 140

Limitations and Strengths 144

Directions for Future Research 146

REFERENCES 148

IV

ABSTRACT

The relationship between downsizing and performance has

not been studied thoroughly, despite the prevalence of

downsizing in the U.S. and overseas. The downsizing issue

continues to be a topic of interest for the nation and the

business world. The streamlining of organizations has become

a perceived necessity in gaining a competitive edge in the

marketplace; however, it has not been clear whether

downsizing does indeed improve profitability. This study

addresses the downsizing-performance link, using

comprehensive multi-year, multi-industry data, and provides a

beginning to understanding the growing (both in occurrence

and importance) phenomenon of downsizing.

The study employs multivariate analysis for the inclusion

of many specific variables that have an impact on economic

performance of an organization. The impact of downsizing (as

workforce reduction) on both corporate and strategic business

unit (SBU) performance is studied, controlling for market

conditions. The study employs both an accounting measure and

a hybrid market/accounting measure of performance to

investigate, not only the impact of downsizing on

organizational profitability, but also on the perceptions of

the market.

The study shows that when controlling for other factors

that affect organizational performance, downsizing has some

negative and some positive effects on SBU and corporate

performance, and that these effects persist for a limited

period of time. Downsizing appears to have some of the

positive effects presented in the anecdotal literature;

however, there appear to be important negative ramifications

of downsizing as well.

VI

LIST OF TABLES

5.1 Descriptive Statistics (SBU datasets) 80

5.2 Descriptive Statistics (CORP datasets) 81

5.3 Correlation Matrices (SBU datasets) 82

5.4 Correlation Matrices (CORP datasets) 83

5.5 Number of SBU's/CORPS by Year 84

5-6 Years of Data Available for TSCS Regressions 85

5.7 Time Series Regression Results (SBU)--All Variables Included 86

5.8 Lagged Time Series Regression Results (SBU)--All Variables Included 87

5.9 Time Series Regression Results (SBU)--ADVINT Excluded 88

5.10 Lagged Time Series Regression Results (SBU)--ADVINT Excluded 89

5.11 Time Series Regression Results (SBU)--RDINT Excluded 90

5.12 Lagged Time Series Regression Results (SBU)--RDINT Excluded 91

5.13 Time Series Regression Results (SBU)--RDINT and ADVINT Excluded 92

5.14 Lagged Time Series Regression Results (SBU)--RDINT and ADVINT Excluded 93

5.15 Time Series Regression Results (CORP)--All Variables Included 94

5.16 Lagged Time Series Regression Results (CORP)--All Variables Included (ROA as Dependent Variable)...95

5.17 Time Series Regression Results (CORP)--ADVINT Excluded 96

5.18 Lagged Time Series Regression Results (CORP)--ADVINT Excluded. (ROA as Dependent Variable) 97

vu

5.19 Time Series Regression Results (CORP)--RDINT Excluded 98

5.20 Lagged Time Series Regression Results (CORP)--RDINT Excluded (ROA as Dependent Variable) 99

5.21 Time Series Regression Results (CORP)--RDINT and ADVINT Excluded 100

5.22 Lagged Time Series Regression Results (CORP)--RDINT and ADVINT Excluded (ROA as Dependent Variable) 101

5.23 Lagged Time Series Regression Results (CORP)--All Variables Included (TOBQ as Dependent Variable).102

5.24 Lagged Time Series Regression Results (CORP)--ADVINT Excluded. (TOBQ as Dependent Variable) 103

5.25 Lagged Time Series Regression Results (CORP)--RDINT Excluded (TOBQ as Dependent Variable) 104

5.26 Lagged Time Series Regression Results (CORP)--RDINT and ADVINT Excluded (TOBQ as Dependent Variable) 105

5.27 Special Lagged Regression Results (SBU)--Base Year ROA as Independent Variable 106

5.28 Special Lagged Regression Results (CORP)--Base Year ROA as Independent Variable 107

5.29 Special Lagged Regression Results (CORP)--Base Year TOBQ as Independent Variable 108

5.30 Concurrent and Lagged (0-2) Regression Results (SBU)--All Variables Included (ROA as Dependent Variable) 109

5.31 Concurrent and Lagged (3-5) Regression Results (SBU)--All Variables Included (ROA as Dependent Variable) 110

5.32 Concurrent and Lagged (0-2) Regression Results (SBU)--RDINT Excluded (ROA as Dependent Variable) Ill

5.33 Concurrent and Lagged (3-5) Regression Results (SBU)--RDINT Excluded (ROA as Dependent Variable) 112

Vlll

5.34 Concurrent and Lagged (0-2) Regression Results (SBU)--RDINT and ADVINT Excluded (ROA as Dependent Variable) 113

5.35 Concurrent and Lagged (3-5) Regression Results (SBU)--RDINT and ADVINT Excluded (ROA as Dependent Variable) 114

5.36 Concurrent and Lagged (0-2) Regression Results (CORP)--All Variables Included (ROA as Dependent Variable) 115

5.37 Concurrent and Lagged (3-5) Regression Results (CORP)--All Variables Included (ROA as Dependent Variable) 116

5.38 Concurrent and Lagged (0-2) Regression Results (CORP)--RDINT Excluded (ROA as Dependent Variable) 117

5.39 Concurrent and Lagged (3-5) Regression Results (CORP)--RDINT Excluded (ROA as Dependent Variable) 118

5.40 Concurrent and Lagged (0-2) Regression Results (CORP)--RDINT and ADVINT Excluded (ROA as Dependent Variable) 119

5.41 Concurrent and Lagged (3-5) Regression Results (CORP)--RDINT and ADVINT Excluded (ROA as Dependent Variable) 120

5.42 Concurrent and Lagged (0-2) Regression Results (CORP)--All Variables Included (TOBQ as Dependent Variable) 121

5.43 Concurrent and Lagged (3-5) Regression Results (CORP)--All Variables Included (TOBQ as Dependent Variable) 122

5.44 Concurrent and Lagged (0-2) Regression Results (CORP)--RDINT Excluded (TOBQ as Dependent Variable) 123

5.45 Concurrent and Lagged (3-5) Regression Results (CORP)--RDINT Excluded (TOBQ as Dependent Variable) 124

5.46 Concurrent and Lagged (0-2) Regression Results (CORP)--RDINT and ADVINT Excluded (TOBQ as Dependent Variable) 125

IX

5.47 Concurrent and Lagged (3-5) Regression Results (CORP)--RDINT and ADVINT Excluded (TOBQ as Dependent Variable) 126

CHAPTER I

INTRODUCTION

This Study explores the relationship between downsizing

and performance. In this study, downsizing is characterized

as a facet of organizational strategy for improving

performance (Cameron, Freeman, & Mishra, 1993). Although the

subtleties of defining organizational downsizing will be

discussed later, in this research downsizing is defined as an

intentional set of activities designed to improve

organizational performance, and which involves reductions in

personnel (Cameron, Freeman, & Mishra, 1993).

Since the 1980's through the present time, around ten

million jobs have been eliminated in the U.S. (Budros, 1997).

Organizational downsizing has been called a "key feature" of

the system of "new capitalism," an economy characterized by

international competition, deregulation of industries, and

technological change (Budros, 1997). It is not clear whether

downsizing does indeed improve profitability, since despite

the prevalence of downsizing, it has not been studied much

(Cameron, 1994). Budros (1997) points out that expectations

are that downsizing will improve performance, and that

companies that downsized are generally seen as positive role

models and acquire social benefits, whether or not operating

efficiencies are achieved. However, there are critics of

downsizing who point out various adverse effects, as well as

more emphasis on growth in recent years (Budros, 1997).

Nevertheless, numerous companies have undergone varying

degrees of downsizing. Over eighty-five percent of the

Fortune 1000 corporations have undergone downsizing (Cameron,

Freeman, & Mishra, 1991). For example, companies that have

downsized extensively include Eastman Kodak (five times in

seven years eliminating over 12,000 jobs). Zenith Electronics

(50 percent of its workforce), UNISYS (50 percent), ITT (over

40 percent), K-Mart (over 20 percent), Peat Marwick (over 20

percent), AT&T (over 10 percent), Sears, Roebuck, & Co. (over

10 percent),and Westinghouse (6 percent) (Cameron, Freeman, &

Mishra, 1991; Lesly & Light, 1992). The downsizing trend is

continuing; among companies with current and future plans of

downsizing are IBM, Xerox, the Postal Service, TRW, Inc., and

General Motors (Cascio, 1993). The 1990's are still filled

with reports of more downsizing (e.g., Uchitelle &

Kleinfield, 1996).

The downsizing issue continues to be a topic of interest

for the nation and the business world. For example, one of

the largest and best known computer businesses in the world,

IBM, has been forced to reevaluate its strategies for

competing in the global market. In an interview with Fortune

(Kirkpatrick, 1993), IBM's new CEO Lou Gerstner says his

first priority is to "get the company right-sized," which

implies more downsizing. Gerstner also says that the

decision on the magnitude and placement of further downsizing

will be at the business unit level. It is not only premiere

U.S. corporations that are facing this phenomenon; Daimler-

Benz of Germany has also announced plans for a workforce

reduction.

The streamlining of organizations has become a

perceived necessity in gaining a competitive edge in the

marketplace; however, it is not clear whether downsizing does

indeed improve profitability. Lesly and Light (1992) point

out that although some downsized companies (e.g., Unisys

Corp., which also narrowed its market) have benefited, many

restructured and so-called "lean" corporations do not appear

to have garnered the long-term expected earnings benefits

from their work-force reductions (e.g., Eastman Kodak, Zenith

Electronics, Sears Roebuck). Interestingly, only one of the

top ten in Fortune's 1993 Ranking of Most Admired

Corporations has fewer employees than it did 5 years ago

(Henkoff, 1993).

Despite the prevalence of downsizing in the U.S. and

overseas, studies of downsizing are relatively sparse in the

strategy and organizational theory literature. The

consequences of downsizing on micro-level issues (e.g.,

usually in human resources and organizational behavior areas)

have, however, been researched, but only to a degree. For

example, studies have been done on survivors of work-force

reductions, and on relating layoffs with motivation (e.g..

Brockner, Davy, & Carter, 1988), self-esteem (e.g., Brockner,

Grover, O'Malley, Reed, & Glynn, 1993, Brockner, 1995;

Daniels, 1995), and organizational commitment (e.g., Wong &

Davis, 1993; Wong & McNally, 1994) . Other work has been done

on how to manage the effects of workforce reductions on those

who remain with the organization, including steps to be taken

before, during, and after the layoffs (Brockner, 1992).

Maintaining the morale of survivors is discussed as an

essential element in successfully implementing mergers and

acquisitions and the attendant layoffs (Gutknecht & Keys,

1993). The phenomenon of survivor guilt has also been

explored (e.g., Brockner, Grover, Reed, DeWitt, & O'Malley,

1987) . Specific issues such as Total Quality Management have

been considered in the literature in relationship to

downsizing (Niven, 1993).

Research on motives behind downsizing actions is just

now beginning to emerge. Kozlowski, Chao, Smith, and Hedlund

(1993) point out that there are many causes and intended

goals associated with downsizing. Harrison (p. 40, 1994)

suggests that downsizing could be explained by diverse

reasons such as "vertical disintegration" of large firms

seeking to escape "bad business climates," an overall shift

from large manufacturing firms to smaller service firms, and

conglomerates trying to return to their core competencies.

Many publications focus on the management of effective

downsizing, attempting to identify processes to implement

downsizing successfully (e.g., Tomasko, 1987; Cameron,

Freeman, & Mishra, 1991; Hendricks, 1992; De Meuse, Bergmann,

Sc Vanderheiden, 1997). Most of the literature on

implementation does not, however, address post-downsizing

organizational performance. In the view of some strategists,

performance issues should be included for research questions

to be of any consequence (Meyer, 1991). There are very few

systematic studies published on the precursors, effects, and

strategies associated with organizational downsizing

(Cameron, Freeman, & Mishra, 1991) . The most extensive and

systematic study of organizational downsizing is that of

Cameron, Freeman, and Mishra (1993), an in-depth field study

including perceptual performance criteria.

Another issue concerning the study of downsizing is that

of divestiture versus work-force reduction. Most of the

strategy literature on restructuring examines divestiture and

diversification refocusing as opposed to work force

reduction. Other studies consider size, but often in terms

of sales or assets instead of number of employees. The

operationalization of size varies greatly by study.

Organizational-size research has been reported in the

organizational theory literature concerning decreasing

financial resources and/or work-force reduction (e.g., see

McKinley, 1992; Sutton & D'Aunno, 1989, 1992). However, in

the organizational theory literature, the dependent variable

in most models is structural (e.g, administrative intensity)

in nature, rather than performance based.

Ginsberg (1988) characterizes empirical research in the

strategy field as addressing two basic questions: (1) "what

factors influence the occurrence of various types of

changes"; and (2) "what are the performance outcomes of these

various types of change." Ginsberg (1988) points out that

the strategy researcher, at some point, must examine

performance outcomes, as improving performance is a basic

tenet of strategic management (Venkatraman & Ramanujam,

1986). This study contributes to the field by focusing on

performance outcomes after downsizing occurs in corporations.

Most of the references to downsizing include anecdotal

evidence (with no clear support for either a negative or

positive effect on profitability) as to the impact of

downsizing. Given the limited amount of theoretical and

empirical work on the very topical and timely issue of

downsizing, especially linked with performance issues, the

primary research question for this study is: What is the

impact of downsizing on corporate and strategic business unit

economic performance, controlling for market conditions?

The use of comprehensive multi-year, multi-industry data

for the examination of performance issues should provide a

beginning to understanding the growing (in both occurrence

and importance) phenomenon of downsizing.

Significance of the Study

This study is significant for the strategic management

field, as well as for the field of organizational theory. It

is concerned with the effect of a strategic change (workforce

reduction) on the performance of organizations, which is of

importance to the strategy field. It also addresses issues

of size, which are clearly of interest in the field of

organizational theory.

The study employs multivariate analysis for the

inclusion of many specific variables that could have an

impact on economic performance of an organization. It uses a

large multi-industry database, which has a variety of

organizations of different sizes. The study provides

information on the relationship between downsizing and

profitability and on the impact of downsizing on the

organization. In summary, the study addresses an issue that

is currently short on theoretical concepts but is of great

importance, not only to the business world, but also to the

national economy.

Limitations

There are some limitations in this study arising from

the use of secondary data bases. For example, the researcher

cannot influence what types or forms of data are collected.

Some variables may not be available in the data base, and

there is little contextual information. Another limitation

to the study is that there is no hypothesis testing; however,

a tentative empirical model is tested.

Organization of the Dissertation

This dissertation has six chapters. Chapter I is the

Introduction. Chapter II contains a review of the relevant

literature in several related subfields of strategy and

organizational theory. Chapter III discusses the rationale

for the study. Chapter IV shows the methodology used, the

regression equations, and the operationalization of the

variables. Chapter V presents the results of the data

analysis. Chapter VI discusses the conclusions based on the

results, and offers implications and strengths and

limitations of the study. Chapter VI closes with directions

for future research.

8

CHAPTER II

LITERATURE REVIEW

The literature reviewed for this study, in the areas of

organizational size, decline, turnaround and retrenchment,

corporate restructuring, and divestiture does not reflect a

consensus on how to conceptualize downsizing. There have

been few empirical research studies on downsizing, and very

little theoretical development. However, there are several

publications geared to practitioners on the implementation of

downsizing.

In addition, there is a small body of literature that

concerns itself with micro-level issues of downsizing, such

as the effect on layoff survivors and how managers should

treat survivors to increase work motivation (e.g., see

Brockner, Grover, O'Malley, Reed, & Glynn, 1993; Brockner,

1995; Daniels, 1995). Other researchers are looking at

layoffs, plant closings, and worker displacement on a

national level (see Hansen, 1988). Hansen (1988) is

concerned with legislation for a displaced worker adjustment

program as part of a comprehensive national employment and

training policy.

The strategic management literature has only just begun

to address the issue of downsizing per se; it appears that

downsizing itself may not have been viewed as a strategic

change. The literature on decline (e.g., see Cameron,

Sutton, & Whetten, 1988; D'Aveni, 1989, Weitzel & Jonsson,

1989) suggests that downsizing is not synonymous with

decline; rather downsizing may be a strategic response to

organizational decline (Greenhalgh, Lawrence, & Sutton, 1988)

or turbulence in the environment.

The corporate restructuring literature generally does

not acknowledge downsizing specifically as an aspect of

restructuring; it instead focuses on asset restructuring

(acquisitions and divestitures), capital restructuring

(infusion of debt), and management restructuring (changes in

organizational structure) (Singh, 1993). In this literature,

downsizing possibly may often be seen as an effect of

restructuring (Bowman & Singh, 1990; Hoskisson, Hitt, & Hill,

1991).

In spite of the scarcity of research on the impact of

downsizing on organizational economic performance, workforce

reduction is viewed as part of the process necessary for

long-term organizational improvements (Cascio, 1993). Many

popular business books and journals give advice to "cut out

the fat" and "get lean and mean" (Cascio, 1993). Tom Peters

(1992) argues for "rethinking scale," and that "big is

dead."

However, some doubt is emerging as to whether the

anticipated economic results of downsizing actually

materialize (Cascio, 1993; Lesly & Light, 1992). Studies by

consulting firms are showing that in many companies that have

10

downsized, expenses have not been reduced adequately, profits

have not increased as expected, and stock prices have not

necessarily gone up over the long term (AMA, 1993; Cascio,

1993; Lesly & Light, 1992). For example, the American

Management Association's (AMA) 1993 Survey on Downsizing

showed that fewer than half of the surveyed organizations

that had downsized since 1988 reported increased profits.

The question of whether downsizing has a positive or

negative relationship with corporate performance has not been

resolved. The American Management Association's (AMA) 1993

Survey on Downsizing showed that on the average, over the

last five years, 45% of the companies surveyed had downsized,

with a five-year average of a 10% reduction of the workforce.

Fewer than half of the surveyed organizations that had

downsized since 1988 reported increased profits. The AMA

(1993) points out that profit levels are affected by many

other variables in addition to workforce reductions. In

contrast, most surveys by consulting and investment firms

usually investigate only a bivariate relationship between

downsizing and profits, leaving out a host of other important

variables that may affect performance.

Neither the practitioner nor the academic literatures

offer theoretical frameworks in which downsizing may be

studied. Indeed, Cameron, Freeman, and Mishra (1993)

recommend that at this early stage of organizational

downsizing study, researchers should be building theories.

11

particularly including the association of downsizing with

successful organizational performance. The impact of

downsizing should be examined in light of strategic moves

(both corporate and SBU), as well as changes in the

environment (e.g., industry structure).

Organizational Size

This section reviews the relevant literature on

organizational size, primarily in the field of organizational

theory. Organizational size is one of the three core

contingency variables (along with technology and the

environment) studied extensively in the organizational theory

field (Bluedorn, 1993). Several basic propositions arose

from the dominant theoretical position of size; the

propositions were concerned with the relationship between

size and organizational structural variables, such as

structural differentiation, administrative proportion,

centralization, and formalization (Bluedorn, 1993). Some

studies have dealt with the relationship between size and

other variables, such as employee pay (Mellow, 1982; villemez

& Bridges, 1988), executive compensation (Gomez-Mejia, Tosi,

Sc Hankin, 1987; Rajagopalan & Prescott, 1990), and

absenteeism (Markham & McKee, 1991).

Other issues concerning size include diversification,

divisionalization, and innovation. There is a debate in the

organizational literature on whether size or diversification

12

strategy is more closely associated with divisionalization

(see Grinyer & Yasai-Ardekani, 1981; Child, 1982; Donaldson,

1982; Grinyer, 1982). Damanpour (1992) found a positive

relationship between size (measured in different ways) and

innovation in a meta-analytic study using 36 correlations.

It was found that the size-innovation relationship was

stronger when size was measured in terms of assets, rather

than number of employees. Damanpour (1992) also suggests

that there is a curvilinear relationship between size and

innovation.

However, there are relatively few studies that consider

performance and size. Christenson and Sachs (1980) found

that government size was positively correlated with perceived

quality of public services. Size was measured as: (1)

number of government employees in a county; (2) the ratio of

government employees to the total population of a county; and

(3) the number of public employees relative to the number of

administrative units. They also found that number of

administrative units was uncorrelated with quality.

A meta-analysis by Gooding and Wagner (1985) showed a

positive correlation between size and performance (absolute

measures), but not between size and efficiency (relative

output/input measures). Results varied with the measure of

size. Performance was measured in various ways including

profits, sales, and number of clients. Size was measured as

number of employees, the log of the number of employees.

13

capacity, assets, and transactions. There was a positive

correlation with performance only when size was

operationalized as the log of the number of employees. No

correlation was seen when size was operationalized as the raw

number of employees, nor as assets. There was a negative

correlation when size was operationalized as capacity or

transactions. Gooding and Wagner (1985) also found that at

the subunit level (as opposed to the organizational level),

there was a negative correlation between size and performance

(depending on the operationalization of performance). It is

evident from the results of the meta-analysis that the

empirical evidence on the relationship between size and

performance is mixed.

Interestingly, an empirical study by Smith, Guthrie, and

Chen (1989) showed that size moderated the relationship

between strategy and firm performance, with strategy measured

using the Miles and Snow (197 8) typology. However,

performance was measured using self-report data. Size was

measured as number of employees.

A meta-analysis was done by Capon, Farley, and Hoenig

(1990) on the determinants of financial performance, relating

environmental, strategic, and organizational factors to

performance. The studies in the meta-analysis analyzed

performance at three levels: industry (7 3 studies),

corporate (205 studies, 163 in multiple industries), and

business (42 studies). Size itself (measured in a variety

14

of ways, usually in terms of sales or assets, but never as

downsizing) appeared to be unrelated to financial performance

(Capon, Farley, & Hoenig, 199 0).

A study examining the relationship between size and

change (measured as expansion into new markets) found an

inverted U-shaped relationship between size and change for

some markets (Haveman, 1993). Haveman (1993) suggests that

size should not be conceptualized as solely an organizational

characteristic, as internal and external constraints vary

among settings, which could affect the relationships under

study.

An empirical study on diversification and corporate

restructuring by Chang (1996) showed that larger firms can

improve performance more than smaller firms, with various

entry and exit strategies. However, firm size was measured

as the log of total assets, rather than in terms of numbers

of employees.

The concept of downsizing certainly has some connection

with the literature on organizational size. However, most

studies on size do not consider changes in size within

organizations, which is inherent in any study of downsizing.

The operationalization of size is also open to question in

the literature; size is not often measured in terms of number

of employees. The literature does not thoroughly address the

relationship between size and performance, as there is little

agreement seen in empirical studies. Additionally, the

15

operationalization of performance is usually perceptual,

rather than objective, in nature.

Decline in Organizations

This section briefly considers the literature on

organizational decline. Within the stream of research on

phases of the organizational life cycle, particularly on

birth, death, and decline (Bluedorn, 1993), the decline phase

has emerged as a very important topic for business

researchers. The high number of overall business failures

and the extensive loss of jobs in the manufacturing

industries (Cameron, Sutton, & whetten, 1988) underscores the

significance of studying decline. However, organizational

decline has been defined in many ways, often ambiguously

(Cameron, Sutton, & Whetten, 1988). Also, downsizing and

retrenchment have frequently been confused with decline.

Cameron, Sutton, and whetten (1988) have suggested a

working definition of organizational decline that

distinguishes responses to decline from decline itself. In

their definition, deterioration of the organization's

adaptation to the microniche leads to reduction of resources

within the organization.

There are organizational, group, and individual

responses to organizational decline. Cameron, Sutton, and

Whetten (1988) clearly consider downsizing, along with

turnaround, divestment, and executive succession, as possible

16

organizational responses to decline. It is interesting to

note that downsizing could occur in response to either K-type

(shifting market demand results in a declining population) or

r-type (where the population is stable or growing) decline.

(See Wilson [1980] for detailed writings on K-type and r-type

deterioration.)

Zammuto and Cameron (1985), in a model based on

population ecology, propose four types of decline: erosion

(continuous change in niche size), contraction (discontinuous

change in niche size), dissolution (continuous change in

niche shape), and collapse (discontinuous change in niche

shape). They suggest that organizations in niches of

decreasing size would be likely candidates for cutback

activities (i.e., a type of structural adjustment). Zammuto

and Cameron (1985) also propose five domain-altering

strategic responses: domain defense (buffering from the

environment), domain offense (expanding markets or product

line), domain creation (diversifying or innovating), domain

consolidation (reducing size of domain and peripheral

actions), and domain substitution (replacing domain when

carrying capacity of original niche goes to zero).

Downsizing activities could be part of a domain consolidating

strategy, with change by deletion as the primary structural

adjustment, in response to a contraction type of decline.

Kozlowski, Chao, Smith, and Hedlund (1993) have combined

the Zammuto and Cameron (1985) dimensions with a model of

17

downsizing decision process formulated by Freeman and Cameron

(1993) . Freeman and Cameron (1993) use the Tushman and

Romanelli (1985) notions of convergence (long time spans of

incremental change) and reorientation (shorter periods of

discontinuous change). Kozlowski, Chao, Smith, and Hedlund

(1993) suggest that environmental variation can affect the

downsizing decision process, and have proposed that

convergent forms of downsizing are more likely to be

associated with changes in niche size, whereas reorientation

forms of downsizing are more likely to be associated with

changes in niche shape. DeWitt (1993) presents a normative

model of downsizing implementation, which includes four

downsizing strategy alternatives to be used when an

organization is facing organizational and/or environmental

decline. The strategies vary in terms of domain and

structural changes. However, the above models are primarily

concerned with processes, rather than performance outcomes,

of downsizing.

Cameron, Sutton, and Whetten (1988) further emphasize

the distinction between decline and downsizing or

retrenchment. Workforce reduction is a strategic response to

conditions caused by decline, but is not decline itself

(Greenhalgh, Lawrence, & Sutton, 1988). Furthermore,

downsizing can be either functional or dysfunctional

(Cameron, Sutton, & Whetten, 1988). If the wrong strategy is

chosen, or a strategy is poorly implemented, further erosion

18

in organizational adaptation and resource flow may occur. On

the other hand, downsizing could signal to customers and

stockholders that the organization is responding correctly to

decline. Cameron, Sutton, and Whetten (1988) point out that

a majority of Fortune 500 companies have downsized to some

degree, regardless of their objective growth or decline

pattern.

There is little integrative work on the consequences of

decline, with most of the literature relying on case studies

in single industries (Cameron, Sutton, & Whetten, 1988). Nor

has there been much work on the consequences of

organizational responses to decline, such as downsizing.

Ford (1980a) proposed a conceptual framework for looking at

the structural changes in declining organizations, where the

occurrence of structural hysteresis is explained. Ford

(19 80a) suggests the relationship between size and structure

is different depending on whether the organization is growing

or declining, but downsizing itself was not considered, and

performance outcomes were not included.

Some of the work that has been done on decline is

concerned with the relationship between decline and

organization variables such as technical and structural

complexity, administrative intensity (McKinley, 1987), and

centralization (Cameron, Whetten, & Kim 1987). McKinley

(1987) found that the positive relationship between

19

internal complexity and administrative intensity was

moderated by organizational growth and decline.

Ludwig (1993) investigated structural and organizational

adaptations of a population of a religious order under

conditions of decline (decreasing membership). In Ludwig's

(1993) study, retrenchment was characterized as either

cutback (percentage by which total organizational operations

were reduced) or reallocation (percentage of allocations to

individual units). Personnel changes were examined in terms

of admissions, departures, and transfers; while downsizing

itself was not addressed.

Others have investigated organizational decline in

relation to the environment. For example, Cameron, Kim, and

Whetten (1987) investigated the related constructs of

turbulence, stagnation and environmental decline. They base

their ideas on three streams of research: (1) organization-

environment literature, especially resource dependence; (2)

crisis-management literature; and (3) uncertainty literature.

They point out that the environment may or may not have

changed when a company goes into decline (as a reduction of

the resources within the organization itself). They also

view decline neutrally; i.e., the management of the decline

will determine positive or negative consequences (Cameron,

Kim, and Whetten, 1987).

An interesting study by Meyer (1982) showed that

organizations respond and adapt in many different ways to

20

environmental jolts (in this case, a doctors' strike

affecting numerous hospitals). Although hospital admission

and occupancy levels were drastically lowered, some hospital

administrators used employee layoffs as short-term solutions,

while others did not.

Harrigan (1980) suggests that firms in declining

industries follow different strategies based on environmental

traits. Divestiture is shown as part of one strategy (early

exit), but downsizing is not mentioned.

Hambrick and D'Aveni (1988) looked at dynamics of

corporate failure where poor performance was a significant

feature of a downward spiral. Weitzel and Jonsson (1991)

provide a model of decline that may serve as a frame of

reference for managers to try to ameliorate or reverse the

downward spiral.

Some researchers chose to study part of the process of

decline by investigating retrenchment as a response to

financial decline and also as an initial phase of a

turnaround strategy (see Robbins & Pearce, 1992). Robbins

and Pearce (1992) roughly classify several activities,

including restructuring, downsizing, and downscoping, as

retrenchment. They use the term retrenchment to primarily

signify cost and asset reductions, and they briefly mention

"head count cuts" as one of the possible strategies in the

retrenchment stage of the turnaround response model.

However, in the empirical test of their model, they did not

21

operationalize retrenchment as workforce reduction; instead

they used reduction of total costs between two times, and net

reduction in assets between two times (Robbins & Pearce,

1992). Turnaround performance was considered as the net

change in ROI between two times.

Barker and Mone (1994) take issue with the contention

that retrenchment is a cause of turnaround (as Robbins and

Pearce [1992] maintain) and suggest that retrenchment is

instead a consequence of organizational decline. Barker and

Mone (1994) believe that how an organization executes its

downsizing or retrenching activities is more important to

turnaround (i.e., performance recovery), than the actual

initiation of retrenchment programs. Again, retrenchment in

the Barker and Mone (1994) empirical study, was

operationalized as reduction in assets or costs, rather than

reduction in personnel.

There is a growing debate in the strategic management

literature on the organizational significance of retrenchment

and downsizing. Pearce and Robbins (1994) assert that the

Barker and Mone (1994) research actually supports the

original Robbins and Pearce (1992) turnaround process theory

with retrenchment as its cornerstone. The question of

retrenchment value has not been resolved.

A later study by Barker and Duhaime (1997) avers that

although some empirical studies demonstrate performance

turnarounds for declining firms resulting from cutback

22

activities (i.e., retrenchment) which increase efficiency,

strategic reorientation is vital to the recovery process of

declining firms. They present a model where the extent of

strategic change varies with the need for and capacity of

declining firms to reorient their strategies. In their

model, firms must have weak strategic postures for strategic

change to be critical for turnaround; i.e., levels of

strategic health, as well as the amount of strategic change,

must be identified and measured for a definitive turnaround

study. They also point out that the causes (or types) of

decline should be controlled for (e.g., firm-based decline

vs. industry-based decline). For example, extensive

strategic change is seen in turnarounds from firm-based

decline, whereas, little strategic change is seen in

industry-based decline. The latter is described in the study

as operational turnaround based on asset and cost reduction

as discussed by Hofer (1980). Also, the capacity for

strategic change is important, and can be controlled for by

firm size and diversification profile.

There appears to be a need for more empirical research

and theory building. Special care should be taken in the

operationalization of variables, particularly when dealing

with downsizing specifically, as has scarcely been done.

The strategic and managerial consequences of

organizational decline were studied by D'Aveni (1989). The

consequences reflected threat-rigidity responses. Like

23

others mentioned in this section, D'Aveni (1989) makes a

clear distinction between decline and downsizing. D'Aveni

(19 89) defines organizational decline as decreasing internal

resource munificence over time. His definition of downsizing

involves changes of organizational size and scope, including

selling off fixed assets, or subsidiaries (e.g.,

divestiture), and reducing product-market domains. However,

he does not explicitly include work-force reduction in his

definition of downsizing, nor in his operationalization in

the study.

D'Aveni (1989) also maintains that while many declining

organizations downsize, "downsizing and decline do not always

occur simultaneously or at the same rate" (p. 57 8). He also

concludes that the effectiveness of downsizing depends on

environmental conditions. Downsizing may give the

organization time to wait for environmental improvement, but

does not necessarily guarantee turnaround (D'Aveni, 1989).

D'Aveni (1989) also concludes that downsizing may become a

habit, "providing an illusion of temporary well-being" (p.

600). He found that, contrary to the idea that downsizing

could be a factor in creating slack for diversification

efforts and strategic change, post-decline managerial

imbalances inhibited the use of funds garnered from

downsizing efforts for successful turnaround strategies.

Murray and Jick (1985) studied six underfunded hospitals

over a period of five years, and documented responses of the

24

hospitals. They propose a framework for organizational

decline management, providing managers with a problem-solving

approach to resource scarcity threats, while recommending to

researchers that a holistic view must be taken (and a multi-

method approach) when studying organizational decline.

Other researchers have looked at decline and

retrenchment issues in terms of turnaround and rejuvenation.

Some have offered prescriptions. For example, Hofer (1980)

offers a framework for designing turnaround strategies at the

SBU level, in response to major declines in performance (in

terms of decreasing profitability, sales, or market share).

Hofer (19 80) distinguishes between operational and strategic

turnaround. Cutting costs or assets is termed operational in

nature, but downsizing itself is not considered.

Stopford and Baden-Fuller (1990) differentiate between

corporate turnaround (finance and efficiency oriented) and

corporate rejuvenation (concerned with both efficiency and

creation of sustainable growth). Detailed industry and case

analyses yielded insights on how corporate rejuvenation was

accomplished. Holistic changes in structure, strategy,

systems, technology, and individuals were required for

sustainable corporate rejuvenation (Stopford & Baden-Fuller,

1990).

Greenhalgh (1982) believes organizational retrenchment

occurs in a situation where an organization fails to adapt or

the carrying capacity of an environmental niche is reduced.

25

The organization then responds by cutting back its scale of

operations, and the size of the workforce is usually reduced

proportionally. Greenhalgh (1982) recommends an action

research program to facilitate comprehensive work force

planning and maintain organizational effectiveness.

Greenhalgh's (1982) action research program is designed

primarily to alleviate common problems arising from workforce

reduction, such as impaired job security. Organizational

effectiveness measures were self-report data on employee

productivity and propensity to leave the organization.

Much of the decline research looks at organizations

under conditions of decline and the concomitant unique

situational aspects and management challenges. Cameron,

Whetten, and Kim (1987) empirically explored a set of

dysfunctional characteristics (including many at the

individual and group levels) of organizations in decline.

Organizational dysfunctions associated with decline include

increases in conflict, secrecy, scapegoating, self-protective

behaviors, rigidity, turnover, decreases in morale,

innovativeness, participation, and long-term planning.

Weitzel and Jonsson (1989) point out an interesting

aspect of decline, in that cutback in terms of size (Whetten,

1980) does not necessarily negatively affect the survival an

organization. Whetten (1980) explains cutbacks in size as

rising from a reduction in the market in which the

organization operates, or from a decrease in the

26

organization's ability to compete with others in its market.

Weitzel and Jonsson (1989) suggest that cutback may not be a

form of decline; instead it may be "temporary adjustment that

is an appropriate response to the environment and enhances,

rather than diminishes, long-term viability" (p.93). This

would be in line with considering downsizing as a strategic

change, its organizational impact to be empirically

determined.

An interesting study by Wiseman and Bromiley (1996)

explores the relationship between decline, risk and

performance. They contrast two theoretical perspectives: a

decline theoretical approach which states that declining

organizations decrease risky activities, and the decreased

risk-taking leads to further poor performance, versus a risk

theoretical approach which states that declining firms take

more risks, and the increased risky behavior (again) leads to

more low performance. The ensuing empirical investigation

shows that when financial resources are decreased (i.e.,

organizational decline), risk-taking is increased, which in

turn leads to lower performance. Although performance is

central to the study, downsizing itself is not considered (as

financial resources are considered in terms of different

kinds of slack, none involving numbers of employees).

The literature on decline clearly shows that downsizing

and decline are not synonymous. Organizational downsizing is

often seen as a type of adaptation to organizational decline.

27

and is considered to be a subject of controversy (McKinley,

1993).

Organizational Size and Decline

This section extends the discussion on organizational

size and decline by briefly reviewing the relevant literature

on the relationship between decreasing size and

organizational decline. Much of the work in this area is

concerned with how to implement downsizing or workforce

reduction strategies. For example, Greenhalgh, Lawrence, and

Sutton (1988) introduce five strategies for workforce

reduction, along with sample tactics for implementing the

strategies. Further, they offer a set of propositions on

which strategies will be chosen, depending on labor

oversupply characteristics, and on contextual factors such as

skill levels, age and seniority, company structure

(multidivisional vs. unitary), and ownership.

Another study looked at the relationship between

decreasing organizational size and unemployment rates,

focusing on absenteeism (Markham & McKee, 1991). Ford

(1980b) found in a study of school districts that the

relationship between size and administrative components

differed depending on whether the study was conducted cross-

sectionally or longitudinally, and that the number of

personnel actually increased during periods of decline.

28

A debate in the organizational theory literature between

researchers (see Sutton & D'Aunno, 1989; McKinley, 1992;

Sutton Sc D'Aunno, 1992) concerns building a model of

workforce reduction. Sutton and D'Aunno (1989) focus on the

effects of decreased workforce size on organizational

structure, including degree of mechanistic structure, and

need for coordination and control.

McKinley (1992) criticizes the Sutton and D'Aunno (1989)

model, by questioning some of their assumptions. McKinley

(1992) believes that there are asymmetries between growth and

decline, both in magnitude and direction of structural

change. However, throughout the debate in the literature,

organizational performance is not addressed.

Corporate Restructuring

This section of the literature review discusses some of

the literature on corporate restructuring and its

relationship to the issue of downsizing. Some researchers

maintain that downsizing and downscoping are a result of

corporate restructuring (Hoskisson, Hitt, & Hill, 1991; Hitt

Sc Keats, 1992). Hoskisson, Hitt and Hill (1991) say that

downsizing and downscoping: (1) reduce vertical controls;

and (2) facilitate the development of strong core values in

the organization.

Hitt and Keats (1992) take an interesting approach and

show a reciprocal interdependence between restructuring and

29

strategic leadership. They define restructuring activities

as mergers and acquisitions, downscoping (reduction in the

number of businesses or products), and downsizing (reducing

operational slack, subtracting administrative layers).

Workforce reduction is seen as a component of both

downscoping and downsizing.

Hitt and Keats (1992) note three human resource

approaches in downsizing and downscoping activities:

decreasing numbers of upper-echelon, highly paid members of

the organization; eliminating middle management layers; and

across-the-board layoffs. Hitt and Keats (1992) point out

that effective restructuring does not necessarily mean

indeterminate workforce reduction; critical organizational

functions must be evaluated in light of long-term goals and

individual skills. They develop the argument that effective

strategic leadership during restructuring must balance short-

term needs with long-term growth and survival.

Other researchers characterize corporate restructuring

as change along the dimensions of assets, capital structure,

or management (Bowman & Singh, 1990, 1993) . Bowman and Singh

(1993) point out that restructuring decisions are a part of

long-term strategic planning. They also discuss the

multidimensionality of restructuring, and the need to

consider the consequences of restructuring on performance.

One type of restructuring, corporate refocusing, was

investigated by Markides (1992). He looked at the

30

relationship between reduction of diversification and

profitability, finding a curvilinear relationship- However,

his study did not include downsizing as a variable.

Markides* (1992) findings are in line with the evidence from

Hoskisson and Hitt (1990), work which shows a curvilinear

relationship between diversification and performance.

Markides (1995) later goes on to further investigate the

links between diversification, restructuring, and

performance. A decrease in diversification (by divestiture,

thereby refocusing on the firm's core business) by

overdiversifled firms was shown to lead to increased

profitability. The study defined restructuring as

refocusing, rather than share repurchasing, consolidation, or

leveraged recapitalization, nor was downsizing considered.

Lewis (1990) includes acquisition or divestiture,

replacement or reassignment of senior managers, reduction in

employee counts, and debt recapitalization as restructuring

activities, and contends that the degree of restructuring is

very important. Most articles and book chapters on corporate

restructuring appear to focus on issues such as valuation,

board of director involvement and corporate governance,

leveraged buyouts, employee stock ownership, executive

succession and compensation, and tax implications (e.g., see

Rock Sc Rock, 1990; Bowman & Singh, 1993) . Downsizing is

considered rarely, with possible limited significance, and

31

sometimes as a part of the aforementioned restructuring

issues.

An exception to the restricted use of downsizing as an

important research concept is the work of Bethel and

Liebeskind (1993). They show that blockholder ownership is a

significant determinant of corporate restructuring, as

operationalized as downsizing (percentage change in firm

sales and firm employees over a period of time). Bethel and

Liebeskind (1993) consider downsizing as a measure of

portfolio restructuring. They frame their study and results

within agency theory. Performance issues, however, were not

addressed.

Singh (1993) uses a trade definition of corporate

restructuring as he found no accepted definition in the

academic literature. The term restructuring is used for

"significant and rapid changes in the firm's assets, capital

structure, or organizational structure" (p. 147). Singh

(1993) reiterates the multifaceted and complex nature of

restructuring. He discusses many aspects of corporate

restructuring, including acquisitions, divestitures,

management buyouts, top management teams and boards, and

mergers; however, downsizing is not included.

Hatfield, Liebeskind, and Opler (1996) explore the

relationship between corporate restructuring and

specialization at the industry level. The study found that

while plant closings and industry entry were important.

32

corporate control transactions such as sell-offs had no

effect on aggregate industry specialization. Although plant

closing were dealt with, downsizing specifically was not.

A study by Chang (1996) examined firm entry and exit

decisions as searching for improved performance, in terms of

diversification and corporate restructuring. Poor

performance was shown to lead to exit, but not entry.

Divestiture (exit) was associated with improved performance,

but when industry profitability was taken into account, the

performance improvements disappeared. Although many

variables were included in the analysis, including firm size

(measured as log of total assets), and line of business size

(measured in terms of sales), downsizing was not considered.

Divestment

This section discusses the relevant literature on

divestment and any links with downsizing. Much of the

literature on divestitures is in the finance field, and very

specifically related to corporate investment and financing

decisions. Pre- and post-divestment organizational

performance was compared by divesting and non-divesting firms

by Montgomery and Thomas (1988) . Weak performers were shown

to be more likely to divest. However, downsizing was not

addressed.

Other work on divestment has focused on the performance

of the divested business -- spin-offs of the corporate parent

33

(see Woo, Willard, & Daellenbach, 1992). The results showed

that for both related and unrelated subsidiaries, post spin­

off performance declined. Again, downsizing was not

included.

Duhaime and Grant (1984) point out that several branches

of the management literature are related to divestment, such

as life cycle theory, end-game strategies, stages of

corporate development, and corporate portfolio theory. Their

work focuses on the effects of various factors hypothesized

to be important influences on the divestment decisions of

large, diversified firms.

Factors influencing corporate divestment decisions

include business unit financial and competitive strength,

business unit interdependency, and corporate financial

strength relative to industry averages (Duhaime & Grant,

1984). General economic conditions and managerial attachment

did not influence corporate divestment decisions. Although

Duhaime and Grant (1984) define corporate divestment as a

decision to dispose a significant portion of assets,

downsizing did not play a role in their study.

Duhaime and Baird (1987) focused on business unit size

effects on divestment decisions. Divestment situations were

characterized as defensive (eliminating a unit with poor

performance) and aggressive (opportunity grabbing, or funding

for potential stars in the portfolio, gathering cash for

bankrolling a new product). Divestment reason was found to

34

be curvilinear; small and large units were divested for

defensive reasons, while medium units were divested for

aggressive reasons (Duhaime & Baird, 1987).

Questions were brought up as to the minimum efficient

size for business units in a corporation's portfolio.

Differences in size of business units was specifically

addressed in the Duhaime and Baird (1987) study, but

downsizing was not.

Wright and Ferris (1997) studied the effect of

divestment on corporate value (in terms of stock returns).

The study was based on agency theory and explored the effect

of public and private political forces on corporate strategy,

The effect of divestment of South African business units on

stock price was found to be negative, indicating that

managers do not always act in the best interests of firm

owners. Downsizing per se was not a part of the study.

Downsizing Practices

Much has been written on how to implement downsizing,

retrenchment, and work-force reduction; however, there are

few theoretical frameworks, nor are there many attempts to

associate various downsizing practices with actual

performance (especially objective measures). This section

offers a brief glimpse of some of the literature on "the

management of downsizing."

35

Hardy (1987) compared two organizations, one that

successfully retrenched, and another that was unsuccessful.

Hardy's (1987) observations were based on attempted plant

and hospital closings, rather than on the more general

concept of downsizing. Through interviews and documentation

analysis. Hardy (1987) suggests how to avoid the costs of

retrenchment by managing several issues, including awareness,

involvement, fair play, disclosure, understanding, and blame.

Another study on the management of workforce reduction

focuses on plant closings (Price & D'Aunno, 1983). Price and

D'Aunno (1983) take the perspective that exchange

relationships are the key to managing the transitions

associated with organizational decline. The links between

the company, transition managers, displaced workers, and the

community are examined through case studies. Prescriptions

are offered for the role of transition manager, including

such concepts as bounded rationality, top management's key

role, the necessity for a long-term view, and the management

of transactions rather than people.

A downsizing program is outlined by Appelbaum, Simpson,

and Shapiro (1987). In their paper, downsizing is defined as

a systematic reduction of a workforce. They develop ten

steps for the human resource department of an organization to

follow for successful downsizing, as well as ten very general

steps for top management, particularly the strategic human

resources executives, to take while planning the downsizing

36

action. Appelbaum, Simpson, and Shapiro (1987) view

downsizing as one possible tool for maintaining or improving

performance, and suggest that monitoring and evaluation are

important for success.

Organizational downsizing may be necessary for many

hospitals in the future (Mullaney, 1989). Mullaney (1989)

offers a case study of a hospital system facing decreasing

demand. Mullaney (19 89) recommends the establishment of a

steering committee, a productivity measurement system, and a

specific reduction-in-force plan.

Robertson (1987) presents two approaches to downsizing,

that have been successful in large organizations. The

product/service approach to downsizing determines which

products and/or services can be streamlined or eliminated,

and then staffing is reduced. The more commonly used

management systems approach is often budget driven, where

middle managers are given new resource allocations to define

the number of workers they can fund (Robertson, 1987) .

Robertson (1987) concludes with suggesting that a vision and

a process for integrating the vision are crucial for

successful downsizing efforts. Performance issues are not

discussed.

An attitudinal study on downsizing in manufacturing

firms was done by McCune, Beatty, and Montagno (1988) , which

sought to identify prescriptions for human resource practice,

Seniority was perceived to be the most used determinant for

37

layoffs, although worker performance was considered in many

non-union firms. Downsizing "programs" typically were

planned and implemented in less than two months.

Prescriptions for human resource managers included using

human resources as a competitive weapon, as well as

integrating human resource strategy with organization

strategy; developing a flexible workforce, adopting employee

stabilization techniques; and ensuring that downsizings are

strategically planned.

McCune, Beatty, and Montagno (1988) also call for future

research, particularly empirical investigation to examine the

validity of the many prescriptive models that abound. They

also suggest that research should be done on the consequences

of downsizing, especially in the areas of survivor reactions,

as well as career transitions.

Cameron, Freeman, and Mishra (1991) present six general

strategies for successful downsizing, gleaned from a field

study of U.S. automobile manufacturers. The "best practice"

strategies appear contradictory in some respects. For

example, successful downsizing was implemented from the top-

down, but was also initiated from the bottom-up; successful

downsizing was short-term and across-the-board, but also

long-term when systemically implemented. Other successful

downsizing practices included outplacement services and

family counseling.

38

The most interesting aspect of the Cameron, Freeman, and

Mishra (1991) study was the apparent contradictory strategies

of effective downsizers. Therein lies much fodder for future

research, especially along broad lines.

A study by De Meuse, Bergmann, and Vanderheiden (1997)

explicates several myths about corporate downsizing. The

first myth is that downsizing is widespread in the U.S., but

in fact, there has been more growth in jobs that loss of

jobs. However, restructuring has become a common corporate

way of life. The second myth is that downsizing improves

profitability, but in reality the savings are overestimated,

and the costs of downsizing are underestimated. The third

myth is that downsizing increases corporate responsiveness,

including communication within the company and with

customers, while there is actually much fear, anger and

resentment which impedes communication and involvement. The

fourth myth is that downsizing focuses on terminated

employees, yet actually those employees left in the company

often have lasting impact on the corporation after downsizing

(e.g., in terms of low morale, increased health costs,

decreased productivity, and increased numbers of lawsuits).

The fifth myth is that downsizing is a last resort, whereas

in reality, it is often implemented without considering

creative alternatives such as pay cuts, unpaid vacations,

shortened work weeks, job sharing, retraining for temporary

other jobs, etc. The sixth myth is that downsizing only

39

happens once, but if downsizing is used for cost cutting, it

will be likely used again. The seventh and final myth

discussed by De Meuse, Bergmann, and Vanderheiden (1997) is

that downsizing is an end in itself, while instead it should

be part of a complete organizational plan that focuses on the

firm after the downsizing occurs. The authors maintain that

downsizing can be implemented successfully if there is

effective strategic planning and communication programs in

place, as well as a supportive corporate culture, where

temporary measures are taken to solve financial problems,

using employee involvement to produce some of the solutions.

Bruton, Keels, and Shook (1996) showed that downsizing

could be beneficial to firms, if the downsizing were a part

of strategic reorientations, by refocusing the firm on its

core businesses. Successful downsizers often reduced asset

size through divestiture. Bruton, Keels, and Shook (1996)

recommend a contingency approach of downsizing integrated

with strategic direction.

An interesting study by Lee (1997) showed differences in

the effect of layoffs on stock price, depending on nation

(U.S. or Japan) . Additionally, the U.S. results showed that

although layoff announcements had a negative effect on stock

price overall (within a five-day period), the type of layoff

mitigated the lowered stock price.

For example, single layoff announcements are worse that

multiple announcements; permanent layoffs are worse than

40

temporary layoffs; the extent of the layoff is important; the

first announcement in the industry is the worst; and

reactive layoffs (those that are the only response to poor

financial performance) are worse than proactive layoffs

(those that are part of a deliberate restructuring strategy).

DeWitt (1998) considers downsizing approaches as

strategic choices for staying in or leaving an industry (or

somewhere in between) . The study characterizes downsizing as

retrenchment, downscaling, or downscoping. Retrenchment

approaches keep the same scope and output of a firm, while

redundant facilities or jobs are eliminated, or managerial

responsibilities are realigned. Downscaling keeps the same

scope but lowers output, by making permanent cuts in human

and physical resources. Downscoping reduces both scope and

output, by product line pruning or market withdrawal, for the

most part.

Firm influences such as capacity and production

investment; industry influences, such as cost advantages

and product differentiation; and strategy influences, such as

domain breadth were shown to be important in choosing

downsizing type, depending on whether the firm was broad of

focused in terms of scope. Dewitt (1998) points out that the

characterization of downsizing is important for understanding

strategic decisions and their impact, and that the downsizing

approach itself is different from the complete strategy of a

declining firm.

41

Gaps in the Reviewed Literature

There are gaps in the organizational science literature

in the exploration and explication of downsizing and its

relationships with other concepts. In the literature on

"size," one of the most important gaps is that changes in

size within organizations are not addressed. One of the

effects, therefore, is that there are no definitions of

downsizing, nor theoretical frameworks on downsizing offered.

Moreover, the operationalization of size is inconsistent,

often contributing to differences in results. Another

drawback in the size literature, at least in relation to

downsizing, is that number of employees is not always

considered. The performance and size relationship is

inadequately studied, and when performance is addressed,

perceptual measures are usually used instead of objective

measures.

In the literature on "decline," there is great

conceptual confusion between downsizing and decline. There

is also blurring of the distinction between decline and

responses to decline. There are few theoretical linkages

between downsizing and decline. Rather, there are many

discussions on how to "handle" decline and the focus is often

on implementation issues. In addition, in the decline

literature, performance relationships are rarely addressed.

In the small amount of literature on "size and decline"

42

together, there is no definitional clarity of concepts;

workforce reduction is often used, even as decline itself.

Implementations issues are addressed, with little emphasis on

theoretical constructs. There is disagreement in the

literature on the possible asymmetry between growth and

decline. Performance issues are, again, not addressed.

The "corporate restructuring" literature has gaps in it

relating to downsizing, as well. Downsizing itself is rarely

considered; downsizing is not usually included in

restructuring definitions. In the one study reviewed. Bethel

and Liebeskind (1993), there was no study of the relationship

between downsizing and performance. Overall, the

consequences of restructuring on performance have been

neglected.

In the literature on "divestment," downsizing is not

addressed. Divestment could be part of an overall downsizing

strategy, but it is not seen that way in the literature on

divestment. Downsizing and divestment appear to be separate

and distinct concepts.

There is a body of literature on "downsizing practices."

Most is at the individual and group level, rather than the

organizational level. Theoretical frameworks are lacking in

the downsizing practices literature. At the same time, there

is little empirical support offered. Another problem in the

downsizing practices literature is that there are no explicit

conceptualizations of downsizing; often, downsizing is

43

discussed in very general terms. The relationship between

downsizing practices and performance has scarcely been

considered. Overall, there is scant scholarly and systematic

research in the area of downsizing practices, and downsizing

in general.

44

CHAPTER III

RATIONALE

The Point at the End of the Cornucopia

The previous literature review may be thought of as a

vast cornucopia of ideas and information related to the

concept of downsizing. There are many yet unresolved

questions about downsizing, including how to study this

phenomenon, and whether (or under what conditions) downsizing

has a positive or negative effect on organizational

profitability. There has been very little systematic and

scholarly research on downsizing. In the words of Cameron,

Freeman, and Mishra (1993),

...it is because of the underdeveloped downsizing theory that investigators should first adopt a theory-building approach as opposed to a theory-testing approach. That is, because no current theories exist regarding organizational downsizing or its association with successful performance, an important first step in research is to identify patterns, relationships, and dynamics rather than to set forth a theory for testing, (p. 28, italics added)

Cameron, Freeman, and Mishra (1993) have set forth

several research questions for current downsizing research,

one of which is the basis for this study, namely: "what is

the impact of downsizing on the organization?" Cameron,

Freeman, and Mishra (1993) state

... almost no empirical studies have empirically investigated the effectiveness of (these) prescriptions. The question of whether

45

organizational downsizing inhibits or enhances organizational performance has largely remained unaddressed, as has the more precise question of which particular downsizing processes are helpful and which are hurtful, (p. 30)

The study attempts to answer the question of whether

organizational downsizing inhibits or enhances organizational

performance.

In the study, organizational downsizing is defined and

characterized as an intentional set of activities designed to

improve organizational performance, and involving reductions

in personnel (Cameron, Freeman, & Mishra, 1993). Downsizing

is considered conceptually distinct from organizational

decline (Cameron, Freeman, & Mishra, 1991, 1993; DeWitt,

1993; Kozlowski, Chao, Smith, & Hedlund, 1993; McKinley,

1993) . According to Cameron, Freeman, and Mishra (1993),

downsizing is strategic in nature, and its purpose is to

improve organizational performance. In the study, the

performance effects of downsizing are studied using a multi-

year, multi-organizational data base. The study includes an

empirical investigation of the performance implications of

downsizing, based on a tentative model. The study does not

test hypotheses, as there is not enough testable theory on

organizational downsizing at this point in time. Instead,

the study contributes to the field by providing systematic

research results for future theory building.

46

DeWitt (1993) suggests that research on downsizing

should be longitudinal, and across- and within-organizations.

The study is longitudinal, as organizations are investigated

over a period of time and the secondary databases provide a

view across organizations.

The study of organizational downsizing has not

explicitly been characterized in terms of level of analysis.

On one hand, most of the popular press information seems to

have been concerned with the corporation as a whole, or with

displaced workers (e.g., see Byrne, 1994). On the other

hand, much of the anecdotal evidence on how to downsize

appears to be at the SBU or site level (e.g., see Hardy,

1987) . A very interesting study by Baily, Bartelsman, and

Haltiwanger (1994) investigated the relationship between

downsizing and productivity, comparing results at the

aggregate industry level versus the disaggregated plant

level. The aggregrate data across the U.S. manufacturing

sector showed falling employment and rising productivity.

However, when the plant level data were examined, there were

both successful and unsuccessful (in terms of increased

productivity) plants that downsized. The Baily, Bartelsman,

and Haltiwanger (1994) study is the only research reviewed

that controlled for industry and other factors (e.g.,

industry, size, region, wages, and ownership type) when

looking at organizational effectiveness. However,

profitability was not addressed. The Baily, Bartelsman, and

47

Haltiwanger (1994) study brings up interesting questions

about downsizing research, including level of analysis

issues.

Based on the literature reviewed, and Rumelt's (1991)

study of industry, corporate, and SBU effects on

organizational profitability, the study investigates the

impact of downsizing on both corporate and SBU profitability,

while controlling for factors that have been shown to affect

profitability. Industry conditions and corporate strategy,

as well as SBU strategy, are included as control measures.

Research Strategy

The research strategy is to use large data bases

(COMPUSTAT Annual Industrial Files and the Industry Segment

Database) to both identify and measure the impact of

downsizing. There are multiple measures of benefits

(performance measures) at a time period starting at a point

from zero (the year of the downsizing) to five years after

the downsizing event. Aside from the direct relationship

between downsizing and corporate performance, there are two

classes of control variables--market conditions and corporate

strategy. The primary analytical tool for building the

theoretical model is the use of pooled cross-section

regression analysis, and also time-series regression

analysis.

48

CHAPTER IV

METHODOLOGY

This chapter presents the data analyzed in this study,

the definition and operationalization of the variables, the

analytical method to be used (multiple regression analysis),

the formulation of the empirical model, and strengths and

limitations of this study.

Data

This section discusses the data used in this study. The

data used in this study is taken from the COMPUSTAT^ II

Business Information Industry Segment and the COMPUSTAT^

Annual Industrial Files. The COMPUSTAT^ Annual Industrial

Files provide financial information at the consolidated

corporate level. The data on the COMPUSTAT^ II Business

Information Industry Segment File were disclosed due to

requirements of the Financial Accounting Standards Board

(FASB) Issued Statement of Financial Accounting Standards

Number 14, Financial Reporting for Segments of a Business

Enterprise (SFAS No. 14). The data are compiled from the

corporations' annual reports and 10-K reports to the

Securities and Exchange Commission (SEC). Corporations are

required to provide information on their principal lines of

business (industry segments).

49

The FASB defines a segment as "a component of an

enterprise engaged in providing a product or service or a

group of related products and services that are sold

primarily to unaffiliated customers (i.e., customers outside

the enterprise) for a profit" (Davis & Duhaime, 1992, p.

512). The unit of observation is the line of business.

The COMPUSTAT II Industry Segment file contains multiple

years of information for each company, and there may be up to

ten industry segments reported per year per company. There

are approximately 7 000 companies in the database.

Each corporation is required to report information on

segments that account for at least ten percent of

consolidated sales, operating profits, or assets. Each line

of business is required to report the following information:

net sales, operating profit or loss, depreciation, capital

expenditures, identifiable assets, equity in earnings of

unconsolidated subsidiaries, investments at equity, number of

employees, order backlog, and research and development

expenditures (company and customer sponsored).

Industry and corporate identification is possible for

all business units, as a 4-digit SIC code and a corporation

code is assigned to each industry segment. SIC codes of

0100-1999 (raw materials), 2000-3999 (manufacturing), and

4000-9999 (service) are represented in the database. This

study uses data from corporations that operate primarily in

the manufacturing sector (SIC codes 2000-3999).

50

An important advantage of the COMPUSTAT II Industry

Segment database is its disaggregation of corporate data to

study multibusiness corporations at the unit of business

level. This database has been shown to be appropriate for

use on research of strategic diversity, industry trends, and

vertical integration (Davis & Duhaime, 1992). The use of the

COMPUSTAT II Industry Segment database in conjunction with

the COMPUSTAT Annual Industrial Files will allow the

calculation of the weighted averages of SBU level data for

the market and corporate strategy control variables.

Another advantage of the COMPUSTAT II Industry Segment

database is the industry comprehensiveness due to the SEC and

FASB requirements, providing a representative sample of major

public corporations of most economic sectors.

Performance Measures

Corporate economic performance has been measured by a

variety of approaches, including accounting-based measures,

such as return on assets (ROA), return on equity (ROE), and

return on sales (ROS); market-based measures, such as

Jensen's alpha; and hybrid measures, such as Tobin's q (a

ratio of market value to replacement costs). In this study,

corporate economic performance is measured in two ways. One

is the accounting-based profitability measure, ROA, due to

its year to year stability in the COMPUSTAT data (Hill, Hitt,

Sc Hoskisson, 1992) . The second is an approximation of

51

Tobin's q, market to book equity. The ROA and Tobin's q

approximation performance variables are measured zero to five

years after the downsizing action occurred.

Return on Assets

Return on assets is defined as operating income divided

by total assets. However, there has been some criticism of

the validity accounting-based measures (Lubatkin & Shrieves,

1986). Therefore, corporate economic performance will also

be measured using the hybrid Tobin's q proxy.

Tobin's q

Tobin's q combines security market data and accounting

data to provide a long run measure of corporate economic

performance (Lindenberg & Ross, 1981; Montgomery &

Wernerfelt, 1991). The market value of debt is difficult to

obtain, so a proxy of Tobin's q (market to book assets) is

often used in strategy research (Bowman, 1980). Amit and

Livnat (1988) used another approximation of Tobin's q (market

to book equity) that is generally constructable from

COMPUSTAT files. The Tobin's q approximation (market to book

equity) used for this study is defined as the share price at

year's end multiplied by the number shares outstanding, then

divided by shareholder's equity.

Business unit economic performance is measured as

profitability (ROA or operating income of the SBU divided by

52

total assets of the SBU). There is no market data for

strategic business units, since they have no stock nor

shareholders themselves (market data are aggregated at the

corporate level).

The dependent variables are lagged for periods of time

after the downsizing action occurred.

Downsizing Measures

The operationalization of the downsizing measure is

discussed in this section. In this study, for corporations

only (not for SBU's), it is not possible to distinguish

between a change in the number of employees due to

divestiture as opposed to the direct elimination of

employees. In other words, the situation where the number of

employees drops due to the selling off of a strategic

business unit, for example, is not differentiated from the

situation where the n\m±>er of employees decreases

dramatically, but without a sell-off.

In this study, downsizing is operationalized as

percentage change in the number of employees from one year to

another. As several years are included in the model for a

given corporation, the regression parameter (and t-value) for

the percentage change in the number of employees will provide

insight into the change in the various performance criteria

listed above.

53

Control Measures

Since the antecedents of organization economic

performance are so varied and comprehensive, there are three

groups of independent variables for each set of regression

equations (with dependent variables of corporate ROA,

corporate Tobin's q approximation, and business unit ROA).

The independent variables used include industry structure

variables, corporate strategy variables, and business unit

strategy variables, since it has been shown that industry,

corporate, and SBU effects all have an impact on ROA (Rumelt,

1991).

Industry Conditions

Industry profitability, industry growth, and industry

concentration represent industry structure (Christensen &

Montgomery, 1981) in this study.

Industry Profitability. Industry profitability is

defined as industry operating income divided by total

industry assets.

Industry Growth. Industry growth is defined as total

industry sales in yeart minus total industry sales in yeart-i,

divided by total industry sales in yeart-i.

Industry Concentration. Industry concentration is

defined as the sum of the sales of the four largest firms in

the industry divided by total industry sales.

54

Corporate Strategy

Corporate strategy is measured as degree of

diversification.

Degree of Diversification. Degree of diversification is

measured as an unweighted, SIC-based, continuous

diversification measure based on narrow and broad product

counts (Lubatkin, Merchant, & Srinivasan, 1993; Varadarajan &

Ramanujam, 1987) .

Business Level Strategy

Ramanujam and Venkatraman (1984) have identified several

variables that measure business-level strategy. In this

study, business-level strategy is measured as advertising

intensity (for a subsample for which these data are

available) , capital intensity, R&D intensity (for a subsample

for which these data are available), and SBU number of

employees, including the downsizing measure.

Advertising Intensity. Advertising intensity is

considered to be representative of differentiation strategies

(Porter, 1980, 1985). Advertising intensity is usually

measured by advertising expenditures divided by total SBU

sales; however, in this study, those data are not available

at the business level. Instead, a corporate proxy can be

calculated using the consolidated corporate advertising

expenditures divided by corporate consolidated sales.

55

Unfortunately, advertising expenditures are not always

reported, so the sample size is reduced when this variable is

included. Therefore in this study, the regression equations

are run both without the advertising intensity variable (for

a large sample size), and with the advertising intensity

variable (a limited sample).

Capital Intensity- Capital intensity represents

production and investment strategy and cost position.

Capital intensity is measured as total SBU assets divided by

total SBU sales.

R&D Intensity. Research and development (R&D) intensity

is also considered to be representative of differentiation

strategies (Porter, 1980, 1985). R&D intensity is measured

as total SBU R&D expenditures (customer and company

sponsored) divided by total SBU sales. The disadvantage to

using R&D intensity is that it reduces the sample size

drastically, as many companies do not report these

expenditures (Hoskisson & Johnson, 1992; Hill, Hitt, &

Hoskisson, 1992). Therefore in this study, the regression

equations are run both without the R&D intensity variable

(for a large sample size), and with the R&D intensity

variable (a limited sample).

56

Method of Analysis

The statistical analyses performed in this study are

based on multiple regression techniques (Neter, Wasserman, &

Kutner, 1989). Since the data in this study cover several

years, and they are pooled, parts of this study can be called

a pooled, time-series, cross-sectional analysis.

The time-series cross-sectional regression analysis is

done with the SAS/ETS (econometric package) VAX software.

The SAS procedure is TSCSREG, which is specifically written

for panel data sets made up of observations in a time series

(7 years for this study), over a particular number of cross-

sectional units (corporations or SBU's in this study). The

econometric model upon which the TSCSREG procedure is based

is as follows:

p

y i t = E XitkPk + JLAit i = l / - - w N; t = l , . . . , T k = l

where N is the number of cross sections, T is the length of

the time series for each cross section, and p is the number

of exogenous variables. The error structure used is the

variance components (or error components) model,

ILiit = t i + et + Cit

with the Fuller-Battese method used to estimate this model.

The fitting-of-constants method is used to estimate the

variance components, and generalized least squares (GLS) are

used to estimate the regression parameters.

57

The TSCSREG procedure requires that all cross sections

(in this study, corporations and SBU's) have the same number

of time periods (contiguous time sequence; 7 years in this

study), and that there is no missing data for any variable.

These requirements reduced the data sets for this study,

depending on which independent variables are in each model,

and on which corporations and SBU's were in the data for all

seven years (see Table 5.6 for the number of corporations and

SBU's). The TSCS regressions were run for models that (1)

included all the independent variables discussed earlier, (2)

included all except R&D intensity, (3) included all except

advertising intensity, and (4) included all except R&D

intensity and advertising intensity.

The other analyses in this study (e.g., datasets with

downsizers only) were done using the SAS regression

procedure, which is not time series.

Kmpiriral Model

The corporate model is specified as:

Corporate Economic Performance = [Industry Structure] +

[Corporate Strategy] +

[Downsizing] +

[SBU Strategy].

More specifically,

Y = bo + biXi + baXs + bsXs + hA + 5X5 + beXg + b7X7 + bgXe + e

58

where,

Y = corporate economic performance (ROA; Tobin's q

approximation, market

to book equity)

Xi = industry concentration

X2 = industry growth

X3 = industry profitability

X4 = level of diversification

X5 = corporate downsizing Xe = advertising intensity (for a subsample)

X7 = capital intensity

XQ = R&D intensity (for a small subsample)

e= error term.

The strategic business unit model is specified as:

SBU Economic Performance = [Industry Structure] +

[Downsizing] +

[SBU Strategy].

More specifically,

YsBu = bo + biXi + b2X2 + 103X2 + b4X4 + bsXB + bgXe + hjXj + e

where,

YsBu = SBU economic performance (ROA)

Xi = industry concentration of the industry in which the

SBU operates X2 = industry growth of the industry in which the SBU

operates X3 = industry profitability of the industry in which the

SBU operates Xg = SBU downsizing

X7 = advertising intensity of the SBU (for a subsample)

Xg = capital intensity of the SBU

X9 = R&D intensity of the SBU (for a small subsample)

e= error term.

59

CHAPTER V

DATA ANALYSIS

This chapter presents the results of the descriptive and

regression analyses in this study. These results are

presented in four sections. The first section presents the

descriptive statistics for the data used in the regression

analyses. The second section shows the time series cross-

sectional (TSCS) regression results, at the SBU and corporate

levels. Both concurrent and lagged equations are presented.

The third section shows the regression models where the base

year ROA and/or TOBQ are used as independent variables with

downsizing for a series of lagged dependent measures. The

fourth section presents the pooled cross-sectional regression

results (not time series) for all models, including SBU and

corporate equations, with concurrent and lagged dependent

measures.

Dpsrriptive Statistics

The means and standard deviations for the data sets used

for the regression analyses are shown in Tables 5.1 and 5.2.

Descriptive statistics are given for entire data sets (from

which the time series cross-sectional data sets are

constructed) and data sets of those SBU's and corporations

that have downsized. The following statistics are for the

SBU level data (Table 5.1). First presented are those for

60

the entire dataset. The mean SBU profitability (ROA) is

0.0245. For industry structure variables, the mean for

industry concentration (INDCN) is 0.7730, for industry growth

(INDGR) is 1.7165, and for industry profitability (INDPR) is

0.0951. The mean for downsizing (DS) is 0.0814. For

business level strategy variables, the mean for capital

intensity (CAPINT) is 5.7350, the mean for advertising

intensity (ADVINT) is 0.0354, and the mean for research and

development intensity (RDINT) is 0.3799.

The second part of Table 5-1 presents the statistics for

the downsizers only dataset. The mean SBU profitability

(ROA) is -0.0419. For industry structure variables, the mean

for industry concentration (INDCN) is 0.7576, for industry

growth (INDGR) is 0.4032, and for industry profitability

(INDPR) is 0.0633. The mean for downsizing (DS) is -0.0557.

For business level strategy variables, the mean for capital

intensity (CAPINT) is 4.7757, the mean for advertising

intensity (ADVINT) is 0.0358, and the mean for research and

development intensity (RDINT) is 0.2201.

The next set of statistics are for the corporate level

data (Table 5.2) . The first part of the table is for the

entire dataset. The mean corporate profitability (ROA) is

0.0856. The mean corporate Tobin's Q (TOBQ) is 2.4366. For

industry structure variables, the mean for industry

concentration (INDCN) is 0.8718, for industry growth (INDGR)

is 0.1482, and for industry profitability (INDPR) is 0.1040.

61

The mean for downsizing (DS) is 0.0679. For business level

strategy variables, the mean for capital intensity (CAPINT)

is 1.3320, the mean for advertising intensity (ADVINT) is

0.0383, and the mean for research and development intensity

(RDINT) is 0.2223. For the corporate strategy variable

diversification (DIVER), the mean is 7.9503.

The second part of the Table 5.2 is for the downsizers

only dataset. The mean corporate profitability (ROA) is

0.0500. The mean corporate Tobin's Q (TOBQ) is 1.6463. For

industry structure variables, the mean for industry

concentration (INDCN) is 0.8773, for industry growth (INDGR)

is 0.1307, and for industry profitability (INDPR) is 0.0991.

The mean for downsizing (DS) is -0.0374. For business level

strategy variables, the mean for capital intensity (CAPINT)

is 1.1872, the mean for advertising intensity (ADVINT) is

0.0396, and the mean for research and development intensity

(RDINT) is 0.1297. For the corporate strategy variable

diversification (DIVER), the mean is 7.9876.

Correlation tables are shown in Tables 5.3 and 5.4.

Table 5.5 shows the number of SBU's and corporations per

year), and Table 5.6 shows how many SBU's and corporations

have how many years of data.

62

Time Series Cross-Sectional (TSCS) Regression Analysis

The time series cross-sectional (TSCS) regression were

run at the SBU level (ROA as the dependent variable) , and at

the corporate level (ROA and TOBQ as dependent variables).

Four different models for each regression equation (SBU-ROA,

CORP-ROA, and CORP-TOBQ) were run to increase the sample

size; i.e., one model with all independent variables

included, one with ADVINT excluded, one with RDINT excluded,

and one with both ADVINT and RDINT excluded.

SBU Level Results

Tables 5.7-5.14 show the regression parameter estimates

(if significant at the .05 level or better, in most cases)

and the p-values for the SBU level data, for the concurrent

and lagged dependent measures.

All Variables (Tables 5.7 and 5.8). The models

containing all the variables (ROA as dependent variable, and

DS, ADVINT, CAPINT, RDINT, INDCN, INDGR, INDPR as independent

variables) for the SBU level data had very few observations

(4 to 8 SBU's, over 3 to 6 years depending on the lag period,

yielding only 24 to 30 observations). Therefore, caution

must be observed in reaching conclusions based on these data.

It can be seen that the downsizing variable is significant

(p-value .06 for the concurrent equation and .05 for the Lag

1 equation) and negative (-0.16 for no lag and -0.14 for Lag

63

1) for the concurrent and the Lag 1 equations only. None of

the other independent variables are significant. The

downsizing variable is insignificant in the Lag 2 and Lag 3

equations, suggesting that the slight but negative impact of

downsizing on SBU performance (ROA) can be seen only in the

year in which the downsizing occurs, and the year after the

downsizing action. ADVINT, CAPINT, and INDCN have a

significant and positive impact on SBU ROA in the second year

after downsizing occurs. CAPINT remains significant, and

INDGR is significant (and negative) in the third year after

downsizing occurs.

Advertising Intensity Excluded (Tables 5.9 and 5.10).

The number of SBU's in the models containing RDINT, but not

ADVINT, range from 66 to 97 SBU's (over 3 to 6 years and from

291 to 415 observations). Downsizing has a significant and

negative impact on SBU ROA in the year of the downsizing (p-

value .0001, parameter estimate -0.20) and in the next year

after the downsizing action (p-value .0004, parameter

estimate -0.11), but not in the second year after downsizing.

However, the DS significance returns (p-value .007, parameter

estimate -0.09) in the third year after downsizing occurs.

CAPINT has a significant and small positive impact on SBU ROA

(concurrent equation only), while RDINT has a significant and

diminishing negative impact (no lag. Lag 1, and Lag 2

64

equations). INDPR becomes significant (positive) in the Lag

2 and Lag 3 equations.

R and P Intensitv Excluded (Tables 5.11 and ^.^7) The

number of SBU's in the models containing ADVINT, but not

RDINT, range from 115 to 129 SBU's (over 3 to 6 years and

from 387 to 690 observations). Downsizing has a significant

(p-value .01) and small negative (parameter estimate -0.02)

impact on SBU ROA in the year of the downsizing action only.

The DS variable is insignificant for the Lag 1 and Lag 2

equations, and then is significant (p-value .01) and positive

(parameter estimate 0.04) in the Lag 3 equation, suggesting

that the negative impact of downsizing on SBU profitability

washes out over two years, and then goes to a small positive

impact. ADVINT is significant and negative in the concurrent

equation only. CAPINT is significant and negative in the no

lag. Lag l and Lag 3 equations. INDPR is significant and

positive in all of the equations (no lag. Lag 1, Lag 2 , and

Lag 3).

ADVINT and RDINT Excluded (Tables 5,13 and 5,14), The

number of SBU's in the models excluding both ADVINT and

RDINT, is 993 SBU'S (over 3 to 6 years and from 2979 to 5958

observations). Downsizing has a significant and small

negative impact on SBU ROA in the year of the downsizing (p-

value .0001, parameter estimate -.05) and the next year (p-

value .03, parameter estimate -.01) only. DS is

65

insignificant in the Lag 2 and Lag 3 equations, showing a

diminishing negative impact of downsizing on SBU performance.

INDPR is significant (p-value .0001) and positive in all

equations (no lag. Lag 1, Lag 2, and Lag 3). CAPINT is

significant and negative (although very small) in the lagged

equations. INDCN is significant and positive in the Lag 2

equation only.

Corporate Level Results

Tables 5.15-5.26 show the regression parameter estimates

(if significant at the .05 level or better,) and the p-values

for the corporate (CORP) level regressions, for the

concurrent and lagged dependent measures. Corporate

performance measures used are ROA and Tobin's Q (TOBQ).

All Variables - ROA (Tables 5.15 and 5.16). The models

containing all the variables (ROA as dependent variable, and

DS, DIVER, ADVINT, CAPINT, RDINT, INDCN, INDGR, INDPR as

independent variables) range from 156 to 193 corporations

(over 3 to 6 years and from 579 to 936 observations)..

Downsizing has a significant and negative impact on CORP ROA

in the year downsizing occurs, and for the following two

years (no lag p-value .0001, parameter estimate -0.06; Lag 1

p-value .0001, parameter estimate -0.06; Lag 2 p-value .02,

parameter estimate -0.03). DS is no longer significant in

the third year. As in the SBU equations, downsizing has a

66

negative effect on profitability. However, the effect

continues to be seen for 2 years after downsizing for

corporations, versus only one year after downsizing for

SBU'S. RDINT, CAPINT, and INDCN are all significant and

negative for all concurrent and lagged regressions. INDPR is

significant and positive for all concurrent and lagged

equations. Diversification (DIVER) does not have a

significant impact on CORP ROA in any of the equations, when

all variables are present.

Advertising Int. Excluded - ROA (Tables 5.17 and 5.18).

The number of corporations in the models containing RDINT,

but not ADVINT, range from 411 to 455 corporations (over 3 to

6 years and from 1365 to 2466 observations) . Downsizing has

a significant and negative impact on CORP ROA in the

concurrent (p-value .0001, parameter estimate -0.03) and the

Lag 1 (p-value .001, parameter estimate -0.02) equations.

The downsizing effect is seen only in the year of, and the

year after it occurs. RDINT is significant and negative in

the no lag equation, but significant and positive in the Lag

1 and Lag 2 equations. CAPINT is significant and negative in

the no lag and Lag 2 equations. INDCN is significant and

negative in only the Lag 1 and Lag 2 equations. INDPR is

significant and positive in all concurrent and lagged

regressions. DIVER is significant and positive for the no

lag. Lag l, and Lag 3 equations (DIVER is significant and

67

negative in Lag 2); however, the parameter estimates are very

small in all cases (around .001).

R and D Intensity Excluded - ROA (Tables 5.19 and 5.20).

The number of corporations in the models containing ADVINT,

but not RDINT, range from 236 to 295 corporations (over 3 to

6 years and from 885 to 1416 observations) . Downsizing is

significant (p-value .0001) and negative (parameter estimate

-.02) for the concurrent equation, and significant (p-value

-03) and positive (parameter estimate .01) in the Lag 3

equation. DS is not significant in either the Lag 1 or Lag 2

equations. Again, it appears that downsizing has a

significant, although small, negative effect on corporate ROA

lasting a short while only, later going to a very small

positive effect. ADVINT is significant and negative in the

concurrent equation only. CAPINT is significant and negative

in all concurrent and lagged equations. INDCN is significant

and negative in the concurrent and the Lag 2 equations.

INDPR is significant and positive in all concurrent and

lagged equations. DIVER is significant and positive (small

values of around .002) for the concurrent. Lag 2, and Lag 3

equations. INDGR becomes significant and positive in the Lag

2 and Lag 3 equations.

ADVTNT and pnTNT ExcJiidPd - ROA (Tables 5.21 and 5,22).

The number of corporations in the models excluding both

ADVINT and RDINT, range from 664 to 7 04 corporations (over 3

68

to 6 years and from 2112 to 3984 observations). Downsizing

has a significant and small negative impact on CORP ROA in

the concurrent year (p-value .002, parameter estimate -.009)

and the year following the downsizing (Lag l, p-value .04,

parameter estimate -.006) only. The effect of downsizing on

corporate profitability again seems to wash out after a

relatively short time. DIVER is significant (p-values .001

to .003) and positive (parameter estimates .002) for all

concurrent and lagged regressions. INDPR is also significant

and positive for all concurrent and lagged equations. INDGR

becomes significant and positive (very small values) in the

Lag 2 and Lag 3 equations. CAPINT is not significant in the

no lag and Lag 2 equations, but significant and positive for

Lag 1 and significant and negative for Lag 3.

All variables - TOBQ (Tables 5.15 and 5.23). The models

containing all the variables (TOBQ as dependent variable, and

DS, DIVER, ADVINT, CAPINT, RDINT, INDCN, INDGR, INDPR as

independent variables) range from 156 to 192 corporations

(over 3 to 6 years and from 57 6 to 936 observations).

Downsizing has a significant (p-value .001) and negative

impact (parameter estimate -1.04) on CORP TOBQ in the year

downsizing occurs, is insignificant for the following two

years (Lag 1 and Lag 2), and is significant (p-value .04 and

positive (parameter estimate 2.02) in the third year (Lag 3).

RDINT is significant and positive for only the Lag 2

69

equation. CAPINT is significant and negative for the Lag 2

and Lag 3 equations. INDPR is significant and positive for

the concurrent and Lag l and Lag 3 equations.

Diversification (DIVER) does not have a significant impact on

CORP TOBQ in any of the equations, when all variables are

present.

Advertising Int. Excluded - TOBO (Tables 5.17 and 5.24).

The number of corporations in the models containing RDINT,

but not ADVINT, range from 411 to 454 corporations (over 3 to

6 years and from 1362 to 2466 observations) . Downsizing has

a significant and negative impact on CORP TOBQ in the

concurrent (p-value .0001, parameter estimate -1.29) equation

only. The downsizing effect is seen only in the year of the

downsizing action. RDINT is not significant for any of the

equations. CAPINT is significant and positive in the no lag

and Lag 1 equations. INDGR is significant and positive in

only the Lag 1 equation. INDPR is significant and positive

(values from 12.65 to 6.13) in all concurrent and lagged

regressions. DIVER is significant and negative (values

around -.03) for the no lag and Lag 2 equations.

p nri n Tnt. Fvrindpd - TOBO (Tables 5.19 and 5.25).

The number of corporations in the models containing ADVINT,

but not RDINT, range from 236 to 291 corporations (over 3 to

6 years and from 87 3 to 1416 observations). Downsizing is

not significant for any of the concurrent or lagged

70

regressions. ADVINT is significant and positive (values

around 9) in the Lag 2 and Lag 3 equations only. CAPINT is

insignificant for all equations. INDPR is significant and

positive (values from 13 to 8) in all concurrent and lagged

equations. DIVER is insignificant for all equations.

ADVINT and RDINT Excluded - TOBQ (Tables 5-21 and 5.26).

The number of corporations in the models excluding both

ADVINT and RDINT, range from 664 to 7 00 corporations (over 3

to 6 years and from 2100 to 3984 observations). Downsizing

is not significant for any of the concurrent or lagged

equations, nor is DIVER. INDCN is significant and negative

(values from -3.3 to -5-4) for all concurrent and lagged

equations. INDPR is significant and positive for the

concurrent and Lag l equations.

BasP Year ROA or TOBQ as Independent Variables

The following results were obtained by "subsuming" all

the independent variables for each regression equation into

the base year (year of the downsizing) dependent variable.

The base year ROA or TOBQ was then used along with the

downsizing variable as another independent variable. The

dependent variables for this analysis were the lagged ROA and

TOBQ (for one through five years after the downsizing

occurred). Tables 5.27-5.29 show the results of the

following regressions.

71

SBU Level Results (ROALAGS = DS + ROA) (Table 5-27)

Five lag periods were tested; the base year ROA was

significant at the .0001 level for all of the lag periods.

However, downsizing was significant (p-value .03) and

negative (parameter estimate -.086) for only the Lag 3

equation. Downsizing had a small negative effect on SBU ROA

that appears three years after the downsizing action, and

then extinguishes thereafter.

Corporate Level Results (ROALAGS = DS + ROA) (Table 5.28)

Of the five lag periods tested, the base year ROA was

significant at the .0001 level for the first four of the lag

periods. However, downsizing was not significant for any of

the equations. This set of equations show no effect of

downsizing on CORP ROA for one through five years after the

downsizing occurs.

Corporate Level Results (TPOBQLAGS = DS + TOBO) (Table 5.29)

Again, five lag periods were tested; the base year TOBQ

was significant for the Lag l and Lag 5 equations.

Downsizing was significant (p-value .02) and positive for

only the Lag 4 (parameter estimate 1.9) and Lag 5 (parameter

72

estimate 10.47) regressions. Downsizing had a positive

effect on CORP TOBQ that only appears four and five years

after the downsizing action.

Pooled Cross-Sectional Regression Results

In this set of analyses, the datasets used contained

only those SBU's or corporations which had downsized at least

by 10 percent in a particular year, including the data for up

to five years after the downsizing occurred. The lagged

equations use the variables DSP1-DSP5, showing downsizing

one to five years before the dependent measures. Note that

the DS variable is retained in all the lagged equations to

control for the current year's change in number of employees.

Concurrent and lagged (for up to five years) equations were

run for SBU ROA and corporate ROA/Tobin's Q. Three models

were used for each dependent variable (to increase sample

size), one with all variables included, one with R&D

intensity (RDINT) excluded, and another with both R&D

intensity and advertising intensity (ADVINT) excluded.

SBU Level Results

Tables 5.30-5.35 show the regression parameter estimates

(if significant at the .05 level or better) and the p-values

for the SBU level data, for the concurrent and lagged

equations.

73

All Variables (Tables 5.30 and 5.31). As with the time

series data, the models containing all the variables (ROA as

dependent variable, and DS (and DSP1-DSP5 representing

downsizing in the previous years), ADVINT, CAPINT, RDINT,

INDCN, INDGR, INDPR as independent variables) for the SBU

level data often had small numbers of observations (24 to

4171 observations, depending on the lag period). In the SBU

set of equations, the Lag 1 through 5 equations had too few

observations (0 to 7) to run. The following values are for

the concurrent equation (only 24 observations, so care must

be taken in evaluating these results). The downsizing

variable is insignificant (p-value .39). ADVINT is

significant (p-value .01) and negative (parameter estimate

-7.09). CAPINT, RDINT, INDCN, INDGR, and INDPR are

insignificant.

R and D Intensity Excluded (Tables 5.32 and 5.33). The

niomber of observations in the models containing ADVINT, but

not RDINT, range from 22 (Lag 5) to 447 (concurrent or no

lag). Downsizing is insignificant for SBU ROA in the year of

the downsizing action (DS), and for all the years (Lags 1

through 5, DSPl - DSP5) after downsizing occurred. The

control variable DS for the current year is significant and

negative for the Lag 2 equation only. CAPINT is significant

in the concurrent year only (p-value .01, parameter estimate

-.009). ADVINT, INDCN, and INDGR are insignificant for the

74

concurrent and all lagged equations. INDPR is significant

and positive in the no lag (p-value .0001, parameter estimate

.59) and Lag 2 (p-value .005, parameter estimate .7 9)

equations.

RDINT and ADVINT Excluded (Tables 5.34 and 5.35). The

number of observations in the models excluding both RDINT and

ADVINT, is from 196 (lag 5) to 4171 (no lag). Downsizing has

a significant and negative impact on SBU ROA in the year of

the downsizing (p-value .0001, parameter estimate -.08), the

next year (p-value .0001, parameter estimate -.30), Lag 2 (p-

value .0001, parameter estimate -.24), and Lag 3 (p-value

.01, parameter estimate -.18). DSP is insignificant in the

Lag 4 and Lag 5 equations. In this model without RDINT and

ADVINT, a negative effect of downsizing on SBU ROA is

sustained (but diminishing in magnitude) over three years

after the downsizing occurs, and then washes out. The

control variable DS for the current year is significant and

negative for all lagged equations. CAPINT is significant (p-

values from .0001 to .02) and negative (parameter estimates

around -.00005 to -.02) in the concurrent and Lag 1, Lag 3,

and Lag 5 equations. INDCN is significant and positive in

the Lag 5 equation. INDGR is insignificant in all equations.

INDPR is significant (p-value .0001 to .006) and positive in

all except the Lag 4 and Lag 5 equations (no lag. Lag l. Lag

2, Lag 3).

75

Corporate Level Results

Tables 5.36-5.47 show the regression parameter estimates

(if significant at the .05 level or better,) and the p-values

for the corporate (CORP) level regressions, for the

concurrent and lagged dependent measures. Corporate

performance measures used are ROA and Tobin's Q (TOBQ) .

All Variables--ROA (Tables 5.36 and 5.37). The models

containing all the variables (ROA as dependent variable, and

DS (and DSP1-DSP5 representing downsizing in the previous

years), DIVER, ADVINT, CAPINT, RDINT, INDCN, INDGR, INDPR as

independent variables) range from 27 (Lag 5) to 425 (no lag)

observations. Downsizing has a significant and negative (p-

value .003, parameter estimate -.09) impact on CORP ROA in

the year downsizing occurs only. Previous downsizing (DSPl -

DSP5) is not significant in Lags 1-5. The control variable

DS for the current year is also insignificant for all lagged

equations. RDINT is significant (p values .0001 to .001) and

negative (parameter estimates from -1.28 to -.89) for the

concurrent. Lag 1 and Lag 3 regressions. CAPINT is

significant and positive in only the no lag equation. INDPR

is significant and positive for only the concurrent and Lag 5

equations. ADVINT, INDCN, and INDGR are not significant in

any concurrent or lagged equations. Diversification (DIVER)

does not have a significant impact on CORP ROA in any of the

equations, when all variables are present.

76

R and D Tntensitv Excluded--ROA (Tables 5-38 and s 3Q)

The number of observations in the models containing ADVINT,

but not RDINT, range from 48 (Lag 5) to 680 (no lag).

Downsizing is not significant in the concurrent, nor any of

the lagged equations. The control variable DS for the

current year is significant and negative in only the Lag 2

equation. CAPINT is significant and negative in the

concurrent and Lag 3 equations. INDGR is significant and

negative in the no lag and Lag 2 regressions. INDPR is

significant and positive in the concurrent and Lag 5

equations. ADVINT and INDCN are not significant for any

concurrent or lagged equations. DIVER is significant and

positive (small values of around .005) for the concurrent.

Lag 1, and Lag 3 equations.

ADVINT and RDINT Excluded--ROA (Tables 5.40 and 5-41)-

The number of observations in the models excluding both

ADVINT and RDINT, range from 13 0 (Lag 5) to 1690 (no lag)

observations. Downsizing is not significant for the

concurrent, nor any of the lagged equations, for corporate

ROA. The control variable DS is insignificant also for all

lagged equations. DIVER is significant (p-values .0001 to

.006) and positive (parameter estimates .002 to .005) for all

concurrent and lagged regressions, except Lag 5. INDPR is

also significant and positive for the concurrent and Lag 3,

Lag 4, and Lag 5 equations. INDGR is significant and

77

negative in the no lag and Lag 2 equations. CAPINT is

significant and negative for all concurrent and lagged

regressions.

All Variables--TOBQ (Tables 5.42 and 5.43). The models

containing all the variables (TOBQ as dependent variable, and

DS (and DSP1-DSP5 representing downsizing in previous years),

DIVER, ADVINT, CAPINT, RDINT, INDCN, INDGR, INDPR as

independent variables) range from 27 (Lag 5) to 406 (no lag)

observations. Downsizing has a significant and positive

impact on CORP TOBQ in the Lag 2 (p-value .02, parameter

estimate 39.26), and the Lag 3 (p-value .003, parameter

estimate 21.07) equations. The DS control variable for the

current year is significant and negative only for the Lag l

equation. RDINT is significant and positive (as opposed to

the regressions using ROA) for the Lag 2, Lag 3, Lag 4 , and

Lag 5 equations (p-values from .0001 to .002, parameter

estimates from 17.55 to 195.72). ADVINT is significant and

negative for the concurrent equation, and then significant

and positive for the Lag 4 and Lag 5 equations. CAPINT is

significant and negative only for the Lag 5 equation. INDCN

is significant and positive for the concurrent equation only.

INDGR is significant and negative for the Lag 5 equation.

INDPR is not significant for any equation, concurrent or

lagged. Diversification (DIVER) shows a significant (and

78

positive) impact on CORP TOBQ in only the Lag 4 equation,

when all variables are present.

R and D Int. Excluded--TOBQ (Tables 5.44 and 5.45). The

number of observations in the models containing ADVINT, but

not RDINT, range from 47 (Lag 5) to 653 (no lag). Downsizing

is significant and positive for the Lag 2 (p-value .02,

parameter estimate 26.77) and the Lag 3 (p-value .02,

parameter estimate 13.34) regressions. The DS control

variable for the current year is not significant in any

lagged equation. ADVINT is significant and negative in the

concurrent equation, and significant and positive in the Lag

5 equation. CAPINT is significant and positive for only the

Lag 3 equation. INDCN is significant and positive in the

concurrent equation, and significant and negative in the Lag

1 equation. INDGR, INDPR and DIVER are not significant in

any concurrent or lagged equation.

RDINT and ADVINT Excluded--TOBO (Tables 5-46 and 5-47),

The number of observations in the models excluding both RDINT

and ADVINT, range from 126 to 1627 observations. Downsizing

has a significant and positive impact on CORP TOBQ in the Lag

2 (p-value .05, parameter estimate 7.80), the Lag 3 (p-value

.0001, parameter estimate 8.39), and Lag 5 (p-value .0007,

parameter estimate 15.60) equations. The DS control variable

for the current is not significant for any lagged equation.

CAPINT is significant and positive for all concurrent and

lagged equations, with the exception of Lag 5. INDCN is

79

significant and negative for the Lag 5 equation only. INDGR

is significant and positive for the concurrent equation, but

none of the lagged equations. INDPR is significant and

positive for the Lag 1 and Lag 5 equations. DIVER is

insignificant for all equations.

80

Table 5.1. Descriptive Statistics (SBU Data).

Variable N Mean Std. Dev.

Entire dataset (for TSCS data construction)

ROA DS ADVINT CAPINT RDINT INDCN INDGR INDPR

14008 10723 1805

14008 1093

13987 12493 14008

0.0245 0.0814 0.0354 5.7350 0.3799 0.7730 1.7165 0.0951

0.2461 0.4515 0.0702

147.6365 2.7034 0.1680

48.7379 0.0656

Downsizers only

ROA DS ADVINT CAPINT RDINT INDCN INDGR INDPR

4208 4179 450 4208 330

4200 4208 4208

-0.0419 -0.0557 0.0358 4.7757 0.2201 0.7 57 6 0.4032 0.0880

0.2646 0.4495 0.0377

169.7209 0-3530 0-1771 4.9553 0.0633

81

Table 5.2. Descriptive Statistics (CORP Data)

Variable N Mean Std. Dev.

Entire dataset (for TSCS data construction)

ROA TOBQ DS DIVER ADVINT CAPINT RDINT INDCN INDGR INDPR

6391 6030 5343 5830 2725 6392 4150 6396 5576 6396

0.0856 2.4366 0.0679 7.9503 0-0383 1.3320 0.2223 0.8718 0.1482 0.1040

0.1828 19.9614 0.4555 6.6101 0.0726 15.5213 8.1624 0.17 07 0.6626 0.0549

Downsizers only

ROA TOBQ DS DIVER ADVINT CAPINT RDINT INDCN INDGR INDPR

1917 1849 1910 1697 765 1917 1254 1917 1917 1917

0-0500 1.6463 -0.0374 7.9876 0.0396 1.1872 0.1297 0.8773 0.1307 0.0991

0.1584 9.4201 0.4620 6.5487 0.0466 3.8077 1.7785 0.1673 0.8305 0.0529

82

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84

Table 5.5. Number of SBU's and Corps, by Year

Year

1985 1986 1987 1988 1989 1990 1990

SBU'S

1499 1718 1930 2048 2198 2354 2270

Number of Corporations

814 853 904 922 947 97 9 1002

85

Table 5.6. Years of Data Available For TCSC regressions.

Years of Data

1 2 3 4 5 6 7

SBU'S

440 394 372 308 272 355 993

Number of Corporations

33 34 44 38 51 64 771

86

Table 5.7. Time Series Regression Results (SBU)--All Variables Included.

ROA as dependent varieible R-square 0.7 300

#sbu's #years #obs

4 6 24

Variable

DS ADVINT CAPINT RDINT INDCN INDGR INDPR

Parameter Estimate

-0.16 n/a n/a n/a n/a n/a n/a

p-value

0.06 0.80 0.10 0.76 0.91 0.11 0.84

87

Table 5.8. Lagged Time Series Regression Results (SBU)-All Variables Included. (ROA as Dependent Variable)

Lag 1 #sbu's #years #obs R-square

Lag 2 #sbu's #years #obs R-square

Lag 3 #sbu's #years #obs R-square

6 5 30 0.4751

6 4 24 0.4104

8 3 24 0.3613

Variable

DS ADVINT CAPINT RDINT INDCN INDGR INDPR

Variable

DS ADVINT CAPINT RDINT INDCN INDGR INDPR

Variable

DS ADVINT CAPINT RDINT INDCN INDGR INDPR

Parameter Estimate

-0.14 n/a n/a n/a n/a n/a n/a

Parameter Estimate

n/a 1.35 n/a n/a 0.36 n/a n/a

Parameter Estimate

n/a n/a 0.12 n/a n/a -0.20 n/a

p-value

0.05 0.08 0.71 0.11 0.15 0.39 0.35

p-value

0.70 0.04 0.06 0.13 0.05 0.70 0.11

p-value

0.09 0.60 0.05 0.09 0.28 0.04 0.69

88

Table 5.9. Time Series Regression Results (SBU) ADVINT Excluded.

ROA as dependent varieible R-square 0.2540

#sbu's #years #obs

66 6 396

Variable

DS CAPINT RDINT INDCN INDGR INDPR

Parameter Estimate

-0.20 0.05 -0.67 n/a n/a n/a

p-value

0.0001 0.0001 0.0001 0.90 0.88 0.26

89

Table 5.10. Lagged Time Series Regression Results (SBU)-ADVINT Excluded. (ROA as Dependent Variable)

Lag 1 #sbu's #years #obs R-square

Lag 2 #sbu's #years #obs R-square

Lag 3 #sbu's #years #obs R-square

83 5 415 0.0643

86 4 344 0.0535

97 3 291 0.0794

Variable

DS CAPINT RDINT INDCN INDGR INDPR

Variable

DS CAPINT RDINT INDCN INDGR INDPR

Variable

DS CAPINT RDINT INDCN INDGR INDPR

Parameter Estimate

-0.109 n/a -0.039 n/a n/a n/a

Parameter Estimate

n/a n/a -0.04 n/a n/a 0.45

Parameter Estimate

-0.085 -0.003 n/a n/a n/a 0.39

p-value

0.0004 0.30 0.03 0.6 0.44 0.29

p-value

0.30 0.75 0.02 0.26 0.78 0.03

p-value

0.007 0.09 0.15 0.21 0.92 0-04

90

Table 5.11. Time Series Regression Results (SBU)--RDINT Excluded.

ROA as dependent varieible R-square 0-1485

#sbu's #years #obs

115 6 690

Variable

DS ADVINT CAPINT INDCN INDGR INDPR

Parameter Estimate

-0.02 -0.71 -0.06 n/a n/a 0.72

p-value

0.01 0.0003 0.0001 0.11 0.64 0.0001

91

Table 5.12 Lagged Time Series Regression Results (SBU) RDINT Excluded. (ROA as Dependent Variable)

Lag 1 #sbu' s #years #obs R-square

Lag 2 #sbu's #years #obs R-square

Lag 3 #sbu's #years #obs R-square

122 5 610 0.0242

127 4 508 0.0206

129 3 387 0.0674

Variable

DS ADVINT CAPINT INDCN INDGR INDPR

Variable

DS ADVINT CAPINT INDCN INDGR INDPR

Variable

DS ADVINT CAPINT INDCN INDGR INDPR

Parameter Estimate

n/a n/a -0.03 n/a n/a 0.29

Parameter Estimate

n/a n/a n/a n/a n/a 0.32

Parameter Estimate

0.044 n/a -0.04 n/a n/a 0.36

p-value

0.99 0.75 0.03 0.46 0.51 0.006

p-value

0.42 0.81 0.56 0.69 0.72 0.005

p-value

0.009 0.15 0.02 0.87 0.87 0.003

92

Table 5.13. Time Series Regression Results (SBU)-RDINT and ADVINT Excluded.

ROA as dependent variable R-square 0.0510

#sbu's #years #obs

993 6 5958

Variable

DS CAPINT INDCN INDGR INDPR

Parameter Estimate

-0.05 n/a n/a n/a 0.60

p-value

0.0001 0.1 0.93 0.98 0.0001

93

Table 5.14. Lagged Time Series Regression Results (SBU) RDINT and ADVINT Excluded. (ROA as Dependent Variable)

Lag 1 #sbu's #years #obs R-square

Lag 2 #sbu's #years #obs R-square

Lag 3 #sbu's #years #obs R-square

993 5 4965 0.0215

993 4 3972 0.0164

993 3 2979 0.0208

Variable

DS CAPINT INDCN INDGR INDPR

Variable

DS CAPINT INDCN INDGR INDPR

Variable

DS CAPINT INDCN INDGR INDPR

Parameter Estimate

-0.01 -0.0005 n/a n/a 0-44

Parameter Estimate

n/a -0.0008 0.049 n/a 0.34

Parameter Estimate

n/a -0.0006 0.049 n/a 0.40

p-value

0.03 0.02 0.37 0.49 0.0001

p-value

0.08 0.0008 0.05 0.85 0.0001

p-value

0.74 0.02 0.08 0.34 0.0001

94

Table 5-15. Time Series Regression Results (CORP) All Variables included.

ROA as dependent variable R-square 0.2437

Variable

DS DIVER CAPINT ADVINT RDINT INDCN INDGR INDPR

Parameter Estimate

-0.06 n/a -0.05 n/a -0.53 -0.08 n/a 0.59

p-value

0.0001 0.59 0.0001 0.72 0.0001 0.009 0.74 0.0001

TOBQ as dependent variable R-square 0.07 92

#corps #years #obs

156 6 936

Variable

DS DIVER CAPINT ADVINT RDINT INDCN INDGR INDPR

Parameter Estimate

-1.04 n/a n/a n/a n/a n/a n/a 12.7

p-value

0.001 0.70 0.30 0.99 0.73 0.11 0.76 0.0001

95

Table 5.16. Lagged Time Series Regression Results (CORP)--All Variables Included. (ROA as Dependent Variable)

Lag 1 #corp's #years #obs R-square

Lag 2 #corp's #years #obs R-square

Lag 3 #corp's #years #obs R-square

184 5 920 0.0709

188 4 752 0.0957

193 3 57 9 0.2087

Variable

DS DIVER ADVINT CAPINT RDINT INDCN INDGR INDPR

Variable

DS DIVER ADVINT CAPINT RDINT INDCN INDGR INDPR

Variable

DS DIVER ADVINT CAPINT RDINT INDCN INDGR INDPR

Parameter Estimate

-0.06 n/a n/a -0.02 -0.16 -0.085 n/a 0.27

Parameter Estimate

-0.03 n/a 0.27 -0.03 -0.36 -0.13 -0.04 0.23

Parameter Estimate

n/a n/a n/a -0.02 -0.84 -0.18 n/a 0.30

p-value

0.0001 0.23 0.35 0.0012 0.05 0-04 0.40 0.0005

p-value

0.02 0.51 0.02 0.0001 0.0001 0.002 0.01 0.006

p-value

0.23 0.82 0.60 0.06 0.0001 0.0001 0.82 0.002

96

Table 5.17. Time Series Regression Results (CORP) ADVINT Excluded.

ROA as dependent variable R-square 0.1495

Variable

DS DIVER CAPINT RDINT INDCN INDGR INDPR

Parameter Estimate

-0.03 0.001 0.007 -0.02 n/a n/a 0.81

p-value

0.0001 0.03 0.0001 0.0001 0.53 0.40 0.0001

TOBQ as dependent varieible R-square 0.0494

#corps #years #obs

411 6 2466

Variable

DS DIVER CAPINT RDINT INDCN INDGR INDPR

Parameter Estimate

-1.29 -0.03 0.18 n/a n/a n/a 12.65

p-value

0.0001 0.04 0.0008 0.83 0.15 0.83 0.0001

97

Table 5.18. Lagged Time Series Regression Results (CORP) ADVINT Excluded. (ROA as Dependent Variable)

Lag 1 #corp's #years #obs R-square

Lag 2 #corp's #years #obs R-square

Lag 3 #corp's #years #obs R-square

448 5 2240 0.0525

450 4 1800 0.0220

455 3 1365 0.0417

Variable

DS DIVER CAPINT RDINT INDCN INDGR INDPR

Variable

DS DIVER CAPINT RDINT INDCN INDGR INDPR

Variable

DS DIVER CAPINT RDINT INDCN INDGR INDPR

Parameter Estimate

-0.019 0.001 n/a 0.005 -0.05 n/a 0.43

Parameter Estimate

n/a -0.0013 -0.012 0.019 -0.05 n/a 0.23

Parameter Estimate

n/a 0.0012 n/a n/a n/a n/a 0.31

p-value

0.001 0.02 0.20 0.05 0.05 0-43 0.0001

p-value

0.67 0.03 0.006 0.003 0.05 0.31 0.0001

p-value

0.40 0.04 0.33 0.78 0.26 0.72 0.0001

98

Table 5.19. Time Series Regression Results (CORP) RDINT Excluded.

ROA as dependent variable R-square 0.1209

Variable

DS DIVER CAPINT ADVINT INDCN INDGR INDPR

Parameter Estimate

-0.02 0.001 -0.06 -0.16 -0.07 n/a 0.45

p-value

0.0001 0.03 0.0001 0.04 0.02 0.34 0.0001

TOBQ as dependent variaible R-square 0.07 34

#corps #years #obs

236 6 1416

Variable

DS DIVER CAPINT ADVINT INDCN INDGR INDPR

Parameter Estimate

n/a n/a n/a n/a n/a n/a 13.0

p-value

0.19 0.91 0.59 0.17 0.06 0.91 0.0001

99

Table 5.20. Lagged Time Series Regression Results (CORP)--RDINT Excluded. (ROA as Dependent Variable)

Lag 1 #corp's #years #obs R-square

Lag 2 #corp's #years #obs R-square

Lag 3 #corp's #years #obs R-square

277 5 1385 0.0322

285 4 1140 0.0437

295 3 885 0.0854

Variable

DS DIVER ADVINT CAPINT INDCN INDGR INDPR

Variable

DS DIVER ADVINT CAPINT INDCN INDGR INDPR

Variable

DS DIVER ADVINT CAPINT INDCN INDGR INDPR

Parameter Estimate

n/a n/a n/a -0.022 n/a n/a 0.28

Parameter Estimate

n/a 0.002 n/a -0.027 -0.077 0.01 0.226

Parameter Estimate

0.013 0.002 n/a -0-012 n/a 0.017 0.44

p-value

0.32 0.07 0.995 0.0007 0.13 0.27 0.0001

p-value

0.47 0.04 0.31 0.0003 0.04 0.001 0.002

p-value

0.03 0.04 0.52 0.04 0.11 0.0001 0.0001

100

Table 5.21 Time Series Regression Results (CORP) RDINT and ADVINT Excluded.

ROA as dependent variedsle R-square 0.0894

Variable

DS DIVER CAPINT INDCN INDGR INDPR

Parameter Estimate

-0.009 0.002 n/a n/a n/a 0.67

p-value

0.002 0.003 0.15 0.18 0.95 0.0001

TOBQ as dependent variable R-square 0.0062

#corps #years #obs

664 6 3984

Variable

DS DIVER CAPINT INDCN INDGR INDPR

Parameter Estimate

n/a n/a n/a -3.3 n/a 9.7

p-value

0.53 0.09 0.13 0.002 0.31 0.005

101

Table 5.22. Lagged Time Series Regression Results (CORP) RDINT and ADVINT Excluded. (ROA as Dependent Variable)

Lag 1 #corp's #years #obs R-square

Lag 2 #corp's #years #obs R-square

Lag 3 #corp's #years #obs R-square

704 5 3520 0.0413

704 4 2816 0.0166

704 3 2112 0.0402

Variable

DS DIVER CAPINT INDCN INDGR INDPR

Variable

DS DIVER CAPINT INDCN INDGR INDPR

Variable

DS DIVER CAPINT INDCN INDGR INDPR

Parameter Estimate

-0-006 0.002 0.003 n/a -0.008 0.40

Parameter Estimate

n/a 0.002 n/a n/a 0.006 0.19

Parameter Estimate

n/a 0.002 -0.005 n/a 0.008 0.26

p-value

0.04 0.001 0.0001 0.10 0.0003 0.0001

p-value

0-16 0-001 0.69 0.09 0.007 0.0001

p-value

0.11 0.002 0.0001 0.31 0.0003 0.0001

102

Table 5.23. Lagged Time Series Regression Results (CORP)-All Variables Included. (TOBQ as Dependent Variable)

Lag 1 #corp•s #years #obs R-square

Lag 2 #corp's #years #obs R-square

Lag 3 #corp's #years #obs R-square

17 8 5 890 0.0186

184 4 736 0.0285

192 3 57 6 0.0368

Variable

DS DIVER ADVINT CAPINT RDINT INDCN INDGR INDPR

Variable

DS DIVER ADVINT CAPINT RDINT INDCN INDGR INDPR

Variable

DS DIVER ADVINT CAPINT RDINT INDCN INDGR INDPR

Parameter Estimate

n/a n/a n/a n/a n/a n/a n/a 9.14

Parameter Estimate

n/a n/a n/a -1.25 14.5 n/a n/a 8.89

Parameter Estimate

2.02 n/a n/a -2.03 n/a n/a n/a 17.40

p-value

0.12 0-45 0.59 0.12 0.56 0.16 0.38 0.05

p-value

0.15 0.81 0.39 0.02 0.003 0.58 0.20 0.09

p-value

0.04 0.58 0.83

.0.02 ' 0.07 0.58 0.60 0.009

103

Table 5.24. Lagged Time Series Regression Results (CORP)--ADVINT Excluded. (TOBQ as Dependent Variable)

Lag 1 #corp's #years #obs R-square

Lag 2 #corp's #years #obs R-square

Lag 3 #corp's #years #obs R-square

439 5 2195 0.0364

444 4 1776 0.0096

454 3 1362 0.0154

Variable

DS DIVER CAPINT RDINT INDCN INDGR INDPR

Variable

DS DIVER CAPINT RDINT INDCN INDGR INDPR

Variable

DS DIVER CAPINT RDINT INDCN INDGR INDPR

Parameter Estimate

n/a n/a 0.14 n/a n/a n/a 9.39

Parameter Estimate

n/a -0.031 n/a n/a n/a n/a 6.13

Parameter Estimate

n/a n/a n/a n/a n/a n/a 7.60

p-value

0-45 0.07 0.01 0.20 0.21 0.07 0.0001

p-value

0.91 0.05 0.70 0.90 0.23 0.33 0.009

p-value

0.92 0.11 0.97 0.58 0.20 0.62 0.006

104

Table 5.25. Lagged Time Series Regression Results (CORP) RDINT Excluded. (TOBQ as Dependent Variable)

Lag 1 #corp's #years #obs R-square

Lag 2 #corp's #years #obs R-square

Lag 3 #corp's #years #obs R-square

268 5 1340 0.0201

27 8 4 1112 0.0191

291 3 87 3 0.0232

Variable

DS DIVER ADVINT CAPINT INDCN INDGR INDPR

Variable

DS DIVER ADVINT CAPINT INDCN INDGR INDPR

Variable

DS DIVER ADVINT CAPINT INDCN INDGR INDPR

Parameter Estimate

n/a n/a n/a n/a n/a n/a 10.3

Parameter Estimate

n/a n/a 9.8 n/a n/a n/a 8.13

Parameter Estimate

n/a n/a 8.73 n/a n/a n/a 10.96

p-value

0.28 0.71 0.46 0.29 0.15 0.26 0.0002

p-value

0.34 0.60 0.01 0.49 0.27 0.88 0-01

p-value

0.49 0.64 0.04 0-99 0.28 0.66 0.003

105

Table 5.26 Lagged Time Series Regression Results (CORP)--RDINT and ADVINT Excluded. (TOBQ as Dependent Variable)

Lag 1 #corp's #years #obs R-square

Lag 2 #corp's #years #obs R-square

Lag 3 #corp's #years #obs R-square

688 5 3440 0.0046

694 4 2776 0.0032

700 3 2100 0.0045

Variable

DS DIVER CAPINT INDCN INDGR INDPR

Variable

DS DIVER CAPINT INDCN INDGR INDPR

Variable

DS DIVER CAPINT INDCN INDGR INDPR

Parameter Estimate

n/a n/a n/a -3.5 n/a n/a

Parameter Estimate

n/a n/a n/a -3.8 n/a n/a

Parameter Estimate

n/a n/a n/a -5.4 n/a n/a

p-value

0.96 0.13 0.08 0.006 0.83 0.10

p-value

0.69 0.13 0.38 0.01 0.91 0.62

p-value

0-88 0.15 0.51 0.007 0.75 0.79

106

Table 5.27. Special Lagged Regression Results (SBU)-Base Year ROA as Independent Variable.

Lag 1 n R-scjuare

3028 0.4770

ROA as dependent -vsLriahle Variable Parameter

Estimate

DS ROA

n/a 0.64

p-

0. 0.

-value

.26

.0001

Lag 2 n R-scjuare

2069 0.3049

ROA as dependent variable V a r i a b l e

DS ROA

Parameter Estimate

n/a 0.48

P-

0. 0.

•value

.14

.0001

Lag 3 n R-scjuare

1343 0.2290

ROA as dependent variable Variable Parameter

Estimate p-value

DS ROA

-0.086 0.41

0.03 0.0001

Lag 4 n R-square

816 0.1323

ROA as dependent varieUsle Variable Parameter

Estimate p-value

DS ROA

n/a 0.31

0.34 0.0001

Lag 5 n R-square

399 0.1053

ROA as dependent variable Variable

DS ROA

Parameter Estimate

n/a 0.29

p-value

0.13 0.0001

107

Table 5.28 Special Lagged Regression Results (CORP) Base Year ROA as independent Variable.

Lag 1 n R-scjuare

17 50 0.5197

ROA as dependent variable Variable Parameter

Estimate p-value

DS ROA

n/a 0.67

0.62 0.0001

Lag 2 n R-square

1306 0.3912

ROA as dependent variable Variable Parameter

Estimate p-value

DS ROA

n/a 0.45

0.95 0.0001

Lag 3 n R-scjuare

950 0.3243

ROA as dependent variable Variable

DS ROA

Parameter Estimate

n/a 0.37

p-value

0.23 0.0001

Lag 4 n R-square

649 0.1890

ROA as dependent variable Variable Parameter

Estimate

DS ROA

n/a 0.25

p-value

0.17 0.0001

Lag 5 n R-square

350 0.0095

ROA as dependent variable Variable Parameter

Estimate

DS ROA

n/a n/a

p-value

0.11 0.65

108

Table 5.29. Special Lagged Regression Results (CORP)--Base Year TOBQ as independent Variable.

Lag 1 n R-square

1687 0.0131

TOBQ as dependent varisible Variable Parameter

Estimate p-value

DS TOBQ

n/a 0.15

0.97 0.0001

Lag 2 n R-square

1260 0.0008

TOBQ as dependent variable Variable Parameter

Estimate p-value

DS TOBQ

n/a n/a

0.96 0.32

Lag 3 n R-square

915 0-0013

TOBQ as dependent variable Variable Parameter

Estimate

DS TOBQ

n/a n/a

p-value

0.66 0.31

Lag 4 n R-square

628 0-0122

TOBQ as dependent variable Variable Parameter

Estimate

DS TOBQ

1.9 n/a

p-value

0.03 0.12

Lag 5 n R-scjuare

332 0.0249

TOBQ as dependent variable Variable Parameter

Estimate

DS TOBQ

10.47 n/a

p-value

0.02 0.06

109

Table 5.30. Concurrent and Lagged (0-2) Regression Results (SBU)--All Variables Included. (ROA as Dependent Variable)

No lag

n R-square

Lag 1

n R-square

Lag 2

n R-square

24 0.5184

7 n/a

4 n/a

Variable

DS ADVINT CAPINT RDINT INDCN INDGR INDPR

Variable

DSPl ADVINT CAPINT RDINT INDCN INDGR INDPR DS

Variable

DSP2 ADVINT CAPINT RDINT INDCN INDGR INDPR DS

Parameter Estimate

n/a -7.09 n/a n/a n/a n/a n/a

Parameter Estimate

n/a n/a n/a n/a n/a n/a n/a n/a

Parameter Estimate

n/a n/a n/a n/a n/a n/a n/a n/a

p-value

0.39 0.01 0.98 0.22 0.68 0.61 0.98

p-value

n/a n/a n/a n/a n/a n/a n/a n/a

p-value

n/a n/a n/a n/a n/a n/a n/a n/a

110

Table 5.31. Concurrent and Lagged (3-5) Regression Results (SBU)--All Variables included. (ROA as Dependent Variable)

Lag 3

n R-square

Lag 4

n R-square

Lag 5

n R-square

2 n/a

2 n/a

0 n/a

Variable

DSP3 ADVINT CAPINT RDINT INDCN INDGR INDPR DS

Variable

DSP4 ADVINT CAPINT RDINT INDCN INDGR INDPR DS

Variable

DSP5 ADVINT CAPINT RDINT INDCN INDGR INDPR DS

Parameter Estimate

n/a n/a n/a n/a n/a n/a n/a n/a

Parameter Estimate

n/a n/a n/a n/a n/a n/a n/a n/a

Parameter Estimate

n/a n/a n/a n/a n/a n/a n/a n/a

p-value

n/a n/a n/a n/a n/a n/a n/a n/a

p-value

n/a n/a n/a n/a n/a n/a n/a n/a

p-value

n/a n/a n/a n/a n/a n/a n/a n/a

111

Table 5.32. Concurrent and Lagged (0-2) Regression Results (SBU)--RDINT Excluded. (ROA as Dependent Variable)

No lag

n R-square

Lag 1

n R-square

Lag 2

n R-square

447 0.0774

107 0.1050

79 0.2048

Variable

DS ADVINT CAPINT INDCN INDGR INDPR

Variable

DSPl ADVINT CAPINT INDCN INDGR INDPR DS

Variable

DSP2 ADVINT CAPINT INDCN INDGR INDPR DS

Parameter Estimate

n/a n/a -0.009 n/a n/a 0.59

Parameter Estimate

n/a n/a n/a n/a n/a n/a n/a

Parameter Estimate

n/a n/a n/a n/a n/a 0-79 -0.16

p-value

0.33 0.79 0.01 0.08 0.08 0.0001

p-value

0.10 0.21 0.40 0.13 0.88 0.15 0.96

p-value

0.24 0.78 0.87 0.93 0.90 0.005 0.01

112

Table 5.33. Concurrent and Lagged (3-5) Regression Results (SBU)--RDINT Excluded. (ROA as Dependent Variable)

Lag 3

n R-square

Lag 4

n R-square

Lag 5

n R-square

54 0.0941

39 0.1711

22 0.3111

Variable

DSP3 ADVINT CAPINT INDCN INDGR INDPR DS

Variable

DSP4 ADVINT CAPINT INDCN INDGR INDPR DS

Variable

DSP5 ADVINT CAPINT INDCN INDGR INDPR DS

Parameter Estimate

n/a n/a n/a n/a n/a n/a n/a

Parameter Estimate

n/a n/a n/a n/a n/a n/a n/a

Parameter Estimate

n/a n/a n/a n/a n/a n/a n/a

p-value

0.97 0.82 0.44 0.75 0.08 0.55 0.66

p-value

0.65 0.96 0.39 0.63 0.36 0.59 0.11

p-value

0.42 0.07 0.15 0.27 0.64 0.52 0.29

113

Table 5.34. Concurrent and Lagged (0-2) Regression Results (SBU)--RDINT and ADVINT Excluded. (ROA as Dependent Variable)

No lag

n R-square

Lag 1

n R-square

Lag 2

n R-square

4171 0.0395

1016 0.0642

741 0.0563

Variable

DS CAPINT INDCN INDGR INDPR

Variable

DSPl CAPINT INDCN INDGR INDPR DS

Variable

DSP2 CAPINT INDCN INDGR INDPR DS

Parameter Estimate

-0.08 -0.00005 n/a n/a 0.58

Parameter Estimate

-0.30 -0.002 n/a n/a 0.52 -0.04

Parameter Estimate

-0.24 n/a n/a n/a 0.66 -0.04

p-value

0.0001 0.02 0.08 0.62 0.0001

p-value

0.0001 0-0006 0.62 0.24 0.0001 0.003

p-value

0.0001 0.06 0.50 0.92 0.0001 0.02

114

Table 5.35. Concurrent and Lagged (3-5) Regression Results (SBU)--RDINT and ADVINT Excluded. (ROA as Dependent Variable)

Lag 3

n R-square

Lag 4

n R-square

Lag 5

n R-square

536 0.0693

345 0.1066

196 0.1276

Variable

DSP3 CAPINT INDCN INDGR INDPR DS

Variable

DSP4 CAPINT INDCN INDGR INDPR DS

Variable

DSP5 CAPINT INDCN INDGR INDPR DS

Parameter Estimate

-0.18 -0.02 n/a n/a 0.47 -0.04

Parameter Estimate

n/a n/a n/a n/a n/a -0.27

Parameter Estimate

n/a -0.002 0.15 n/a n/a -0.16

p-value

0.01 0.0001 0.07 0.49 0.006 0.04

p-value

0-14 0.43 0.44 0.69 0.58 0.0001

p-value

0.90 0.004 0.04 0.49 0.66 0.0002

115

Table 5.36. Concurrent and Lagged (0-2) Regression Results (CORP)--All Variables Included. (ROA as Dependent Variable)

No lag

n R-square

Lag 1

n R-square

Lag 2

n R-square

425 0.2568

101 0.1512

71 0.1279

Variable

DS DIVER ADVINT CAPINT RDINT INDCN INDGR INDPR

Variable

DSPl DIVER ADVINT CAPINT RDINT INDCN INDGR INDPR DS

Variable

DSP2 DIVER ADVINT CAPINT RDINT INDCN INDGR INDPR DS

Parameter Estimate

-0.09 n/a n/a 0.08 -0.89 n/a n/a 0.43

Parameter Estimate

n/a n/a n/a n/a -1.28 n/a n/a n/a n/a

Parameter Estimate

n/a n/a n/a n/a n/a n/a n/a n/a n/a

p-value

0.003 0.78 0.39 0.0001 0.0001 0.91 0.48 0.002

p-value

0.27 0.48 0.18 0.80 0.006 0.73 0.68 0-47 0.92

p-value

0.67 0.31 0.35 0.51 0.20 0.42 0.53 0.56 0.88

116

Table 5.37. Concurrent and Lagged (3-5) Regression Results (CORP)- -All Variables Included. (ROA as Dependent Variable)

Lag 3

n R-square

Lag 4

n R-square

Lag 5

n R-square

59 0.5056

46 0.4590

27 0.6519

Variable

DSP3 DIVER ADVINT CAPINT RDINT INDCN INDGR INDPR DS

Variable

DSP4 DIVER ADVINT CAPINT RDINT INDCN INDGR INDPR DS

Variable

DSP5 DIVER ADVINT CAPINT RDINT INDCN INDGR INDPR DS

Parameter Estimate

n/a n/a n/a n/a -0.98 n/a n/a n/a n/a

Parameter Estimate

n/a n/a n/a n/a n/a n/a n/a n/a n/a

Parameter Estimate

n/a n/a n/a n/a n/a n/a n/a 0.81 n/a

p-value

0.37 0.63 0.16 0.06 0.001 0.30 0.72 0.30 0.37

p-value

0.63 0.24 0.12 0.23 0.09 0.07 0.06 0.98 0.07

p-value

0.25 0.72 0.16 0.43 0.71 0.48 0.15 0.01 0.21

117

Table 5.38. Concurrent and Lagged (0-2) Regression Results (CORP)- -RDINT Excluded. (ROA as Dependent Variable)

No lag

n R-square

Lag 1

n R-square

Lag 2

n R-square

680 0.0588

158 0.0724

118 0.1908

Variable

DS DIVER ADVINT CAPINT INDCN INDGR INDPR

Variable

DSPl DIVER ADVINT CAPINT INDCN INDGR INDPR DS

Variable

DSP2 DIVER ADVINT CAPINT INDCN INDGR INDPR DS

Parameter Estimate

n/a 0.004 n/a -0.01 n/a -0.02 0.23

Parameter Estimate

n/a 0.005 n/a n/a n/a n/a n/a n/a

Parameter Estimate

n/a n/a n/a n/a n/a -0.03 n/a -0.11

p-value

0-09 0-0001 0.45 0.009 0.94 0.008 0.05

p-value

0-43 0.04 0.07 0.69 0.64 0.19 0.51 0.50

p-value

0.63 0.13 0.22 0.55 0.74 0.0003 0.80 0.05

118

Table 5.39. Concurrent and Lagged (3-5) Regression Results (CORP)--RDINT Excluded. (ROA as Dependent Variable)

Lag 3

n R-square

94 0.1581

Lag 4

n R-square

73 0.2405

Lag 5

n R-square

48 0.5022

Variable

DSP3 DIVER ADVINT CAPINT INDCN INDGR INDPR DS

Variable

DSP4 DIVER ADVINT CAPINT INDCN INDGR INDPR DS

Variable

DSP5 DIVER ADVINT CAPINT INDCN INDGR INDPR DS

Parameter Estimate

n/a 0.005 n/a -0-07 n/a n/a n/a n/a

Parameter Estimate

n/a n/a n/a n/a n/a n/a n/a n/a

Parameter Estimate

n/a n/a n/a n/a n/a n/a 0.90 n/a

p-value

0.79 0.04 0.18 0.009 0.28 0.68 0.45 0.85

p-value

0.72 0.12 0.12 0.15 0.45 0.11 0.33 0.23

p-value

0.43 0.74 0.07 0.88 0.31 0.14 0.0001 0.21

119

Table 5.40. Concurrent and Lagged (0-2) Regression Results (CORP)- -RDINT and ADVINT Excluded. (ROA as Dependent Variable)

No Lag

n R-square

Lag 1

n R-square

Lag 2

n R-square

1690 0.1070

37 3 0.2117

311 0.2342

Variable

DS DIVER CAPINT INDCN INDGR INDPR

Variable

DSPl DIVER CAPINT INDCN INDGR INDPR DS

Variable

DSP2 DIVER CAPINT INDCN INDGR INDPR DS

Parameter Estimate

n/a 0.004 -0.01 n/a -0.02 0.34

Parameter Estimate

n/a 0.005 -0.02 n/a n/a n/a n/a

Parameter Estimate

n/a 0.003 -0.04 n/a -0.03 n/a n/a

p-value

0.15 0.0001 0.0001 0.64 0.003 0.0001

p-value

0.32 0.0004 0.0001 0.92 0.09 0.10 0.56

p-value

0.91 0.001 0-0001 0.82 0.0001 0.12 0.09

120

Table 5.41. Concurrent and Lagged (3-5) Regression Results (CORP)--RDINT and ADVINT Excluded. (ROA as Dependent Variable)

Lag 3

n R-square

Lag 4

n R-square

Lag 5

n R-square

250 0.2113

186 0-4821

130 0.3181

Variable

DSP3 DIVER CAPINT INDCN INDGR INDPR DS

Variable

DSP4 DIVER CAPINT INDCN INDGR INDPR DS

Variable

DSP5 DIVER CAPINT INDCN INDGR INDPR DS

Parameter Estimate

n/a 0.003 -0.008 n/a n/a 0.39 n/a

Parameter Estimate

n/a 0.002 -0.06 n/a n/a 0.34 n/a

Parameter Estimate

n/a n/a -0.04 n/a n/a 0.84 n/a

p-value

0.49 0.006 0.0001 0.79 0.61 0.004 0.86

p-value

0.57 0.005 0.0001 0.24 0.06 0.001 0.17

p-value

0.99 0.23 0.04 0-54 0.92 0.0001 0.09

121

Table 5.42 Concurrent and Lagged (0-2) Regression Results (CORP)--All Variables Included. (TOBQ as Dependent Variable)

No Lag

n R-square

406 0.0373

Lag 1

Variable

DS DIVER ADVINT CAPINT RDINT INDCN INDGR INDPR

Variable

n R-square

Lag 2

n R-square

98 0.1703

67 0.2562

DSPl DIVER ADVINT CAPINT RDINT INDCN INDGR INDPR DS

Variable

DSP2 DIVER ADVINT CAPINT RDINT INDCN INDGR INDPR DS

Parameter Estimate

n/a n/a -32.82 n/a n/a 10.16 n/a n/a

Parameter Estimate

n/a n/a n/a n/a n/a n/a n/a n/a -3.56

Parameter Estimate

39.26 n/a n/a n/a 195.72 n/a n/a n/a n/a

p-value

0.14 0.65 0.03 0.27 0.12 0.03 0.74 0.34

p-value

0.33 0.86 0.43 0-90 0.33 0.21 0.79 0.21 0.02

p-value

0.02 0.80 0.46 0.95 0.001 0.25 0.60 0.17 0.95

122

Table 5.43. Concurrent and Lagged (3-5) Regression Results (CORP)--All Variables included. (TOBQ as Dependent Variable)

Lag 3

n R-square

Lag 4

n R-scjuare

Lag 5

n R-square

56 0.5231

46 0.5720

27 0.8281

Variable

DSP3 DIVER ADVINT CAPINT RDINT INDCN INDGR INDPR DS

Variable

DSP4 DIVER ADVINT CAPINT RDINT INDCN INDGR INDPR DS

Variable

DSP5 DIVER ADVINT CAPINT RDINT INDCN INDGR INDPR DS

Parameter Estimate

21.07 n/a n/a n/a 76.99 n/a n/a n/a n/a

Parameter Estimate

n/a 0.09 15.76 n/a 22.90 n/a n/a n/a n/a

Parameter Estimate

n/a n/a 26.02 -2.99 17.55 n/a 6.96 n/a n/a

p-value

0.003 0.85 0.93 0.72 0.0001 0.16 0.14 0.09 0.19

p-value

0.15 0.05 0.01 0.11 0.0001 0.93 0.61 0.11 0.62

p-value

0.07 0.51 0.005 0.04 0.002 0.33 0.01 0.73 0.87

123

Table 5.44. Concurrent and Lagged (0-2) Regression Results (CORP)--RDINT Excluded. (TOBQ as Dependent Variable)

No lag

n R-square

Lag 1

n R-square

Lag 2

n R-square

653 0.0189

153 0.1139

113 0.0949

Variable

DS DIVER ADVINT CAPINT INDCN INDGR INDPR

Variable

DSPl DIVER ADVINT CAPINT INDCN INDGR INDPR DS

Variable

DSP2 DIVER ADVINT CAPINT INDCN INDGR INDPR DS

Parameter Estimate

n/a n/a -18.63 n/a 6.81 n/a n/a

Parameter Estimate

n/a n/a n/a n/a -3.88 n/a n/a n/a

Parameter Estimate

26.77 n/a n/a n/a n/a n/a n/a n/a

p-value

0.66 0.53 0.05 0.39 0.02 0.18 0.26

p-value

0.59 0.52 0.09 0.45 0.02 0.30 0.23 0.20

p-value

0.02 0.73 0.27 0.68 0.75 0.33 0.20 0.19

124

Table 5.45. Concurrent and Lagged (3-5) Regression Results (CORP)- -RDINT Excluded. (TOBQ as Dependent Variable)

Lag 3

n R-square

Lag 4

n R-square

Lag 5

n R-square

90 0.2720

71 0.1148

47 0.2954

Variable

DSP3 DIVER ADVINT CAPINT INDCN INDGR INDPR DS

Variable

DSP4 DIVER ADVINT CAPINT INDCN INDGR INDPR DS

Variable

DSP5 DIVER ADVINT CAPINT INDCN INDGR INDPR DS

Parameter Estimate

13.34 n/a n/a 3.40 n/a n/a n/a n/a

Parameter Estimate

n/a n/a n/a n/a n/a n/a n/a n/a

Parameter Estimate

n/a n/a 19.64 n/a n/a n/a n/a n/a

p-value

0.02 0.34 0.73 0-001 0.57 0.27 0.17 0.70

p-value

0.74 0.52 0.09 0.19 0.38 0.80 0.79 0.51

p-value

0.97 0.9996 0.02 0.32 0.98 0.25 0.71 0.41

125

Table 5.46. Concurrent and Lagged (0-2) Regression Results (CORP)- -RDINT and ADVINT Excluded. (TOBQ as Dependent Variable)

No lag

n R-square

Lag 1

n R-square

Lag 2

n R-square

1627 0.0104

358 0.1868

300 0.0534

Variable

DS DIVER CAPINT INDCN INDGR INDPR

Variable

DSPl DIVER CAPINT INDCN INDGR INDPR DS

Variable

DSP2 DIVER CAPINT INDCN INDGR INDPR DS

Parameter Estimate

n/a n/a 0.16 n/a 0.60 n/a

Parameter Estimate

n/a n/a 0.15 n/a n/a 6.71 n/a

Parameter Estimate

7.80 n/a 0.96 n/a n/a n/a n/a

p-value

0.55 0.97 0.005 0.17 0.04 0.12

p-value

0.08 0.66 0-0001 0.17 0.13 0.004 0.12

p-value

0.05 0.65 0.05 0.44 0.08 0.15 0-17

126

Table 5.47. Concurrent and Lagged (3-5) Regression Results (CORP)--RDINT and ADVINT Excluded. (TOBQ as Dependent Variable)

Lag 3

Variable

n R-scjuare

Lag 4

n R-square

Lag 5

n R-square

242 0.1041

181 0.1400

126 0.2412

DSP3 DIVER CAPINT INDCN INDGR INDPR DS

Variable

DSP4 DIVER CAPINT INDCN INDGR INDPR DS

Variable

DSP5 DIVER CAPINT INDCN INDGR INDPR DS

Parameter Estimate

8.39 n/a 0.14 n/a n/a n/a n/a

Parameter Estimate

n/a n/a 0.82 n/a n/a n/a n/a

Parameter Estimate

15.60 n/a n/a -10.80 n/a 35.63 n/a

p-value

0.0001 0.84 0.003 0-46 0-40 0.21 0.67

p-value

0.88 0.99 0.0001 0.59 0.78 0.06 0.33

p-value

0.0007 0.09 0.15 0.003 0.34 0.002 0.73

127

CHAPTER VI

DISCUSSION AND CONCLUSIONS

This chapter presents an overview of the study,

including the research question and its importance; a

discussion of the results of the study and the conclusions

based on the results; and the implications for researchers

and practitioners. The strengths and limitations of the

study are discussed, and directions for further research are

presented.

The purpose of this study is to explore the relationship

between downsizing and performance. In this study,

downsizing is characterized as a facet of organizational

strategy for improving performance (Cameron, Freeman, &

Mishra, 1993). In this study downsizing is defined as an

intentional set of activities designed to improve

organizational performance, and which involves reductions in

personnel (Cameron, Freeman, & Mishra, 1993).

Since the 1980's through the present time, around ten

million jobs have been eliminated in the U.S. (Budros, 1997) .

Organizational downsizing has been called a "key feature" of

the system of "new capitalism," an economy characterized by

international competition, deregulation of industries, and

technological change (Budros, 1997). It is not clear whether

downsizing does indeed improve profitability, since despite

the prevalence of downsizing, it has not been studied much

128

(Cameron, 1994). Budros (1997) points out that expectations

are that downsizing will improve performance, and that

companies that downsized are generally seen as positive role

models and accjuire social benefits, whether or not operating

efficiencies are achieved. However, there are critics of

downsizing who point out various adverse effects, as well as

more emphasis on growth in recent years (Budros, 1997).

Because of the limited amount of theoretical and

empirical work on downsizing, especially linked with

performance issues, the primary research cguestion for this

study is: 'What is the impact of downsizing on corporate and

strategic business unit economic performance, controlling for

market conditions? This study contributes to the field by

examining performance outcomes after downsizing occurs in

corporations and strategic business units (SBU's).

The study employs multivariate regression analysis

(including time series cross-sectional regression) for the

inclusion of specific variables that could have an impact on

economic performance of an organization. The study uses

comprehensive multi-year, multi-industry ciata for the

examination of performance issues (measured both as

accounting-based and as hybrid market/accounting variables)

associated with downsizing, including industry structure

variables (industry concentration, industry growth, industry

profitability), corporate strategy variables

(diversification), and business unit strategy variables

129

(capital intensity, R&D intensity, advertising intensity).

This study addresses an issue that is underdeveloped

theoretically, but is widespread and iirportant for businesses

and the national economy.

Conclusions

Time Series Cross-Sectic^nai (TSCS) Regression--SBU LPVPI

Over the four models run (one with all independent

variables of downsizing, capital intensity, advertising

intensity, R&D intensity, industry concentration, industry

growth, and industry profitability; one with advertising

excluded; one with R&D intensity excluded; and one with both

advertising intensity and R&D intensity excluded) , downsizing

has a small negative effect on SBU ROA in the year of the

downsizing. The negative effect generally continues into the

year after the downsizing occurs (Lag l), and then

extinguishes. The exceptions to the early wash out are seen

only in the equation excluding advertising intensity (where

the negative effect reappears in the third year after

downsizing), and the equation excluding R&D intensity (where

the negative effect is seen only on the year of the

downsizing, is insignificant for the next two years, and then

becomes a small positive effect in the third year after

downsizing).

130

other variables that are shown to have an impact on SBU

ROA include capital intensity (generally negative and small,

in most concurrent and lagged equations); advertising

intensity (negative, and only in the concurrent equation) ;

R&D intensity (negative but weakening effect from the

concurrent year through the second year after downsizing);

industry concentration (positive in the Lag 2 ecjuation, only

when both advertising intensity and R&D intensity are

excluded); and industry profitability (positive throughout

most no lag and lagged periods).

TSCS Regression - Corporate Level (ROA)

Over the four models run (one with all independent

variables of downsizing, diversification, capital intensity,

advertising intensity, R&D intensity, industry concentration,

industry growth, and industry profitability; one with

advertising excluded; one with R&D intensity excluded; and

one with both advertising intensity and R&D intensity

excluded), downsizing has a small negative effect on

corporate ROA in the year of the downsizing. The corporate

ROA effects are similar to those found for the SBU ROA. The

negative effect generally continues into the year after the

downsizing occurs (Lag 1), and then abates. The negative

effect continues for two years after downsizing in the

ecjuation with all the independent variables. The exception

is seen only in the equation excluding R&D intensity where

131

the negative effect is seen only on the year of the

downsizing, is insignificant for the next two years, and then

becomes a small positive effect in the third year after

downsizing, again similar results to those obtained for SBU

ROA.

Other variables that are shown to have an impact on

corporate ROA include capital intensity (usually negative and

small, in most concurrent and lagged ecjuations) ; R&D

intensity (generally negative, although when advertising

intensity is excluded from the equation, it is positive in

the Lag l and Lag 2 periods) ; industry concentration

(negative for most concurrent and lagged equations) ; and

industry profitability (positive throughout all ecjuations,

both concurrent and lagged). interestingly, the

diversification variable is significant (and generally

positive) when either or both advertising intensity or R&D

intensity are excluded from the ecjuation.

TSCS Regression - Corporate Level (TQBQ)

Over the four models run (one with all independent

variables of downsizing, diversification, capital intensity,

advertising intensity, R&D intensity, industry concentration,

industry growth, and industry profitability; one with

advertising excluded; one with R & D intensity excluded; and

one with both advertising intensity and R&D intensity

excluded) , downsizing has a negative effect on corporate TOBQ

132

in the year of the downsizing. However, the downsizing

effect does not persist beyond the year of the downsizing,

except in one case, in the equation with all the variables,

the downsizing variable is insignificant for Lag 1 and Lag 2,

but reverses its sign and becomes positive in the third year

after downsizing (Lag 3). Interestingly, the downsizing

variable is never significant when R&D intensity is excluded

from the equation.

Other variables that are shown to have an impact on

corporate TOBQ include capital intensity (positive when

advertising intensity is excluded, no lag and Lag l; but

negative when all variables are included. Lag 2 and Lag 3);

industry concentration (negative for the concurrent and

lagged ecjuations when advertising and R&D intensity are

excluded); and industry profitability (positive throughout

all ecjuations, both concurrent and lagged, except the

equation excluding both advertising intensity and R&D

intensity). The diversification variable is significant (and

negative) only when advertising intensity is excluded from

the equation (no lag and Lag 2).

Base Year ROA or TQBQ as Independent Variables

The dependent variables for this analysis were the

lagged ROA and TOBQ (for one through five years after the

downsizing occurred).

133

SBU Level Results. These results show that downsizing

has a small negative effect on SBU ROA that appears three

years after the downsizing action, and then extinguishes

thereafter.

Corporate Level Results - ROA. This set of equations

show that downsizing has no effect on CORP ROA for one

through five years after the downsizing occurs.

Corporate Level Results - TQBQ. These results show that

downsizing has a positive effect on CORP TOBQ that only

appears four and five years after the downsizing action.

Two basic conclusions have been made with respect to the

time series and other above analyses: (l) there appears to

be a dampening effect of the downsizing impact over time, and

(2) SBU's seem to be more sensitive to the impact of

downsizing than are corporations. In this study, a more in-

depth analysis was done to further investigate and clarify

the relationship between downsizing and organizational

performance. The next section discusses the results from the

analysis done on only those SBU's or corporations that

downsized at least ten percent, and continues for the next

five years after the downsizing occurred.

Pooled Crnss-Sectional Regression Ppsnits - SBU

Only those SBU's which had downsized at least by 10

percent in a particular year are included for this set of

134

regressions, including the data for up to five years after

the downsizing occurred.

In the model with both advertising intensity and R & D

intensity excluded, downsizing has a negative effect on SBU

ROA in the year of the downsizing. The negative effect

persists for three years after the downsizing occurs (Lag 1,

Lag 2, and Lag 3) and then extinguishes.

Other variables that are shown to have an impact on SBU

ROA include downsizing in the current year (negative in all

lagged equations), capital intensity (negative and small);

and industry profitability (positive throughout the

concurrent. Lag 1, Lag 2, and Lag 3 periods).

Pooled Cross-Sectional Regression Results - Corporate ROA

Again, only those corporations which had downsized at

least by lO percent in a particular year are included for

this set of regressions, including the data for up to five

years after the downsizing occurred.

Over the three models run (one with all independent

variables of downsizing, diversification, capital intensity,

advertising intensity, R&D intensity, industry concentration,

industry growth, and industry profitability; one with R&D

intensity excluded; and one with both advertising intensity

and R&D intensity excluded), both current year downsizing

(control variable DS), and previous years downsizing (DSPl -

135

DSP5) are insignificant for corporate ROA in the year of the

downsizing, and all subsecjuent years measured, with two

exceptions. In the model with all variables, downsizing is

significant and negative for the concurrent equation, and in

the model with RDINT excluded, the current DS control

variable is significant and negative in the Lag 2 ecjuation.

The corporate ROA effects are different than those found for

SBU ROA. The negative effect is practically imperceptible;

in the comparable equations (the model with both RDINT and

ADVINT excluded) , downsizing has no effect on corporate ROA

in any year.

Other variables that are shown to have an impact on

corporate ROA include diversification (positive and very

small), capital intensity (usually negative and small, in

most concurrent and lagged ecjuations) ; R&D intensity

(negative); and industry profitability (positive).

In comparing the above SBU and corporate ROA analyses,

it appears that SBU's are more sensitive to downsizing than

are corporations, when controlling for many factors. Some

explanations for the apparent SBU ROA downsizing sensitivity

could include: (1) corporations do not downsize across SBU's

evenly, and SBU's have more market-specific activities; (2)

corporations may be able to recover faster than SBU's as they

can draw upon a more diffuse workforce enabling the overall

corporation to recoup and return to efficiency more rapidly a

single SBU; and (3) corporations can spread risk across

136

several SBU's, and the downsizing negative charges against

ROA are spread over the whole corporation, rather than a

particular SBU. Therefore, the following proposition is

presented:

Proposition 1

Strategic business unit ROA is more sensitive to downsizing

than is corporate ROA.

The study also shows that the negative impact of

downsizing on SBU ROA begins in the year of the downsizing,

then continues, but abates, for three years after the

downsizing took place, and then goes to insignificance for

the next two years.

Proposition 2

The impact of downsizing on SBU ROA is initially negative,

but the organization tends to recover to previous levels of

performance.

Proposition 3

SBU's with significant downsizing do not improve ROA

performance in the out years.

Differences in the impact of downsizing on ROA have been

found in this study, depending on the level of analysis.

137

whereby corporate ROA appears to be impervious (but not

improved either) to downsizing.

Proposition 4

Downsizing has minimal impact on corporate ROA, possibly due

to the impact being disproportionately absorbed by SBU's.

Pooled Cross-Sectional Regression Results - Corporate TQBQ

Over the three models run, downsizing is not significant

for corporate TOBQ in the year of the downsizing, nor for the

first year after downsizing. However, previous downsizing

has a positive effect on market value starting in the second

year (Lag 2) after the downsizing. In the equation with all

the variables, and the equation with RDINT excluded, the

previous downsizing variable is insignificant for Lag 1, is

positive in the second and third years after downsizing (Lag

2 and Lag 3), and is insignificant again for the fourth and

fifth years. In the comparable equations (the model with

both RDINT and ADVINT excluded) , the positive effect of

previous downsizing on market perceptions is the same as

above, but reappears in the fifth year after downsizing.

Other variables that are shown to have an impact on

corporate TOBQ include capital intensity (generally

positive); advertising intensity (negative in the concurrent

ecjuations, and positive in Lag 4 and Lag 5 equations);

138

industry concentration (positive for some concurrent

ecjuations, and negative for some lagged ecjuations); and

industry profitability (positive in the Lag 1 and Lag 5

ecjuations when both advertising intensity and R & D intensity

are excluded). The diversification variable is insignificant

throughout all the models, both concurrent and lagged

equations (except in one instance, where it is positive for

the Lag 4 ecjuation with all variables included) .

The above results show that there is little reaction

with respect to the Tobin's Q proxy, to corporate downsizing

within the year of the downsizing action. However, the

market value of downsized corporations seems to increase in

the years after the downsizing, starting in the second year.

One explanation could be that the market is slow to react,

given that it may take many months for a downsizing to

actually be implemented and be completed. The concurrent

year is the year the downsizing began, but may not be

completed. It often takes at least one full year or more to

downsize. Perhaps the market wants to see the downsizing

actually occur and what subsequently happens.

Proposition 5

Market reaction to downsizing, in terms of the Tobin's Q

proxy, appears to have a lagged effect.

139

Proposition 6

The reality of the downsizing (implementation) has a positive

effect on market value.

It is interesting that this study shows that SBU ROA is

negatively affected by downsizing (the negative effect

diminishes over time) ; and that corporate ROA is not affected

by downsizing, but corporate Tobin's Q is positively affected

by downsizing (albeit later in time).

Perhaps the market is looking for signs that a downsized

corporation is going to stabilize in performance.

Reconsidering the antecedents to downsizing/consequences of

downsizing puzzle, if a corporation had declining ROA's for

some years, then downsized and improved by arresting the

decline, the market may have positive perceptions.

Proposition 7

Lack of eroding ROA in downsized corporations leads to

increased market value.

Implications

Implications for Research

The results and conclusions of this study continue to

demonstrate the need to address performance implications when

examining strategic issues, such as downsizing. The study

shows that downsizing does have an effect on organizational

140

performance, even when controlling for other performance

related factors, in fact, the study shows that when

addressing organizational performance, many other factors

should be taken into account to better isolate the relative

effects of each factor, including downsizing.

The study also shows that it is important to address

performance not only in terms of accounting based measures,

but also in terms of market perceptions (for corporations) ,

as results may differ. This study shows that for

corporations that downsize at least by ten percent, the

accounting based measure ROA is not affected, but the hybrid

accounting/market measure Tobin's Q proxy, is positively

affected.

The study also affirms the value of examining strategic

issues over time, especially when looking at performance

after a strategic action such as downsizing. Concurrent

studies do not always reveal the entire picture; lagged

studies should be considered for many strategy research

issues. In this study, the value of lagged ecjuations is

clearly illustrated in showing the diminishing negative

effect of previous downsizing on SBU ROA, as well as the late

positive effect on corporate Tobin's Q, particularly in those

ecjuations where downsizing is represented as ten percent or

more.

Another issue for researchers in studying downsizing and

performance is the antecedent/consecjuence conundrum. More

141

research should be done on what precipitates the downsizing

action (e.g., past poor performance, changes in environment

or niche, organizational theory size issues, "new capitalism"

forces (Budros [1997], etc.), as well as what the

consequences are on future performance.

Another interesting facet brought up by this study is

the level of analysis issue for organizations. Again,

results may differ significantly depending on whether the

sample is composed of SBU's or corporations. This study

shows that SBU's are much more highly sensitive to downsizing

than corporations, in terms of ROA. The negative effect of

previous downsizing continues for three years for those SBU's

who have downsized ten percent or more, while the ROA for

corporations that have downsized at least ten percent is not

affected. More research is needed on the relationships,

similarities, and differences between corporations and

strategic business units, including how they may implement

and handle the consecjuences of downsizing.

It is also interesting to explore downsizing in terms of

strategic change, which has not always been done in the

strategy literature. Is downsizing a strategic response to

organizational decline? Does downsizing contribute to

organizational decline? Is downsizing truly part of the

process necessary for long term organizational improvements,

as Cascio (1993) suggests? This study shows that downsizing

effects persist for up to three years for SBU's, are almost

142

imperceptible for corporation in terms of ROA, and often do

not appear until the second year for corporations in terms of

market value.

Implications for Practice

This study helps to address the heretofore unresolved

cjuestion of whether downsizing has a positive or negative

effect on performance. The study shows that in general,

downsizing has a negative effect on SBU performance lasting

up to three years after the downsizing. An exception (a

small positive effect of downsizing) in the third year lagged

(time series only) equation without R&D intensity needs

further study, including the relationship between R&D

expenditures and downsizing. The study shows different

results with corporate performance, when measured as ROA, as

a very small negative effect for up to one year after the

downsizing, as in the time series regression, or no effect as

shown by the ten percent or more downsizers regression. When

corporate performance is measured as a Tobin's Q proxy (a

hybrid market/accounting measure), downsizing has a positive

effect that only appears by the second year (at least ten

percent downsizers only) or third year (time series) after

downsizing. The time series regressions show some early

negative effect of downsizing on Tobin's Q, but when those

corporations that downsized by ten percent or more are

examined, there are no negative effects seen.

143

One possible explanation for the initial (and

continuing, in the case of SBU's) negative impact on ROA is

that the accounting charges associated with downsizing

actions (e.g., severance packages, outplacement counseling,

early retirement incentives, etc.) are reflected in the year

of the downsizing, and sometimes for another year or longer.

It is interesting that the negative effect of downsizing on

ROA does abate, but that no positive effect is seen, even

after five years in the ten percent or more downsizer

ecjuations.

The downsizing effect on the Tobin's Q proxy is

positive for those corporations that downsized ten percent or

more, but only appearing two years after the downsizing. One

explanation may be that initially the market perceives that

the corporation is in trouble and therefore is downsizing.

(In the time series regression, a concurrent year negative

effect disappears in the year after downsizing.) Then later,

after the market sees how the organization copes with the

downsizing, it then rewards the downsized corporation.

Limitations and Strengths

Limitations

There are some limitations in this study arising from

the use of secondary data bases. For example, the researcher

cannot influence what types or forms of ciata are collected.

144

Some variables may not be available in the data base, and

there is little contextual information, it would be

interesting to know, for example, at what levels of the

organization was the downsizing being carried out.

Another limitation of the study is that is does not

attempt to test a set of hypotheses generated from the

existing literature, because of the lack of theory at this

time. However, the study contributes to theory building in

the nascent field of downsizing research, by testing a

tentative empirical model.

Another limitation is that only seven years of data were

available, so the effects of downsizing could be examined for

a period of up to only five years after the downsizing took

place.

Strengths

There are several strengths of the study. The study is

one of the first studies on the performance implications of

downsizing. This type of research on downsizing is currently

being called for in the organization science literature. It

is needed, not only in the academic world of scholarly

research, but also in the practitioner world of business.

This study attempts to answer the often asked question, "What

impact does downsizing have on the bottom line?" Strategy

researchers call for studies that investigate performance

relationships; businessmen and employees want to know if

145

downsizing will help their organizations, as conventional

wisdom tells them. The study should be of use to both

researchers and practitioners, in that it addresses an issue

that is of great importance and timeliness, not only to the

business world, but also to the national economy.

Another strength of this study is that it employs

multivariate analysis for the inclusion of many specific

variables that could have an impact on economic performance

of an organization. It uses a large multi-industry database,

which has a variety of organizations of different sizes.

The study also contributes in that it uses not only

corporate data, but also strategic business unit (SBU) data.

There is little research reported in the literature on

performance of SBU's, as SBU level data is often difficult to

obtain.

Directions for Future Research

The performance implications of downsizing need to be

studied further, both in breadth and depth. It would be

useful to have additional years of data to be able to follow

more strategic business units and corporations for a longer

period of time, especially to confirm the flex point of

performance change (i.e., the point at which the negative

effect of downsizing on SBU ROA washes out, and if it ever

goes to a positive effect).

14 6

The apparent disproportionate negative impact of

downsizing on SBU's vis-a-vis corporations should be examined

more closely. The SBU downsizing sensitivity issue would be

of interest to both researchers and those in industry.

The lagged positive effect of downsizing on the Tobin's

Q proxy is also interesting, and should be researched

further. Examining the relationship of market value and

downsizing, requires a closer look at what goes on in

antecedent years. The cjuestion of what primarily drives

downsizing is important.

Finally, this study puts forth some propositions which

may be useful in forming tentative hypotheses to be tested

with other data. The theoretical development of downsizing

and performance implications may be pushed forward.

147

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