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Cronin, Jerome Joseph, Jr.

THE RELATIVE IMPACT OF FINANCIAL STRUCTURE AND MARKETING PERFORMANCE ON RETAIL PROFITABILITY

The Ohio Stale University Pli.D. 1981

University Microfilms International 300 N. Zeeb Road, Ann Arbor, M l 46106

Copyright 1981 by Cronin, Jerome Joseph, Jr. All Rights Reserved NOTE TO USERS

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35

This reproduction is the best copy available

UMI • THE RELATIVE IMPACT OF FINANCIAL

STRUCTURE AND MARKETING PERFORMANCE

ON RETAIL PROFITABILITY

MSSERTATION

Presented in Partial F ulfillm ent of the Requirements fo r

the Degree Doctor of Philosophy in the Graduate

School of The Ohio State University

by

Jerome Joseph Cronin, J r., B.S., M.B.A.

The Ohio State University

1981

Reading Committee: Approved by

Bernard J. LaLonde

H. Lee Mathews

James F. Robeson

Advisor Department of Marketing To My Parents Ted and Edith ACKNOWLEDGEMENTS

In any project of this magnitude, the credit for any accomplish­

ments must be shared with a number of individuals. In itia lly , a debt of

gratitude is owed to my parents to whom this effort is dedicated. My

father and my late mother are largely responsible for instilling in me

a great appreciation for the pursuit of knowledge. Without their early

insistance upon the necessity of a thorough education, this dissertation

would not have been undertaken. For this and much more, I thank them.

In one's career, the advice of others is also a necessity. As my

advisor throughout the doctoral program, as well as the chairman of my

dissertation committee, Dr. Bernard J. LaLonde has contributed greatly to

the completion of my education. Many before myself have attempted to

describe the contributions of Dr. LaLonde, but his role in shaping the

careers of those working with him can not be adequately described nor

overestimated. However, I do believe his greatest contribution lies in h

dedication to assisting his students in achieving the maximum potential within th e ir chosen area.

Dr. LaLonde's totally unselfish giving of his time, along with his

ability to motivate a sometimes sluggish student, were of invaluable

assistance in the completion of this project. For this, as well as for

his guidance throughout ny program, I can not adequately express my grat­

itude to this absolute gentleman. I can only hope I am able to justify

his confidence. In addition, the contributions of several additional professors needs to be acknowledged. Dr. James Robeson and Dr. H. Lee Mathews generously gave of th e ir time to serve on the reading committee. Their excellent comments greatly strengthened the final draft of the dissertation. The contributions of Dr. John Grabner and Dr. Robert House to the early drafts of the dissertation are also gratefully acknowledged. Two other former professors also need to be recognized. Dr. Stanley Stough and Dr.

Walter Wilson in it ia lly suggested, even pushed, me towards,the pursuit of

Ph.D. during my association with them at the University of Dayton. Their foresight and confidence is appreciated.

During the data gathering portion of the project, Management Horizons of Columbus, Ohio graciously permitted me to access their data. Their assistance is also greatly appreciated. In addition the aid of the com­ pany's lib ra ria n , Ms. Yvonne Speece, is also graciously acknowledged.

The contribution of my fellow students to this dissertation can not be neglected. Kenneth Martel 1 assisted in the methodological design and computer analysis phases of the project. Without Ken's assistance I might s t i l l be in some computer room. Martha Cooper also aided me simply by being a cordial o ffic e neighbor and a w illin g sounding board for my ideas and a ll too frequent confusion.

The editorial and typing assistance of Ms. Marjorie King is also graciously acknowledged. Her s k ill, as well as her willingness to work under deadline pressures, significantly contributed to the completion of this project. In addition the assistance and support provided by Karen and my often neglected dog Travis are likewise appreciated.

Finally, my appreciation to my students and those who have worked as my graders needs to be expressed. Richard Brand and Julie O'Keefe's performance as graders allowed my attention to be directed towards this dissertation without slighting my students. Those students must also be thanked, because th e ir response to my efforts in the classroom have con­ sistently provided justification for the continuation and completion of this project.

v VITA

April 27, 1952 ...... Born - Springfield, Ohio

1974 ...... B.S., The Wright State University Dayton, Ohio

1974 - 1976 ...... Assistant to the Director of the MBA Program, The University of Dayton, Dayton, Ohio

1976 ...... Graduate Teaching Assistant, The University of Dayton Dayton, Ohio

1976 ...... M.B.A., The University of Dayton, Dayton, Ohio

1976 - 1977 ...... Account Manager, The Specialty Papers Company, Dayton, Ohio

1977 - 1981 ...... Graduate Research and Teaching Associate, The Ohio State University

1981 - present ...... Visiting Assistant Professor The Ohio State University

PUBLICATIONS

Jerome J. Cronin. "A Preliminary Investigation of the Impact of Financial Structure and Marketing Performance on P rofitability," Proceedings of the Tenth Annual Albert Haring Symposium, 1980.

Bernard J. LaLonde and Jerome J. Cronin. "The Winter of 1978: A Strategic Assessment," submitted fo r review, The Journal of Long Range Planning.

Bernard J. LaLonde and Jerome J. Cronin. "D istribution Career Patterns," Distribution Worldwide, March 1989.

Bernard J. LaLonde and Jerome J. Cronin. "Weathering Winter: What You Can Do," D istribution Worldwide, December 1978. vi Bernard J. LaLonde and Jerome J. Cronin. "Career Patterns in D istribution--Profi1e 1978," Proceedings of the National Council of Physical D istribution Management (Chicago, Illin o is ) , 1978.

FIELDS OF STUDY

Major Field: Marketing

Professors Robert Bartels, Bernard J. LaLonde, James L. Ginter, and Frederick D. Sturdivant

Minor Field: Business Logistics

Professors Bernard J. LaLonde, John R. Grabner, J r., and Robert G. House

Quantitative Methods and Research Tools

Professors Alan G. Sawyer, Thomas F. Nygren, and Robert C. Maccal1um TABLE OF CONTENTS Page ACKNOWLEDGEMENTS...... i i i

VITA...... vi

LIST OF TABLES...... xi i

LIST OF FIGURES...... xvi

Chapter

1. INTRODUCTION...... 1

The Statement of The Problem ...... 3 The Research Objectives ...... 7 The Specific Research Questions ...... 9 The Rationale For The Study ...... 10 The Scope of The Research ...... 12 The Research Hypotheses ...... 12 The Methodology ...... 14 Phase 1: Data C ollection ...... 14 Phase 2: The Investigation of The Data Base.. 15 Phase 3: The Data Analysis ...... 15 Phase 4: The Id e ntifica tion of TheResearch Models ...... 16 Phase 5: The Validation Procedure ...... 16 The Potential Limitations of The Research ...... 16 The Potential Contributions of The Research ...... 19 The Potential Contributions To Theory ...... 19 The Potential Contributions To Methodology ...... 20 The Potential Contributions To Practice ...... 20 The Organization of The Research ...... 21

2. A REVIEW OF THE LITERATURE...... 23

Introduction ...... 23 The Research Utilizing Financial Structure ...... 23 Variables As Predictors of Corporate Failure 24 A Summary of the Implications of the Financial Structure Literature ...... 40 The Research Investigating The Relationship Between Financial Structure and Corp^-ate Performance ...... 44 v i i i Page

A Review of The Literature Investigating The Impact of Marketing Performance and Other Variables on P ro fit Performance ...... 47 A Summary o f The Research E ffo rts ...... 52 The Theoretical Background of The Research ...... 58

3. THE RESEARCH METHODOLOGY...... 61

Introduction ...... 61 The Dependent Variable o f The Research ...... 63 The Independent Variables ...... 64 The Methodological Framework..., ...... 67 Phase 1: The Data Collection ...... 67 Phase 2: The Investigation of The Data Base,... 70 Phase 3: Data Analysis ...... 73 Phase 4: Id e n tifica tio n of The Research Models. 87 Phase 5: The Validation Procedure ...... 88 The Specific Data Analysis Procedures ...... 89 M ultiple Regression Programs ...... 89 The Cross Validation Program ...... 89 Nonparametric S ta tis tic s ...... 90 The Data Sources ...... 90 The V a lid ity of The Data Sources ...... 92 The Treatment o f Data Irre g u la ritie s ...... 92 The Data Collection Framework ...... 93 A Summary...... 94

4. THE PRESENTATION OF THE RESEARCH FINDINGS...... 95

Introduction ...... 95 The Ide n tifica tio n of The Independent Variables 96 The Relative Impact of Financial Structure and Marketing Performance On P ro fita b ility ...... 103 The Relative Impact of The Six Subclassifications of Financial Structure and Marketing Performance On P ro fita b ility ...... 123 The Reliability of The Significant Predictor Variables Over Time ...... 134 The Reliability of The Significant Predictor Variables Over Firms of Differing Profit Performance ...... 138 The Reliability of The Significant Predictor Variables Across Pricing Strategies ...... 140 The Reliability of The Significant Predictor Variables Over D iffering Geographic Diversification Strategies ...... 143 The Reliability of The Significant Predictor Variables Over Line of Trade Strategies ...... 148 The Investigation of The Shape of The Relation­ ship Between The Significant Predictor Variables and Retail Profitability ...... 153 The Secondary Research Findings ...... 154 i x Page

5. THE SUMMARY AND CONCLUSIONS...... 158

Introduction ...... 158 A Summary of The Research ...... 158 The Research Findings ...... 164 The Identification of The Descriptive Model 180 The Identification of The Predictive Model 184 The Managerial Implications of the Research 188 The Implications of The Individual Measures Identified As Components of The Descriptive Model ...... 188 Net Sales Index, ...... 188 Total Debt To Net Worth ...... 190 Gross Margin To Net Sales ...... 191 Advertising Expenditures To Net Sales..., 192 Average Market Share ...... 195 Net Worth To Cash Flow Total L ia b ilitie s To Cash Flow ...... 196 The Implications of The Total Descriptive Model. 198 The Implications of The Predictive Model ...... 200 The Implications of The Secondary Research Finding ...... 201 A Concluding Note ...... 202 Suggestions For Future Research,.., ...... 204

APPENDICES

A. Correlation Matrix of All Sample Variables ...... 206

B. The Stepwise Analysis of The RelationshipBetween Marketing Performance Variables and Profitability., ...... 214

C. The Stepwise Analysis o f The Relationship Between Financial Structure Variables, and Profitability ...... 216

D. The Stepwise Analysis of The Relationship Between Financial Structure Variables,, and Profitability ...... 219

E. Stepwise Analysis of All 21 Variables ...... 221

F. Significance Tests of Regression C oefficients ...... 245

G. The Pairwise Hierarchical Analysis of The Six Variable Subclassifications ...... 247

H. The Complete Hierarchical Comparison of The Six Variable Subclassifications ...... 249

x Page

I. Sample Variations Based On Managerial Strategy ...... 250

J. Analysis of The Residual P lots ...... 251

K. The Firms Included in The Research Sample ...... 259

L. The Classification Test Analysis of The Predictor Model ...... 260

M. Data Provided By The COMPUSTAT F ile s ...... 261

N. The Stepwise Analysis of The S ignificant Predictor P ro file ...... 265

0. The Stepwise Analysis of The Significant Predictor P rofile With Current Debt To Cash Flow Substituted For Total Debt To Cash Flow ...... 275

P. The Mean Values For The Twenty-One Research Variables ...... 285

Q. A Glossary of Terms Used in The Research ...... 289

BIBLIOGRAPHY...... 294

xi LIST OF TABLES

Table Page

1-1 The Research D efin itio ns ...... 2

2-1 L ist of Ratios Tested By Beaver ...... 28

2-2 The Significant Discriminating Factors Identified By Altman and Loris ...... 30

2-3 The Variables Investigated By Altman, Et A1 ...... 32

2-4 Altman, Et A l's 7-Variable Model Components ...... 33

2-5 Comparative C lassification Accuracy Between The Zeta Model and Various Forms of A Prior Bankruptcy Model.. 34

2-6 The Financial Performance Indicators U tilized By Sharma and Mahajan ...... 36

2-7 Financial Ratios Investigated By Bazley ...... 37

2-8 The Thirty-Two Financial Ratio Variables Utilized By Norton And Smith ...... 39

2-9 Financial Ratios Tested As Predictor Variables By Elam...... 42

2-10 Variables Identified By Researchers As Predictors Of Bankruptcy ...... 43

2-11 Summary Of Variables Found Useful In The Prediction Of Bankruptcy ...... 45

2-12 The General Economic Conditions Variables Investigated By Gray ...... 48

2-13 The Variables Investigated By Gray's Descriptive Research ...... 51

2-14 Independent Variables U tilized By Schoeffler, Buzzell, and Heany ...... 53

xi i Page Table

2-15 A List of Potential Financial Structures And Marketing Performance Variables For Investigation By The Present Research ...... 55,56,57

3-1 The Data Organization Format ...... ,. 69

4-1 The Multicoll inearity Analysis: Step 1 ...... 101

4-2 The Independent Variables Included A fter The Investigation Of The Data Base ...... 102

4-3 The Variables Included In The Marketing Performance And Financial Structure Classifications ...... 105

4-4 The Order Of Entry Of The Marketing Performance Variables In The Stepwise Regression Analysis 106

4-5 The Stepwise Analysis Of The Relationship Between Marketing Performance Variables And Profitability ...... 107

4-6 The Order Of Entry Of The Financial Structure Variables^ In The Stepwise Regression A n a lysis,,.. 110

4-7 The Stepwise Analysis Of The Relationship Between Financial Structure^ Variables and Profitability.. HI

4-8 The Observed Relationship Between Financial Structure^ Variables And P rofitability ...... 112

4-9 The Order Of Entry Of The Financial Structurep Variables In The Stepwise Regression Analysis 113

4-10 The Observed Relationship Between Financial Structure^ Variables And P rofitability ...... HA

4-11 The Stepwise Analysis Of The Relationship Between Financial Structure^ Variables And Profitability.. 115

4-12 The Significant Precictors Identified When All The Research Variables Are Considered Simultaneously.. 120

4-13 The Amount Of Incremental Variance Explained By The Significant Financial Structural And Marketing Performance Variables...... 121,122

4-14 The Hierarchical Comparison Of The Financial Structure And Marketing Performance Variables 124

xi i i T u i paqe Table

4-15 The Amount Of Incremental Variance Explained By Each Of The Six Subclassifications Of The Marketing Performance And Financial Structure Variables...... 126

4-16 The Order Of Entry Of The S ignificant Independent Variables In The Overall Stepwise M ultiple Regression Analysis ...... 127

4-17 The S ta tistica l Significance Of The Variance Explained By The Six Variable Subclassifications When A ll Are Considered Simultaneously ...... 128

4-18 The Total Variance Accounted For By Each Of The Six Variable Subclassifications When Considered Individually ...... 130

4-19 The S ta tistica l Significance Of The Variance Explained By The Six Subclassifications When Each Is Considered Separately ...... 131

4-20 The Hierarchical Analysis Of The Incremental Variance Explained By The Six Subclassifications. 133

4-21 The Significant Predictor Profile ...... 135

4-22 Predictor Reliability Over Time...... 136

4-23 A Summary Of The R e lia b ility Of The S ignifica nt.. Predictor Variables ...... 137

4-24 The Friedman Test Of Hypothesis 5 ...... 139

4-25 Predictor R e lia b ility Over P ro fita b ility Groups.. 141

4-26 The Friedman Analysis Of Hypothesis 6 ...... 142

4-27 Predictor Reliability Over Pricing Strategies.... 144

4-28 The Friedman Analysis Of The Pricing Strategy Portion Of Hypothesis 7 ...... 145

4-29 Predictor R e lia b ility Over Geography ...... 147

4-30 The Friedman Analysis Of The Geographic D iversification Portion Of Hypothesis 7 ...... 149

4-31 Predictor Reliability Over Lines Of T>ade ...... 151

xiv Table Page

4-32 The Friedman Analysis Of The Effects Of Differing Line Of Trade Strategies On Hypothesis 7 ...... 152

4-33 An Analysis Of The Beta Weights Of The Significant Predictor Profile ...... 153

5-1 A Summary Of The Research Findings ...... 161,162,163

5-2 The Significant Predictor Profile Variables 182

5-3 The Predictive Model Identification Analysis.... 186

xv LIST or FIGURES

Figure Page

1-1 The P ro fit Planning Process ...... 4

1-2 A Representation Of The Impact Of Retail Decisions On P ro fit Performance ...... 6

2-1 The Interrelationships Which Exist Amongst And Between The Research Variable Subclassifications And Profit Performance...... 59

3-1 The Methodological Framework ...... 68

3-2 An Illustration Of The Correlation Matrix In Phase 2 ...... 71

3-3 The Wilcoxon Test Design For Hypothesis 3 ......

3-4 The Calculation Of The Incremental Variance Explained By Each Variable Subclassification 77

3-5 The Three Time Segments ...... 80

3-6 The Significant Predictor Profile ...... 82

3-7 The Friedman Test Design For Investigating Hypothesis 5 ...... 83

3-8 The Friedman Test Design For Investigating Hypothesis 6 ...... *...... 84

3-9 The Friedman Test Design Employed In The Analysis of Hypothesis 7 ...... 86

5-1 A Ballantine Portrayal Of Incremental Variance... 168

5-2 The Incremental Variance Explained By The Significant Predictor Variables ...... 187

5-3 The Cumulative Variance Explained By The Significant Predictor Variables ...... 187

xvi CHAPTER 1

INTRODUCTION

Today's merchandising manager has a number of responsibilities which have been summarized as ensuring that the rig h t merchandise is available at the right place, at the right time, in the right quantities, and at the right price.1 The profitability requirements imposed by the necessity to maintain an attractive rate of return for a company's own­ ers demands that individual firms always pay particular attention to any measures related to the p ro fit performance of the firm . However, increasingly high rates of inflation, rising interest rates, and declin­ ing labor productivity presently are making the realization of profit­ ability goals exceedingly difficult. In addition, the same factors appear to be creating higher expectations among investors thereby mag­ nifying the problems of managers in designing marketing strategies to meet the profitability goals of stockholders.

Because most companies are operating with budget constraints, along with the aforementioned other hinderances to p r o fita b ility , a careful consideration of a ll managerial actions seems necessary in order to allow for the timely identification of problems as well as the strate­ gies to hopefully correct them. While previous studies have suggested

10oseph B. Mason and Norris L. Mayer, Modern Retailing: Theory and Practice (Dallas, Texas: Business Publications, Inc., 1978), p. 320. that one of the areas with the greatest potential for increasing profit performance lie s in the interaction between financial management and marketing management, the literature has neglected to thoroughly con­ sider how decisions made in the two areas affect company p ro fita b ility.2

In particular, the relationship between what can be called financial structure decisions and marketing performance decisions and p r o fit­ ability has been neglected. For the purpose of the present research, the two decision areas are defined as presented in Table 1-1.

Table 1-1

The Research D efinitions

Variable Classification D efinition

1. Financial Structure measures which indicate how the funds or assets of a company are employed A. Financial Management measures which re fle c t a firm 's debt structure B. Liquidity Management measures which show how adequately a firm 's indebtedness is covered by its assets C. Cash Flow Management measures which reveal the efficiency with which a firm meets its need for cash D. Asset Management measures which denote how the funds of a particular company are invested 2. Marketing Performance measures which indicate how the e ffo rts of a company's marketing group are directed A. Margin Management measures which indicate the price mark­ up over cost for a firm's product mix B. Marketing Management measures which reveal how the marketing group structures the firm's distribu­ tion, promotion, and product strategies C. Asset Management* measures which re fle c t how the funds of a particular company are invested

?This is documented in Chapter 2, pp.24-40.

*Asset Management is included in both classificatio ns because each has an asset dimension; fo r example, assets impact the amount of invest­ ment in firm and also reflect such marketind decisions as the number of sales outlets employed. Thus, only six subclassifications exist. The basic intent of the present research is to determine the

strength of the relationship between each of the two major categories, as

well as the six component subclassifications which are liste d in Table

1-1, with re ta il p ro fit performance. With that intent in mind, Chapter 1

now continues with a consideration of the general problem to be investi­

gated.

Statement of The Problem

Business firms make a number of decisions which can have a direct

impact on th e ir a b ility to earn a p ro fit. The decision to purchase

additional assets, to utilize more debt, to increase margins, or to make

additional promotional expenditures are common examples. While each of

the decisions can represent a strategy to achieve some specific goal for

a particular business unit within the firm ,3 the ultimate objective is to

earn additional p ro fits fo r the company. Because each of these decisions

compete fo r scarce resources within the firm ,1* companies must plan how

p ro fits are to be achieved. Figure 1-1 represents the typical process which a re ta il firm might employ in order to achieve the p ro fit goals

set fo r the company.

Top management firs t has to determine an appropriate profitability

goal for the fim;, which is usually expressed as a target rate of return on the owners' investment in the company. The top management group also

Business units refers to what might be termed departments within larger companies; for instance, the marketing group or department.

‘'The resources referred to include ca p ita l, labor, management exper­ tise, facilities, and other similar assets. t TIME T A R G ERE T T U RON NNE W T O R T H iue -. h Pr i Pann Process Planning fit ro P The 1-1. Figure FINANCIAL• MANAGEMENT 1 . MA. R GMA I N N A GAS E• M S EMA E T N N T A G E M E N T CASH• FLOW LIQUIDITY- MANAGEMENT ~ r ~ ASSET PROFIT PLANNING MARKETING* MANAGEMENT -* RETURN ON TOTAL ASSETS < --- * f ——— M A N A G E M E N T A G NFINANCIAL MARGIN ------■< --- : M A N A G E M E N T- 4 5

has to make some decisions about the amount and sources of funds a v a il­ able for company projects, as well as decisions pertaining to the pur­ chase or sale of company assets, and pricing goals. These decisions are

then inputs in the top management's designation of the p ro fita b ility goals fo r the company's operating managers. Once the decisions are made,

the p ro fit performance of the company is dependent on the a b ility of the company's operational managers to meet the p ro fita b ility targets set fo r them by upper management.

The a b ility of the operating managers to achieve the p r o fita b ility goals set fo r them, is dependent on the individual managers' a b ility to manage the assets, financial resources, and marketing tools5 at their disposal. The basic problem for the operating manager is then to deter­ mine the re la tive impact of each of the decision areas noted in Figure

1-1 on p r o fita b ility , because every decision made by the manager can impact one or more of these areas. Figure 1-2 summarizes the basic probl em.

In essence, the basic problem to be investigated by the research can be identified by analyzing Figure 1-2. Every retail decision has a poten­ tial impact on a firm's internal strategies; however, not all the impacts are positively related to p ro fit performance. Therefore, i f management wishes to maximize the aspects of a re ta il decision which have a positive influence on profitability, a necessity seems to exist to segment the basic decision into managerial areas. Figure 1-2 id e n tifie s financial structure and marketing performance as two of the basic areas which are

influenced by the decisions made by re ta il managers.

Marketing tools include both price (margin management) and such o th e r strategic variables as advertising and the determination of an -'jr'De1'’ o* sa^es outlets. 6

EXTERNAL ENVIRONMENT

FINANCIAL STRUCTURE . FINANCIAL STRUCTURE • LIQUIDITY MANAGEMENT • CASH FLOW MANAGEMENT • ASSET MANAGEMENT

MARKETING PERFORMANCE - MARGIN MANAGEMENT . MARKETING MANAGEMENT RETAIS PROFIT DECISION . ASSET MANAGEMENT PERFORMANCE

OTHER INTERNAL STRATEGIES . HUMAN RESOURCE MANAGEMENT • MANAGEMENT INFORMATION SYSTEMS

INTERNAL OPERATING STRATEGIES

Figure 1-2. A Representation of The Impact of Retail Decisions on P ro fit Performance Obviously, such external environmental factors as inflation, inter­

est rates, and the legal system also influence re ta il p ro fit performance,

but those are not factors which an individual company can manage. How­

ever, retail firms generally do have control over the company's finan­

cial and marketing strategies, so a means to identify the relative impact

each area has on p ro fita b ility would allow better decisions to be made.

Thus, the basic problem to be researched is the id e n tific a tio n of the

relationship which exists among financial strategies, marketing strate­

gies, and re ta il p ro fit performance. Using the basic problem as a guide­

line, the overall objectives of the present research are now identified.

The Research Objectives

The overall goal of the research is to identify the relationship which exists among financial strategies, marketing strategies, and profit

performance in order to determine i f specific areas w ithin a firm 's

strategic mix deserve greater attention in the consideration of alterna­ tive retail decisions. To satisfy the overall goal, the following series of more specific objectives are employed.

1. To identify the appropriate measures to represent each of the six variable subclassifications.6

2. To determine which of the two strategy variables, financial structure or marketing performance, has the greatest impact on re ta il p ro fit performance.

r’The six variable subclassifications are financial management, liq u id ity management, cash flow management, asset management, margin management, and marketing management. They represent subdivisions of the two major strategy variables Financial Structure and Marketing Performance. 8

3. To determine the re la tive impact which each of the six

decisions areas7 contained in the financial structure and marketing per­

formance classifications have on re ta il p r o fit performance.

4. To determine if the relationships between the predictor vari­

ables used to represent the strategic variable classifications and retail

p ro fit performance are stable over time.

5. To investigate the e ffe ct which variations in selected mana­

gerial strategies have on the ability of each of the variable classifi­

cations to predict re ta il p ro fit performance.

6. To develop a descriptive model which id e n tifie s the specific

financial structure and marketing performance variables which deserve

the particular attention of retail managers.

7. To identify a model to predict the profitability of retail firms

from the data collected thus allowing re ta il managers to predict, a

p rio ri, the possible profit impact of proposed changes in financial and marketing activities.

8. To investigate the nature of the relationship between the differ­ ent variable classifications and profitability.

9. To id e n tify areas fo r future research.

In order to promote an understanding of how the nine objectives are incorporated in the present research, the specific questions addressed by the study are now presented.

7The six decisions areas are represented by the six variable sub­ cla ssifica tio n s; financial management, liq u id ity management, cash flow management, asset management, margin management, and marketing management. The Specific Research Question

1. From the available financial and marketing information, what are the most appropriate variables to use to represent the six variable subclassifications?8

2. In general, are financial structure variables or marketing per­ formance variables9 more highly correlated with retail profit per­ formance?

3. Which of the six variable subclassifications have a signifi­ cant correlation with re ta il p ro fit performance?

4. Does the magnitude of the relationship between the six variable subclassifications and profitability differ over time, pricing strate­ gies, geographic d ive rsifica tio n strategies, or lin e of trade strate­ gies?1 0

5. Can a p ro file or composite of financial structure and marketing performance variables, which is s ig n ific a n tly correlated with p r o fit­ ability and stable over time, be identified?

6. Does the relationship between the individual measures which are used to represent the six variable subclassifications and profitability assume an identifiable pattern?

The question of why the research being undertaken is important is now addressed in the rationale for the study.

8The six variable classifications are identified in footnote seven.

'’The variables used to represent the various classifications are identified in Chapter 3, p.

10The exact specification of the strategies and the de fin itio n of the time measure are presented in Chapter Three, pp. 80-87. 10

The Rationale For The Study

The basic problem which this research attempts to investigate may be stated as follows: how can the decisions made by re ta il managers be broken down so that the impact of these decisions on p ro fit per­ formance can be better managed? , Ilf- Retail ers in the United States have experienced unsatisfactory pro­ f i t performance fo r the past th ir ty years.11 While one major re ta il consulting firm continually stresses the need for a 15.0 percent return on net worth a fte r taxes, the groups' own studies have found overall re ta il p r o fita b ility declining from 11.8 percent to 9.6 percent return on net worth during the five year period 1969-1974. Within the overall retail industry one finds similar problems for specific retail groups;

Discount Department Store companies declining from 14.2 percent to 5.2 per­ cent,12 Variety Store companies declining from 10.6 percent to 4.8 per­ cent,13 and Supermarket companies from 11.2 percent to 8.2 percent.14

The rationale behind the present study therefore lie s in the id e n ti­ fication of a methodological framework with the capacity to determine which of a firm s' financial structure and marketing performance variables are related to the a b ility of the company to be profitable. The importance of the present research is further magnified when one examines the impact which the re ta ilin g sector has on the American economy. Retail sales

“ Albert D. Bates, Ronald L. Ernst and Cyrus C. Wilson, P ro fit- ability, and Productivity Trends in Retailing (A Retail Intelligence Service Report of Management Horizons Inc., 1975), p. 1.

12 Ib id .

1 3 Ib id . , p. 31. i Ib id . , p. 33. 1'

alone consume some 55 percent of the disposable personal income of

American consumers.15 The 55 percent translates to 37.9 percent of the

Gross National Product of the United States.16

More specifically, the present research investigates the retail food

sector which alone accounts fo r 12.0 percent of the disposable personal

income of American consumers.17 The re ta il food sector also seems to be

quite competitive, thereby magnifying the need to identify any means

possible to improve managerial performance. In addition, the f i r s t para­

graph of the present section indicates that the recent profit perform­

ances of the re ta il food sector evidences a need fo r improved p r o fita b ility .

Thus, one can seemingly suggest that the re ta il sector in general,

and the retail food sector in particular, are significantly large

economic e n titie s whose declining performance ju s tify the attempt the

study makes to id e n tify possible strategies which can help overcome the

industry's profitability problems. In addition, the high rates of infla­

tion and soaring interest rates of recent periods are putting increased

pressure on the costs of nearly all firms; thereby, increasing the impor­

tance of the identification of a more efficient manner in which to employ

company resources.

15Adopted from The Survey of Current Business, (Volume 59m No. 6, Washington, D.C., The U.S. Government Printing O ffice, 1979), various pages.

16 Ib id . 17 Ibid. 12

The Scope of The Research

The purpose of the research is to examine the re la tive impact of financial and marketing strategy on p ro fit performance in the re ta il sector. However, only the retail food industry is considered. In addition, the only food companies included are those fo r whom secondary data is publically available. In essence, th is lim its the analysis to publically held corporations whose financial and marketing statistics are available from the COMPUSTAT Data Tapes, and The Retail Yearbook of

Management Horizons. 18

The Research Hypotheses

The hypotheses stated in the present section are primarily a basis for statistical testing and should not be interpreted as an indication of an a priori belief as to the direction of the possible significance of the effects of the variables. In addition, to predict which o f the finan­ cial structures or marketing performance variables w ill achieve s ig n ifi­ cance is a futile effort because of the inconclusiveness of the previous research in the area,18 and the introduction of new variables.

The hypotheses are now presented:

iaThese data sources are described in detail in Chapter Three in the section en title d The Data Sources.

iqThe inconclusiveness is due to the fact that the previous research e ffo rts which have attempted to study the determinants of p r o fita b ility have used d iffe re n t dependent variables and have not always incorporated any measures to determine significance. See Chapter 2, pp. 24-52. 13

H^: Variables which measure the performance of marketing

activities are not significant predictors of retail

profitability.20

Hg'- Variables which re fle c t how the funds or assets of

a firm are employed are not significant predictors

of retail profitability.

H3: Marketing Performance Variables are better pre­

dictors of retail profitability than Financial

Structure Variables.

H^: The amount of variance explained by the variables

usee' as surrogate measures for each of the six

variable types is not significantly greater than

zero.

H^: The profile of the significant predictor variables

is not stable over time. The profile is simply the

composite l i s t of the predictor variables achiev­

ing significance.

Hg: The profile of significant predictor variables

does not d iffe r between more and less profitable

firms.

H^: The profile of significant predictors does not

d iffe r between or among d iffe rin g managerial

70For the purposes of the present research p r o fita b ility is defined as the pre-tax rate of return on total assets. 14

strategies. The strategies investigated are

pricing, geographic d iv e rs ific a tio n , product

diversification, and line of trade.

H8: The relationship between the individual measures

found significant and profitability is linear.

The Methodology

The methodology consists of five stages: data collection, investi­ gation o f the data base, data analysis, id e n tific a tio n o f the research models, and validation. Each of these is discussed b rie fly 1n this section.

Phase 1: Data Collection

The purpose of the f ir s t phase o f the research is to generate the data necessary to undertake the Investigation. Four data sources are u tiliz e d ; the COMPUSTAT Data Tapes, The Retail Yearbook, the Securities and Exchange Commission 10-K Reports, and the Supermarket News'

D istribution Study o f Grocery Sales. A ll the sources are described in

Chapter 3, in the section e n title d The Data Sources. In addition, the th irty -th re e independent variables fo r which data is collected are also described in Chapter 3; in the section e n title d The Independent Variables.

The dependent variable is described in the same chapter under the title

The Dependent Variable. Data is collected fo r th irty -fiv e companies21 for each of the years 1969-1977.

2'Tl :h irty -fiv e companies are ide ntified in Appendix K. 15

Phase 2: The Investigation of The Data Base

The purpose of the second phase is twofold: f ir s t , to examine the data collected to determine i f the information has any s ta tis tic a l characteristics which invalidate the use of a regression based analysis; and second, to correct the id e n tifie d problems and in so doing to identify the variable set to be utilized in the research. First, the data is visually examined to determine If any spurious correlation exists due to commonalities between the independent and dependent measures.

Next, the Durbin-Watson Test is employed to ascertain the level of auto­ correlation which exists in the data base. F inally, a correlation matrix is generated to allow m u ltfco llin e a rity problems to be id e n tifie d and cor­ rected. A cross validation procedure is then used to determine the p re ci­

sion of the estimation of the regression coefficients which indicates the validity of regression analysis for the given data sample. At each step, any variable which jeopardizes the use of regression techniques is elim­ inated, thus allowing the final set of research variables to be identified.

Phase 3: The Data Analysis

A variety of techniques are used to test the research hypotheses, however, a ll are based on the results of regression procedures. The f ir s t two hypotheses u tiliz e the F-test. Hypothesis 3 uses the Wllcoxon Test and hierarchical regression. Hypothesis 4 employs the F-test and hier­ archical regression. Hypotheses 5, 6, and 7 require the F-test and the

Friedman Test, while hypothesis 8 relies on the analysis of the standard­ ized regression residuals and the residual plots of the standardized regression. 16

Phase 4: The Ide ntifica tion of The Research Models

Two models are ide ntified from the analysis of phase three. F irst,

a descriptive model is developed to id e n tify the variables which best

d iffe re n tia te p ro fit performance w ithin the research sample. The impact

of each of the measures on p ro fita b ility is examined. The second model

is a predictive to o l, again developed from the information provided in

phase three of the study. The objective is to develop an efficient tool

to predict the impact of strategy modificationson the p ro fit performance

of retailers.

Phase 5: The Validation Procedure

In phase 5 an attempt is made firs t to partially test the validity

of the overall findings by ascertaining their reliability over time and

a variety of managerial strategy variations. In addition, the v a lid ity

of the predictive model is examined through the use of a classification

test.

The Potential Limitations of The Research

There are several factors which serve to place lim its on the present

research. First, ^he restriction of the sample population to retail gro­ cery firms for the reasons explained in an earlier section effectively limits the interpretation of the research findings to similar companies.

The second limitation is also related to the selection of the data base.

The financial and marketing information required for the research is common data collected by most, i f not a ll, of the companies doing busi­ ness in the United States. However, taking a random sample is beyond the time and budgetary constraints of the project. Therefore, the sample 17

is lim ited to the companies which are common to the COMPUSTAT Data Tapes22

and The Retail Yearbook. 23 Because a ll the firms contained within the

retail foodstores' classification of the COMPUSTAT Data Tapes are inclu­

ded in The Retail Yearbook, the lim ita tio n becomes the COMPUSTAT source.

COMPUSTAT contains a sample of approximately 2700 publicly held

corporations drawn from a ll industries. The companies on the data base

are traded on the New York, American, and the over-the-counter stock mar­

kets. Basically, the re s tric tio n of the data base causes the generali-

zability of the study's findings to be limited to companies similar to

the ones found on the tapes. In general, publicly held corporations

are larger than the privately held counterparts so extrapolation of the

findings of the study to smaller firms must be scrutinized carefully.

COMPUSTAT also contains firms from widely dispersed geographic regions

so the sample is also subject to performance variations such as wage

rate differentials and labor force availability, which are peculiar to

individual regions.

Another lim ita tio n revolves around the shortcomings of accounting

data. Suggestions have been made that present accounting rules do not

allow the financial records of firms to adequately re fle c t the opera­

tional results of the firm .7,1 To the extent that such charges are

77The COMPUSTAT Data Tapes are a proprietary source of financial and marketing information on 2700 companies offered by the Standard Statis­ tics Co., Inc., which is a subsidiary of the Standard and Poor Corporation.

?7The Retail Yearbook is a proprietary service of Management Hori­ zons Inc., of Columbus, Ohio, containing selected marketing oriented information on approximately 520 re ta il firms doing business in the United States.

?,'See fo r example, "The P ro fit Illu s io n ," Business Week, March 19, 1979, pp. 108-112. accurate, the data used in the study is flawed. However, the more

serious problem with the accounting systems presently in use results

from differing practices between firms. Differing methods of recording

depreciation, capitalizing costs, and other practices all distort the comparability of the data collected from d iffe re n t companies. When com­ parisons are made between specific firm s, care should be taken in exam­

ining the sim ilarity of the companies accounting practices.

One further lim ita tio n of the proposed research which should be dealt with is related to the time span of the data base. All informa­ tion included in the data base w ill be drawn from the same periods. How ever each firm is not necessarily operating under a consistent pattern of external or internal pressures during the time span. The time span selected may cut across five or ten year development plans, changes in management or ownership, sig n ifica n t legal problems, or other poten­ t ia lly sig n ifica n t randomly occurring events. One problem, which is also related to the number of time periods the data base cuts across, is the influence which the general price level changes occurring over the years have on accounting costs. However, past research e ffo rts , which are reviewed in the second chapter,75 indicate that the problem does not sig n ifica n tly a ffect the performance of historical costs in prediction model s.

The problem of the data base cutting across d iffe re n t planning periods or being influenced by random internal or external events is controlled fo r in the research design, by analyzing the v a ria b ility of

’’See the Bazley, and the Norton and Smith a rticle s which are covered in the lite ra tu re review contained in Chapter 2. the individual variables across time periods, so the most serious lim i­ tation appears to be the selective interpretation of accounting practices made by individual corporations. The problem is d ifficu lt to deal with unless one makes an intensive investigation of all the accounting records of each firm included in the study. Such an effort is beyond the scope of the proposed research so all possible variations in accounting prac­ tices are considered random.

The Potential Contributions of The Research

The present research has the potential to make contributions to theory, methodology, and management. For theorists, the study provides a partial examination of the findings of the PIMS study and the strategic p ro fit model concept. In the area of methodology the study presents a means to u tiliz e secondary data in a regression based study incorporating many s ta tis tic a l safeguards including an attempt to validate the research findings. Finally, for practitioners the research offers several con­ tributions to the profitable management of retail operations.

Potential Contributions To Theory

While the PIMS study offered marketing a variety of managerial over­ a ll concepts, i t did so using a data base and procedures which are pro­ prietary. That study also concentrates on the manufacturing sector. The present research advances the earlier findings by providing a means to use publicly available data to consider the effectiveness of companies' mar­ keting efforts. In addition, the present study also concentrates on the re ta il sector thereby extending the application of the study of mar­ keting efforts to increase profitability to a new sector. 20

In addition, one advance the present study makes which has not been

presented in previous studies is the simultaneous study of the effects of

financial and marketing strategy. In so doing, the question of how much

interaction is necessary between the two management groups to ensure the

most e ffic ie n t p ro fit performance fo r the firm is addressed.

Potential Contributions To Methodology

The basic contribution made to methodology by the present study is not

the generation of a new methodological tool, but rather the incorpor­ ation of all appropriate statistical controls. This includes the care­ ful consideration of the characteristics of the data base, the incorpor­ ation of multiple measurement techniques, and the inclusion of relia­ b ility and validity considerations.

tPotential Contributions , To Practice

The ability to identify specific measures which reflect the effects

of the designation of managerial strategies while being related to p r o fit­ ability represents an important potential contribution to the management of re ta il operations. In addition, the simultaneous consideration of financial and marketing strategies allows a consideration to be made of the re la tive impact of each area. Such an analysis allows for the desig­ nation of the appropriate amount of resources to concentrate in the management of each area. The research design also allows the re la tive importance of the two strategy areas versus a ll the other impacts on profit performance identified in Figure 1-2 to be identified by comparing the portion of the total variance in p ro fita b ility explained by the two.

The present research also id e n tifie s two models which are of value to re ta il managers. F irs t, the descriptive model details the order of 2',

importance of the variables which re fle c t the d iffe re n t managerial

strategies in terms of their impact on profitability. The model also

reveals the magnitude and direction of the relationship of the variables with p ro fit performance. These findings allow re ta il managers to more

e ffic ie n tly allocate scarce resources.

A predictive model is also identified by the present research. The contribution offered by the second model is to allow the consideration of the impact of managerial strategy modifications on p ro fita b ility . In addition, the model can be used to id e n tify areas where strategy changes are necessary or to id e n tify the attractiveness of potential acquisition by similarly considering the impact of each alternative on profitability.

The Organization of The Research

The remainder of the study is presented in four chapters. Chapter

2 contains the lite ra tu re review which examines the previous research related to the subject at hand. In the chapter the previous research results are investigated in an effort to aid in the identification of the variables to be studied by the proposed research. The general finance and marketing literatures are also studied in order to offer additional assistance in the identification of variables for study.

Chapter 3 presents the research design. In the chapter the method­ ology used to investigate the hypotheses e a rlie r stated is presented along with an explanation of the data collection procedure. Chapter three also includes sections on the data analysis techniques, the research sample, the pretest, the v a lid ity controls and a detailed description of the data base which is u tiliz e d . 22

The findings of the research study are exhibited in Chapter four.

A discussion of all problems with the research methodology and all find­ ings which contradict the a p rio ri hypotheses are presented in depth. Any points of unexpected confusion or complexity are also examined.

Chapter 5 concludes the research report with a summary of the con­ clusions generated by the research. The contributions of the study's findings and the implications of the results for strategic decision making in re ta ilin g firms are presented as well. All suggestions fo r further research are also discussed in the fina l chapter.

The present research now continues with a consideration of the literature pertaining to the prediction of company profitability, success, and failure. CHAPTER 2

A REVIEW OF THE LITERATURE

Introduction

The second chapter reviews a number of articles from the financial

and marketing disciplines in order to assess the relevance held by

past research findings for the current project. Initially, the lite r­

ature which examines the relationship between financial indicators and

bankruptcy is observed. Even though the present research is designed to

investigate p r o fit performance, the studies have relevance because of the

identification of financial ratios which are significant predictors of

bankruptcy; which is simply a dichotomous p ro fita b ility measure re fle c t­

ing successful or unsuccessful p ro fit performance. Such studies, there­

fore, are of value in the id e n tifica tio n of potential independent v a ri­

ables for the present research. In addition, the few studies which use

p r o fita b ility as a dependent variable in the study of the importance of

financial indicators are also reviewed fo r the same reason.

A second major group of a rtic le s examined in Chapter 2 study the

relationship between marketing indicators and profitability. The impor­

tance held by the review of this group of studies is th e ir relevance to

the identification of the appropriate marketing predictor variables to be

incorporated into the present research.

Therefore, the purpose of Chapter 2 is twofold: f ir s t , to identify the variables to be incorporated into the study; and second, to examine

23 24 the methodological formats used in similar research projects in order to aid in the design of the present research. In order to accomplish the stated objectives, a number of research e ffo rts from the finance and mar­ keting lite ra tu re s are reviewed.

To aid in accomplishing the purpose of the chapter, and in order to add c la rity to the presentation, the remainder of the chapter is presented in the following sections:

1. The Research U tiliz in g Finance Structure As Predictors of Corporate Failure

2. The Implications Of The Financial Structure Literature For The Present Study

3. The Research Investigating The Relationship Between Financial Structure And Corporate Performance

4. A Review Of The Literature Investigating The Impact of Marketing Performance And Other Variables On P ro fit Performance

5. A Summary of The Research E fforts

6. The Theoretical Background of The Research

The Research U tiliz in g Financial Structure Variables As Predictors Of Corporate Failure

In the financial lite ra tu re several studies are found which utilize financial structure information as predictor variables in investigations of the determinants of the success or failure of indivi­ dual firms. One of the studies is based on a Ph.D. dissertation by 25

Sul1ivan.1

Sullivan's research attempts to em pirically test two hypotheses: first, that "powerful firms" utilize greater financial leverage than do

"less powerful firms," and second, that "powerful firms" are also more profitable. In testing the two hypotheses, Sullivan utilizes the follow­ ing definitions:

(1) profitability; measured by the ratio of net

in •'me to the book value of the stockholder's

equity.

(2) market power; measured by the market concen­

tration and entry barriers existent in a firm's

industry. The sales leader of an industry where

seventy percent or more of the total sales of

the industry are dependent on eight or fewer

firms is considered "powerful."

(3) financial leverage; measured by each firm's ratio

of total debt to total invested capital.2

Data fo r the study is taken from the COMPUSTAT Data Tapes, a pro­ prietary product of the Standard & Poor Corporation, and Moody's Indus­ t r ia l Manual.

timothy G. Sullivan, "Market Power, Profitability and Financial Leverage." Journal o f Finance, December 1974, pp. 1407-1414. 21bid., pp. 1407-1408. 26

Using an analysis of variance design incorporating ninety firms from thirty industries, Sullivan's study reveals that "more powerful" firms do employ less financial leverage and earn higher profits than do the "less powerful" counterparts.3 Sullivan's study thereby lends cre­ dence to the investigation of financial leverage as a determinant of corporate profitability. However, the implications of the study are limited by the fact that only one leverage variable is used. The ratio chosen for inclusion, the ratio of total debt to total invested capi­ ta l, is a very aggregate measure which reveals l i t t l e about the p ro file of the individual components of a firm's debt structure and nothing at a ll about the other aspects of financial structure such as cash flow, liquidity, and asset management. Therefore, several additional studies are now reviewed in order to help id e n tify additional variables which are appropriate for further investigation.

Most of the remaining lite ra tu re which examines the Impact of financial structure on corporate performance, investigates the u tility of such variables in the prediction of corporate failure. In essence, the studies which are examined in the remainder o f the present section use financial structure in the prediction of bankruptcy. The present research seeks to investigate the relationship between financial structure and p r o fita b ility rather than bankruptcy, but the necessity for examining such studies seems apparent as bankruptcy is simply a dichotomous index of p ro fita b ility . The f i r s t of the studies to be reviewed is the result

31bid., pp. 1408-1409. 27 of research performed by William Beaver.1* Beaver's research defines bankruptcy as the inability of a firm to pay financial obligations as the debts mature,5 then id e n tifie s a sample of seventy-nine failed firms which are classified according to industry and asset size for analysis. The classification scheme uses a three- digit number to indicate the company's principal line of activity. The system is based on the Standard Industrial C lassification (SIC) System developed by the Department of Commerce.5 Beaver also uses the identical criteria to select a sample of nonfailed firms for the purpose of com­ parison.

Five groups of financial ratios are then identified and incorporated into the research design in order to assess the ability of each variable to predict bankgruptcy. Beaver uses three criteria to select the vari­ ables to be investigated; (1) popularity, defined as frequent appearance in the lite ra tu re ; (2) that the ra tio has performed well in previous research efforts; and (3) all ratios have to be defined in terms of a cash flow concept. The ratios found to meet the criteria are listed in

Table 2-1.

Beaver's research methodology incorporates a cla s s ific a tio n test to determine the predictive accuracy of each of the variables, but no sig­ nificance tests are presented. However, the research does reveal that,

''William H. Beaver, "Financial Ratios as Predictors o f F ailure." Empirical Research in Accounting: Selected Studies 1966, Journal of Accounting Research,'Supplement V, 1^67, pp. 71-1111

51bid., p. 71.

bIb id . , p. 73. 28

TABLE Z - 1

LIST OF RATIOS TESTED BY BEAVER

GROUP 1 (CASH-FLOW RATIOS) GROUP V (LIQUID-ASSET TO CURRENT • 1. Cash flow to sales DEBT RATIOS) 2. Cash flow to total assets 1. Cash to current liabilities 3. Cash flow to net worth 2. Quick assets to current 4. Cash flow to total debt 1Iabil1t1es 3. Current raio (current assets GROUP I I (NET-INCOME RATIOS) to current liabilities) 1. Net Income to sales 2. Net income to to ta l assets GROUP VI (TURNOVER RATIOS 3. Net income to net worth 1. Cash to sales 4. Net income to total debt 2. Accounts receivable to sales GROUP I I I (DEBT TO TOTAL ASSET 3. Inventory to sales RATIOS) 4. Quick assets tQ sales 1. Current liabilities to total 5. Current assets to sales assets 6. Working capital to sales 2. Long-term 1ia b ilitie s to 7. Net worth to sales total assets 8. Total assets to sales 3. Current plus long-term l i a ­ 9. Cash Interval (cash to fund bilities to total assets expenditures for operations) 4. Current plus long-term plus 10. Defensive Interval (defen­ preferred stock to total sive assets to fund expendi­ assets tures for operations) 11. No-cred1t Interval (defen­ GROUP IV (LIQUID-ASSET TO TOTAL- sive assets minus current ASSET RATIOS) lia b ilitie s to fund expendi­ 1. Cash to total assets tures for operations) 2. Quick assets to total assets 3. Current assets to total assets 4. Working capital to total assets

Source: William H. Beaver, "Financial Ratios as Predictors o f Failure." Empirical Research in Accounting: Selected Studies, 1966, Journal o f Accounting Research, Supplement 77T5B7jrpT7H. 29 based on the highest percentage of correct cla ssifica tio n s, the cash flow-to-total debt ratio is the best predictor of bankruptcy followed by the net income-to-total assets ratio. Beaver also found that varia­ tions in the financial ratio variables are uncorrelated to asset size.8

A second study which investigates the ability of financial struc­ ture variables to predict bankruptcy is a 1976 publication by Altman and

Loris.9 From an unidentified total of twenty-four variables, the six ratios which are lis te d in Table 2-2 are id e n tifie d by the researchers as significant determinants of bankruptcy.10 Along with the inclusion of the significance test, a major contribution of the study appears to be the more detailed methodology.

First, Altman and Loris used several analytical tools including forward stepwise multiple regression, backward stepwise multiple regres­ sion, discriminant analysis, and a univariate F-test. The use of the m ultiple analytical tools helps ascertain whether the research findings are consistent with d iffe rin g s ta tis tic a l procedures and therefore more reliable. In addition, the authors examined the validity of the Research through the use of a predictive classification test and the Lachenburch te s t.11 The design thus aids in identifying a methodology fo r this study.

8Ib id ., p. 85.

9Edward I. Altman and Bettina Loris, "A Financial Early Warning System For Over-The-Counter Broker-Dealers," The Journal o f Finance, September 1976, pp. 1201-1217.

1 ° Ib id ., p. 1207.

w For an explanation of this test see P.A. Lachenburch, "An Almost Unbiased Method of Obtaining Confidence Intervals fo r the Probability of Misclassification in Discriminant Analysis." Biometrics, December 1967, pp. 639-645. 30

TABLE 2-2

THE SIGNIFICANT DISCRIMINATING FACTORS* IDENTIFIED BY ALTMAN AND LORIS

1. NET INCOME/TOTAL ASSETS

2. TOTAL LIABILITIES AND SUBORDINATE LOANS/OWNERS EQUITY

3. TOTAL ASSETS/ADJUSTED NET CAPITAL

4. ENDING CAPITAL — CAPITAL ADDITIONS/BEGINNING CAPITAL

5. SCALED AGE**

6. COMPOSITE***

*a ll variables were sig n ifica n t at the .001 leve^.by a univariate F-test. **length of experience in securities business ***a single variable composed o f ten separate dichotomous elements

Source: Edward I. Altman and Bettina Loris, "A Financial Early Warning System For Over-The-Counter Broker-Dealers." The Journal of Finance, September 1976, pp. 1207-1208. 31

Altman also participated in a second study which is very similar to the 1976 research. In the second study Altman and two co-authors again examine the ability of a number of financial structure variables to pre­ dict bankruptcy.12 Using a sample of fifty-three bankrupt and fifty - eight non-bankrupt manufacturers and re ta ile rs , the authors analyze the significance of each o f the twenty-eight variables lis te d in Table 2-3 and f nd that the seven liste d in Table 2-4 determine the most e ffic ie n t pre­ dictive model.13 The seven variable model is considered the most e ffi­ cient because no other combination of variables explains a significantly greater portion of the total variance of the relationship between the predictors and bankruptcy.

The research methodology employed includes both lin e a r and quadra­ tic discriminant analysis, a scaled vector test, a separation of means test, a conditional deletion test, and a univariate F-test. The research's r e lia b ility is again assessed by examining the consistency of the findings across the d iffe re n t analyses techniques.

The validation process incorporated in the study u tiliz e s the Lachen- 1 ** burch test plus a two and a five year holdout classification test. The procedures confirm the findings that (1) the linea r discriminant analysis is more appropriate than the quadratic model, and that (2) the new seven variable model is a more accurate predictor than Altman and Loris' 1968 model. Table 2-5 compares the two models.

12Edward I. Altman, Robert G. Hal deman, and P. Narayanan, "Zeta Analysis: A New Model to Identify Bankruptcy Risk of Corporation." Journal of Banking and Finance, Volume 29, Issue 54, 1977, pp. 29-54.

13Ib id . , pp. 34-35.

Ibid. 32

TABLE 2-3

THE VARIABLES INVESTIGATED BY ALTMAN, ET AL.

Variable

No.

(1) EBIT TA (2) NATC TC (3) Sales/FA (4) Sales AC (5) FBIT/Sales (6) NATC Sales (7) Log. tang, assets (8) Interest coverage (9) Log. no. (8) & 15 (10) Fixed charge coverage (11) Earnings debt (12) Earnings /5 y r. mats (13) Cash flow /fixed charges (14) Cash flow/TD (15 WC LTD (16) Current ratio (17) MC/total assets (18i WC/cash expenses (19) Ret. earn, to ta l assets (20) Book equity TC (21) MV equity TC (22) 5 yr. MV equity TC (23) MV equity total liabilities (24) Standard error of estimate of EBIT/TA (norm) (25) EBIT drop (26) Margin drop (27) Capital lease/total assets (28) Sales/fixed assets

Source: Edward I. Altman, Robert G. Haldeman, and P. Narayanan, "Zeta Analysis: A New Model to Ide ntify Bankruptcy Risk of Corporations." Journal of Banking and Finance, Volume 29, Issue 54; 1977, p. 54. 33

TABLE 2-4

ALTMAN, ET AL'S 7-VARIABLE MODEL COMPONENTS

X ,: Return on Assets measured by EBIT/total assets

X : Stability of Earnings measured by a normalised measure o f the standard error o f estimate around a ten year trend on return on assets

X3: Debt Service measured by EBIT/total in te re st payments

X*: Cumulative P r o fita b ility measured by retained earnings/total assets

X5: Llqulty measured by the current ra tio

X6: Capitalization measured by common e q u ity /to ta l capital

X?: Size measured by to ta l assets

Source: Edward I. Altman, Robert G. Haldeman, and P. Narayanan, "Zeta Analysis: A New Model to Identify Bankruptcy Risk of Corporations." Journal of Banking and Finance, Volume 29, Issue 54, 1977, p. 54. Table 2-5

Comprative classification accuracy between the ZETA model and various forms o f a p rio r bankruptcy model (in percent)

Years prior ZETA model Altman's 1968 model* 1968 model. ZETA 1968 variables, ZETA to ______sample para. ______bankruptcy bankrupt Non-bankrupt Bankrupt toon-bankrupt bankrupt Non-bankrupt Bankrupt Non-bankrupt (1) (2) (3) (4) (5) <6) (7) (8) (9)

1 96.2 89.7 93.9 93.0 «6.8 82.4 92.5 84.5

2 84.9 93.1 71.9 93.9 83.0 89.3 83.0 86.2

3 74.5 91.3 48.3 n.a. 70.6 91.4 72.7 89.7

4 68.1 89.7 28.6 n.a. 61.7 86.0 57.5 83.0

5 69.8 82.0 36.0 n.a. 55.8 86.2 44.2 82.1

*East cell entry reveals the percentage of correct classifications made by the particular model represented by that column when the data from 1 to 5 years previous to the occurrence of bankruptcy was used. The table allows Altman's various models to be compared.

Source: Edward I. Altman, tiobert G. Hal deman, and P. fiarayanan, *Zeta Analysis: A New Model to Identify Bankruptcy Risk o f Corporations.*' Journal o f 8anking and Finance, Volume 29, Issue 54; 1977, p. 54. 4*CO 36

TABLE 2-6

THE FINANCIAL PERFORMANCE INDICATORS UTILIZED BY SHARMA AND MAHAJAN

Profitabi1ity

1. Return on Assets:* Earnings Before Interest and Taxes/Total Assets

Leverage Ratios

2. Debt Service: Earnings Before Interest and Taxes/Interest

3. Cash Flow: Cash Flow/Total Debt

4. Capitalization: Market Value of Equity/Total Capital

Liq uidity Ratios

5. Current Ratio:* Current Assets/Current Liabilities

6. Cash Turnover: Net Sales/Cash

7. Receivables Turnover: Net Sales/Receivables

8. Inventory Turnover: Net Sales/Inventories

9. Sales Per Dollar Working Capital: Net Sales/(Current Assets - Current L ia b ilitie s )

Miscellaneous

10. Retained Earnings/Total Assets

11. Total Assets (in thousands of dollars) *Variables identified by a discriminant analysis as the important predictors of failure. Source: Subhash Sharma and Vi jay Mahajan, Early Warning Indicators of Business Failure," Journal of Marketing, Fall 1980, pp. 80-89. 37

TABLE 2-7

FINANCIAL RATIOS INVESTIGATED BY BAZLEY

General Category Specific Ratio

1. Cash Flow Ratio Cash Flow To Total DebtA 2. P ro fitab ility Ratio Net Income To Total Assets*

3. Short-Term Liquidity Ratio Current Ratio** 4. Long-Term Solvency Ratio Net Worth To Fixed Assets 5. Asset Liquidity Ratio Cash To Total Assets** 6. Capital Turnover Ratio Sales To Working Capital***

♦Identified as the best predictors of bankruptcy ♦♦Identified as the second best predictors of bankruptcy ♦♦♦Identified as the worst predictors of bankruptcy No significance values are included

Source: John D. Bazley, "An Examination of the A b ility o f Alternative Accounting Measurement Models to Predict F a ilu re." Review o f Business and Economic Research. F a ll, 1976, p. 37. 38 generates data unbiased by the general economic fluctuations which occur

in the marketplace.17

Using a h isto rica l cost model, an adjusted cost model, and a current cost model,18 Bazley's study finds that no significant differences exist

among the variables' performance under any of the three models although

the data does indicate that the historical cost model is slightly super­

io r. 19 The methodology Bazley uses to confirm the study's findings

includes a comparion of the mean values of the three simulations and a

series of dichotomous classification tests. In general, Bazley's efforts

confirm the findings of Beaver with regards to the predictive value of

the individual variables with one exception; that being the conclusion

that the net worth to fixed asset ratio is a good predictor of failure.20

Another study which confirms Bazley's finding that data adjusted

for general price level variables is no more accurate in the prediction

of bankruptcy than unadjusted histo rical costs is offered by Norton and

Smith.21 Using the th irty-tw o variables lis te d in Table 2-8, the study

employs discriminant analysis to reach the same conclusion as Bazley. In

addition, the Norton and Smith research also em pirically proves that the

number of variables which are utilized in a predictive model can be

17Ib id . , p. 32.

18Historical costs are unadjusted financial costs, adjusted costs are historical costs adjusted for general price level changes, and current costs are historical costs adjusted for specific price level changes.

19Bazley, p. 46

2 0 Ib id ., p. 43.

2C u rtis L. Norton and Ralph E. Smith, "A Comparison of General Price Level and H istorical Cost Financial Statements in the Prediction of Bank­ ruptcy." The Accounting Review, January 1975, pp. 25-43. 39

TABLE 2-8

THE THIRTY-TWO FINANCIAL RATIO VARIABLES UTILIZED BY NORTON AND SMITH

VARIABLE NUMBER VARIABLE DESCRIPTION

001 Current ra tio * 002 Cash to current liabilities* 003 Cash to total assets 004 Quick assets to current lia b ilitie s * 005 Quick assets to total assets 006 Current assets to total assets* 007 Working capital to total assets* 008 Cash flow to sales 009 Cash flow to total assets* 010 Cash flow to net worth 011 Cash flow to total lia b ilitie s * 012 Net Income to sales 013 Net Income to total assets* 014 Net Income to total lia b ilitie s * 015 Current lia b ilitie s to total assets* 016 Long-term lia b ilitie s to current assets 017 Total lia b ilitie s to total assets* 018 Total lia b ilitie s * preferred stock to total assets* 019 Net worth to total lia b ilitie s * 020 Net worth to long-term lia b ilitie s * 021 Net worth to fixed assets* 022 Net Income to net worth 023 Sales to cash* / 024 Sales to accounts receivable 025 Sales to Inventory 026 Sales to quick assets 027 Sales to current assets 028 Sales to working capital 029 Sales to total assets 030 Cash Interval* 031 Defensive Interval 032 Sales to net worth

♦Identified as significant predictors of bankruptcy. The significance level is 95 percent.

Source: Curtis L. Norton and Ralph E. Smith, "A Comparison of General Price Level and H istorical Cost Financial Statements in the Prediction of Bankruptcy." The Accounting Review, January 1979, p. 75. 40 substantially reduced by elim inating the variables which do not make a sig n ifica n t contribution to the explained variance without reducing the predictive significance of the overall model.22

One additional study which examines the effect of accounting assump­ tions on the predictive value of financial structure variables is authored by Elam.23 Employing the twenty-eight financial variables id e n tifie d in Table 2-9,Elam finds that adjusting the financial statements of the sample firms to reflect the capitalization of future lease pay­ ments does not improve the ability of the variables to predict bank­ ruptcy. Again, the historical accounting model is found to be at least as e ffic ie n t as any other model.

A Summary of the Implications of the Financial Structure Literature

Although most of the lite ra tu re ju s t reviewed pertains to the pre­ diction of bankruptcy rather than company profitability, the studies appear to contain some obvious suggestions as to the proper design of the present research. The suggestions relate mainly to the identifica­ tion of the appropriate variables for investigation and to the proper design o f the research methodology. The la tte r topic is discussed in depth as part of Chapter 3, so the present section is devoted to the ques­ tion of variable selection.

The variables which are id e n tifie d by each of the studies reviewed as sig n ifica n t predictors of corporate fa ilu re are liste d in Table 2-10

22Ib id ., pp. 78-84.

23Rick Elam, "The Effect of Lease Data on the Predictive A b ility of Financial Ratios." The Accounting Review, January 1975, pp. 25-43. TABLE 2-9

FINANCIAL RATIOS TESTED AS PREDICTOR VARIABLES BY ELAM*

SHORT-TERM LIQUIDITY RATIOS

1. Cash to current liabilities 2. Current assets to current liabilities 3. Current assets minus inventories to current liabilities.

CASH-FLOW RATIOS

4. Cash flow to sales 5. Cash flow to total assets 6. Cash flow to net worth 7. Cash flow to total liabilities

LONG-TERM SOLVENCY RATIOS

8. Net worth to total liabilities 9. Net worth to long-term liabilities 10. Net worth to fixed assets 11. Net operating profit to interest

SHORT-TERM CAPITAL PRODUCTIVITY RATIOS

12. Sales to inventory 13. Sales to accounts receivable 14. Sales to work capital 15. Sales to current assets minus inventories 15. Sales to cash.

PROFIT MARGIN RATIOS

17. Net operating profit to sales 18. Net profits to sales.

LONG-TERM CAPITAL PRODUCTIVITY RATIOS

19. Sales to fixed assets 20. Sales to total assets 21. Sales to net worth. TABLE 2-9 (continued)

RETURN ON INVESTMENT RATIOS

22. Net income to net worth 23. Net operating p ro fit to total assets 24. Net operating profit to total debt.

DEBT COVERAGE RATIOS

25. Current liabilities to total assets 26. Long-term liabilities to current assets 27. Current plus long-term liabilities to total assets 28. Current plus long-term liabilities plus preferred stock to total assets.

*The actual predictive significance of the variables is not tested; rather the e ffect of d iffe re n t accounting assumptions on the re la tive ability of the variables to predict bankruptcy is investigated.

Source: Rich Elam "The Effect of Lease Data on the Predictive A b ility of Financial Ratios." The Accounting Review, January 1975, p. 26. 43

TABLE 2-10

THE VARIABLES IDENTIFIED BY RESEARCHERS AS PREDICTORS OF BANKRUPTCY

BY SULLIVAN: Debt to Totai Invested Capital

BY BEAVER: Cash Flow to Total Debt Net Income to Total Assets Total Debt to Total Assets Working Capital to Total Assets Current Ratio to Total Assets No-credit Interval BY ALTMAN (1968): Working Capital to Total Assets* Retained Earnings to Total Assets* Earnings Before Interest and Taxes to Total Assets* Market Value o f Equity to Book Value of Total Debt* Sales to Total Assets

BY BAZLEY: Cash Flow to Total Liabilities Net Income to Total Assets Net Worth to Fixed Assets

BY ALTMAN AND LORIS: Net Income After Taxes to Total Assets Total L iabilities minus Subordinate Loans to Owners Equity Total Assets to Adjusted Net Capital Ending Capital—Capital Additions to Beginning Capital Scales Age Composite Variable BY ALTMAN, HALDEMAN, AND NARAYANAN Return on Assets Stability of Earnings (Standard Error of Estimate of EBIT/Total Assets) Debt Service (EBIT/Total Interest Payments) Cumulative P rofitability (Retained Earnings/Total Assets) Liquidity (Current Ratio) Capitalization (Common Equity/Total Capital) Size (Total Assets)

*Found significant by F-test at .001 level of significance. 44 according to the author(s). The variables id e n tifie d in Table 2-19 are then summarized in Table 2-11 into one composite l i s t so that a ll the v a ri­ ables designated at least once as one of the predictors of bankruptcy are liste d . The present research uses Table 2-11 as one basic guideline to the choice of variables for inclusion in the study.

The Research Investigating the Relationship Between FinanciaT Structure and Corporate Performance' ~

Several studies are found in the financial lite ra tu re which use financial structure variables in efforts to predict the profit per­

formance of corporations. The studies vary from proprietary services

to purely exploratory research e ffo rts . The Value Line Ratings consti-

tude one of the studies.21* Basically, the intent of the ratings is to

predict the value of a stock over the next twelve month period. To

accomplish the goal a time-series regression model u tiliz e s the current year's average stock price, the estimated dividends for the next year,

the estimated earnings per share, and a Market Sentiment index based on

a fifty stock average yield, as explanatory variables.

A second research effort, the Whitbeck-Kisor Model,25 predicts the

price-earnings ratio of common stocks using the anticipated earnings per

share growth rates, the standard deviation of a firm's earnings, and the expected dividend payout ration in a linear regression analysis. Using

21*For an explanation of the service see A. Bernhard, The Evaluation of Common Stocks (New York: Simon and Schuster, Inc., 195$}T

25V.S. Whitbeck and M. Kisor, J r., "A New Tool in Investment Decision-Making," Financial Analysts Journal, May-June 1963, pp. 55-62. 45

TABLE 2-11

SUMMARY OF VARIABLES FOUND USEFUL IN THE PREDICTION OF BANKRUPTCY

1. Scaled Age 2. No Credit Interval 3. Total Assets 5. Net Income to Total Assets 6. Working Capital to Total Assets 7. Total Debt to Total Assets 8. Current Ratio to Total Assets 9. Retained Earnings to Total Assets 10. Common Equity to Total Assets 11.( Earnings Before Interest and Taxes to Total Assets 12. Standard Error of Estimate of Earnings Before Interest and Taxes to Total Assets 13. Sales to Total Assets 14. Net Income A fte r Taxes to Total Assets 15. Net Worth to Fixed Assets 16. Market Value o f Equity to Book Value of Total Debt 17. Cash Flow to Total Liabilities 18. Total Liabilities — Subordinate Loans to Owners Equity 19. Ending Capital — Capital Adjustments to Beginning Capital 20. Total Assets to Adjusted Net Capital 21. Earnings Before Interest and Taxes to Total Interest Payments 22. Debt to Total Invested Capital

Note: Not a ll of the variables were subjected to significance te st by the authors using them. In addition, those subjected to such tests had varying levels of significance 46

the estimates of security analysts, the model is used to predict the mar­

ket prices of stocks in order to identify underpriced and overpriced

firms. The major problem with both the Value Line Ratings and the Whit-

beck-Kisor research, in relationship to the present research, is the

reliance on predictions or educated guesses as explanatory variables.

The present study uses existing secondary data as independent variables,

so the relationship between the studies is minimized.

The Gordon Model26 represents a third research effort. Basically

the research predicts the value o f a share of stock using a firm 's

current earnings, the company's retention or reinvestment rate, the rate

of return on investments, and the rate of return required by investors.

A fourth study is offered by Malkiel.27 The Malkiel Model attempts

to predict companies' price earning ratios using the earnings growth

rate and the dividend payout ra tio .

Summarizing, each of the studies utilizes anticipated or projected earnings as a major explanatory variable in the estimation of corporate earnings or stock prices. The problem with the approach is that the

accuracy of such models is dependent upon the accuracy o f the predictions

of experts. Reliance on predictions in a predictive model appears ques­

tionable to the present researcher because the models are reliant upon

personal estimations rather than actual performance data. In addition,

the managerial relevance appears reduced because managers seem to have

26M.J. Gordon, The Investment, Financing and Valuation of the Cor­ poration (Homewood, Illin o is : Ricnard D. Irw in, In c., 1962), Chapter 4.

27B.G. Malkiel, "Equity Yields, Growth, and the Structure of Share Prices," American Economic Review, December 1963, pp. 1004-1031. 47 less control over expert's predictions than over the composition of actual performance variables.

The study now directs attention to the marketing oriented studies directed towards the question of how marketing and financial management affect p ro fit performance.

A Review of The Literature Investigating The Impact o f Marketing Performance and Other Variables on P ro fit Performance

One study which incorporates a number of types of variables is the result o f the Masters Thesis of C. Jackson Gray.28 Gray presents a descriptive analysis of the behavior of liquidity ratios, leverage ratios, productivity ratios, and profitability ratios in the retailing and whole­ saling sectors between 1950 and 1968. In addition, an attempt is made to establish a predictive relationship between the variables listed in Ta­ ble 2-12.and profitability as measured by the rate of return on net worth.

In the attempt to establish a lin k between the various environmental variables listed in Table 2-12 and profitability, two dependent variables are incorporated into a stepwise m ultiple regression analysis; aggregate net profits after taxes and the rate of return on net worth.29 All the variables in each case are found to contribute s ig n ific a n tly to the prediction of p r o fita b ility , but the author does suggest that a

29C. Jackson Gray, " P ro fita b ility Trends and P ro fita b ility Relation­ ships in the Retailing and Wholesaling Sectors of the Economy: A Pre­ liminary Investigation." An Unpublished Masters Thesis, The Ohio State University, 1971. 2 9Ib id ., p. 67. TABLE 2-12

THE GENERAL ECONOMIC CONDITIONS VARIABLES INVESTIGATED BY GRAY*

1. GROSS NATIONAL PRODUCT

2. DISPOSABLE PERSONAL INCOME

3. PERSONAL CONSUMPTION EXPENDITURES

4. RETAIL SALES

5. WHOLESALE SALES

6. WHOLESALE PRICE INDEX

7. INTEREST RATE

8. WAGE RATE IN RETAILING

9. WAGE RATE IN WHOLESAING

*A11 variables were found to make a significant incremental contribution to explaining the variance in p ro fita b ility at the 95 percent prob­ ability level.

Source: C. Jackson Gray," P ro fita b ility Trends and P ro fita b ility Relationships In the Retailing and Wholesaling Sectors of the Economy: A Preliminary Investigation," An Unpublished Master Thesis, The Ohio State U niversity, 1975, p. 66. 45

potential multicollinearity problem does confound the findings.30 In

essence, the study appears to have both m ulticoll inearity and auto cor­

relation problems which effe ctive ly invalidate the attempt to develop a

predictive model. However, the descriptive assessment of the movement of

the various ratios over time does appear to have value in the identifica­

tion of independent variables fo r the present study. Table 2-13 id e n ti­ fies the variables which are investigated by Gray.

A second study which investigates the relationship between various marketing variables and p ro fit performance is the PIMS study.31 PIMS, which is an acronym fo r the P ro fit Impact of Marketing Strategies, attempts to establish the relationship between strategic planning and p ro fit performance using a sample of 100 participating corporations who operate over 1000 businesses.32 Using thirty-seven predictor variables, the study is able to explain more than eighty percent of the variation in the dependent measure which is each company's pretax return on average investment.3 3

Basically, the PIMS research identifies the six major classes of profitability influences which are identified below:

Industry/market environment . Competitive Position • D ifferentia tion from competitors

30Ib id . , pp. 65-70.

31A synopsis of the study is offered in the following a rtic le : Sidney Schaeffler, Robert D. Buzzell, and Donald F. Heany," Impact of Strategic Planning On P ro fit Performance," Harvard Business Review, March-April, 1974, pp. 137-145.

3?The Strategic Planning In s titu te , Some Research Findings, 1977, p. 4.

3 30p. Ci t . , Schaeffler, Buzzell, and Heany," pp. 137-140. 50

Capital structure • Production process Budget a llo ca tio n s3*4

Within each of the six classes key profitability determinants are also identified through the analysis of cross tabulations. These key p r o fita b ility determinants are id e n tifie d below:

Industry/Market Environmental

- Long-run industry growth rate - Short-run market growth rate - Stage in 1ike cycle

. Competitive Position

- Market share

- Relative market share (your share t share of your big 3 competitors)

. Differentiation From Competitors

- Quality - Relative price - New product introductions

Capital Structure

- Investment in te n sity - Fixed capital intensity

Production Process

- Vertical integration

Budget Allocation

- R&D/sales - Marketing/sales

The PIMS research carries several implications for the present study.

First, PIMS suggest that higher marketing expenditures and higher

3"Op. C jt . , Strategic Planning In s titu te , pp. 11-70. TABLE 2-13

THE VARIABLES INVESTIGATED BY GRAY'S DESCRIPTIVE RESEARCH

LIQUITY/LEVERAGE RATIOS

1a • Current Assets to Current Liabilities 2. Inventory to Net working Capital 3. Current Liabilities to Inventory 4. Current Liabilities to Tangible Net Worth 5. Total Liabilities to Tangible Net Worth 6. Fixed Assets to Tangible Net Worth 7. Total Assets to Tangible Net Worth

PRODUCTIVITY RATIOS

1. Net Sales to Accounts Receivables 2. Net Sales to Inventory 3. Net Sales to Net Working Capital 4. Net Sales to Tangible Net Worth 5. Net Sales to Total Assets

PROFITABILITY RATIOS

1. Net P ro fits A fte r Taxes to Net Sales 2. Net P rofits A fter Taxes to Net Working Capital 3. Net P rofits A fter Taxes to Tangible Net Worth 4. Net P rofits A fter Taxes to Total Assets

Source: C. Jackson Gray, " P ro fita b ility Trends and P ro fita b ility Relationships In the Retailing and Wholesaling Sectors of the Economy: A Preliminary Inveatigation." An Unpublished Master Thesis, The Ohio State University, 1971, pp. 124-129. 52

investment intensity both carry negative impacts on p ro fita b ility .

Second, according to PIMS, increases in market share lead to increases

in p r o fita b ility . F inally, PIMS suggest that sales growth aids p ro fit

performance. These three findings indicate the financial structure and

marketing performance may be related to p ro fit performance and therefore

are deserving of research.

However, the PIMS lite ra tu re reveals two main weaknesses in terms of

the possible contribution to the present effort. First, the thirty-

seven variables are not all identified and some also seem ill-defined.35

For instance, the Schaeffler et al. a rtic le only deals with the six va ri­ ables ide ntified in Table 2-14. In addition, significance test values are not provided.

A Summary of The Research Efforts

The literature review just completed indicates the relative lack of empirical investigations which investigate the impact which strategic

financial and marketing decisions have on the profitability of merchan­

dising firms. Often the two sets of variables appear in fact to be

interrelated because of the financial changes which might be instigated

by a decision, fo r example, to extend a product line or to increase the

promotional budget. Likewise a revision in the financial strategy of

3sFor example the d e fin itio n used in the PIMS study fo r product quality demands the subjective judgment of managers as to the quality of their own products as compared to their competitors; an additional example is the exact d e fin itio n of market share, a subject which has been a continuing marketing problem. 53

TABLE 2-14

INDEPENDENT VARIABLES UTILIZED bY SCHAEFFLER, BUZZELL, AND HEANY*

MARKET SHARE: ratio of dollar sales by a business to total sales by a ll competitors 1n the same market during the same time period. Market Is defined to Include a ll of the products or servicesi customer types, and geographic areas that are directly related to the activities of the business.

PRODUCT(SERVICE) QUALITY: based on estimates of management as to percentage of th e ir sales which represent products superior to those of their competitors.

MARKETING EXPENDITURES: total costs for the business' sales force, promotion, marketing research, and marketing administration.

R*0 EXPENDITURES: total costs of product development and process improve* ment. Including a ll costs Incurred by corporate-level units which can be attributed directly to Individual and business.

INVESTMENT INTENSITY: ra tio of total Investment tc sales

CORPORATE DIVERSITY: an Index reflecting (1) the number of 4 -d lg lt Standard Industrial Classification Industries In which an Indi­ vidual business operates, (2) the percentage of each business' total employment 1n each Industry, and (3) the degree of s im ilarity among the Industries In whlcty the business operates

♦Significance test values not provided

Source: Sidney Schaeffler, Robert D. Buzzell, and Donald F. Heany, "Impact of Strategic Planning On P ro fit Performance." Harvard Business Review. p. 140. S4 the firm, such as a decision to restrict debt financing, can affect such marketing strategies as the magnitude of the monetary commitment to advertising, f a c ility expansirr. and development, or new product develop­ ment =

With such relationships in mind, the present research is designed to investigate the significance of a number of financial and marketing variables simultaneously. The marketing performance variables chosen fo r the study are drawn largely from the research performed by Gray36 and the PIMS study.37 The financial structure variables are drawn mainly from the financial studies reviewed e a rlie r in the chapter. The v a ri­ ables which comprise the in it ia l l i s t of measures to be examined is con­ tained in Table2-15. A fter a data reduction procedure is used to isolate the variables having the greatest impact on p ro fit performance, the sig­ n ific a n t variables are ind ivid ually analyzed.

Two additional conclusions are also reached from the lite ra tu re review. The f i r s t is that the histo rical cost assumption normally used in corporate accounting systems performs as well as any adjusted data could.38 In addition, in order to make the study as managerially use­ ful as possible, an emphasis is placed on selecting variables for study which can be manipulated or controlled by individual managers.39

36See footnote 28.

37See footnote 31.

38See the review of the Financial Structure lite ra tu re presented in this chapter.

33The v a lid ity of such an approach is suggested by L. Gayle Rayburn, "Accounting Tools in the Analysis and Control of Marketing Performance," Industrial Marketing Management, Volume 6, 1975, p. 75. TABLE 2-15

A LIST OF POTENTIAL FINANCIAL STRUCTURE ANDMARKETING PERFORMANCE VARIABLES FOR INVESTIGATION BY THE PRESENT RESEARCH BASED ON PAST USE IN THE STUDIES REVIEWED SHORT-TERM COVERAGE RATIOS 1. Current Assets to Current Liabilites 2. Current Assets minus ending inventory to Current Liabilities 3. Current Debt to Net Worth 4. Cash + Marketable Securities + Accounts Receivable to Current Debt 5. Current Debt to Ending Inventory 6. Number of Days Payable Outstanding LONG TERM SOLVENCY RATIOS 1. Net Worth to Total L iabilities 2. Net Worth to Long-term L ia b ilitie s 3. Net Worth to Fixed Assets (Gross) 4. Net Worth to Fixed Assets (Net) 5. Net P ro fit Before Taxes and Extraordinary Income to Interest Expense

WORKING CAPITAL SUFFICIENCY RATIOS 1. Working Capital to Sales 2. Working Capital to Total Assets 3. Working Capital to Net Worth 4. Working Capital to Current Liabilities 5. Working Capital to Total Liabilities

LONGER TERM DEBT COVERAGE RATIOS 1. Current L ia b ilitie s to Total Assets 2. Long Term L ia b ilitie s to Current Assets 3. Total Current and Long Term Liabilities to Total Assets 4. Total Debt to Total Assets 5. Total Debt to Net Worth 6. Long Term Debt to Net Worth TABLE 2-15 (continued)

ASSET COMPOSITION RATIOS 1. Fixed Assets to Net Worth 2. Total Current Assets to Net Worth 3. Total Assets to Net Worth 4. Current Assets to Total Assets 5. Fixed Assets to Total Assets 6. Receivables to Current Assets 7. Receivables to Total Assets 8. Accounts Payable to Total Lia b ilitie s

MARKETING PRODUCTIVITY VARIABLE 1. Net Sales to Total Assets 2. Net Sales to Current Assets 3. Net Sales to Cash and Marketable Securities 4. Net Sales to Ending Inventory 5. Net Sales to Receivables 6. Net Sales to Fixed Assets 7. Net Income A fter Taxes and Before Extraordinary Income to Net Sales 8. Net Income A fter Taxes and Before Extraordinary Income to Total Assets 9. Net Income A fter Taxes and Before Extraordinary Income to Net Worth 10. Net Sales to Working Capital 11. Gross P ro fit to Sales 12. Operating Expenses to Sales 13. Operating Expenses to Gross P rofits 14. Gross Margin Return on Investment 15. A ll Expenses To Sales 16. Interest Expense to Sales 17. Net Sales Index 18. Net Worth to Net Sales 19. Average Daily Sales 20. Sales per Employee 21. Gross Margin per Employee 57

TABLE 2-15 (continued)

22. Sales per Square Foot 23. Sales per Inventory Dollar 24. Accounts Payable per Inventory D ollar 25. Gross Margin per Inventory Dollar 26. Net Sales 27. Number of Outlets 28. Average Daily Sales 29. Average D ollar Sales at Cost 30. Times Interest Earned 58

The Theoretical Background of The Research

Before continuing on to the specification of the research design, a consideration of the theory underlying the research seems appropriate in order to identify the relevance of the just completed literature review as well as to identify the basic intent of the upcoming specification of the study's methodology. That consideration, therefore, is now presented.

Business firms frequently make a number of decisions which have a direct impact on their ability to earn a profit. The decisions to pur­ chase additional assets, to utilize more debt, to adjust margins, or to make additional advertising expenditures are common examples. The six subclassifications identified in Table 1-1 represent definable areas for which companies must make choices which can have a direct impact on p r o fit­ a b ility .

When a firm makes a decision which changes the commitment in any of the six subclassifications, the proper procedure seems to be to determine which area offers the greatest potential advantageous impact on p ro fit performance before the decision is fina lize d. For instance, when a company decides to increase advertising expenditures or the number of sales out­ lets, the decision can impact other subclassifications as well as profit­ a b ility . In the example, the debt structure might be affected due to the need to borrow funds. The asset structure might also be affected i f other investment opportunities have to be forgone. This lin e of reason­ ing is analagous to the total systems concept.1*0 Figure 2-1 depicts the basic relationship.

4“Donald J. Bowersox, Logistical Management (Second Edition. New York City: The Macmillan Publishing Co., Inc., 1978), pp. 41-49. r ASSET > MANAGF.MEN'

ROFIT PERFORMANCE

/MARKET INI MANAGEMEN

Figure 2-1 The Interrelationships Which Fxist Amongst and Retween The Research Variable Subclassi- ficntions and Profit Performance

The diagram basically suggests that the impact of changes in anyone of the subclassifications can not be properly evaluated unless the changes' impact on the other areas is also considered. For example, a decision to increase advertising expenditures may increase sales but if the decision creates a cash flow shortage the long run p r o fita b ility may decline if the cash flow variable is more highly related to profit per­ formance than advertising.

The proper method of analyzing the potential benefit of any retail decision, therefore, seems to be to define the relationship which each of the six subclassifications have with re ta il p ro fit performance. A decision's impact on each of the areas can then be measured and the possible outcome on profitability predicted. In addition, by comparing the relative impact which the derision has on the six subclassifications 60

and the relative relationship which each of the six areas have with pro­

fit performance, strategy modifications might be identifiable. For

instance, i f each cash flow management is found to be the most important

predictor of re ta il p ro fit performance, then the e ffe ct on cash flow of

every decision should be emphasized. In this process, modifications to a

strategy may be id e n tifie d which w ill allow the project to be undertaken

in such a way that the p r o fita b ility generated can be enhanced by mini­

mizing any negative influences cash flow restrictions might have on pro­

f i t performance.

The purpose of the just completed literature review is to identify

a lis t of potential variables for use as surrogate measures for the six

subclassifications of the two major classes of research variables in the

investigation of th e ir impact on re ta il p ro fit performance. That pur­

pose is accomplished with the Id e n tifica tio n of the variables lis te d in

Table 2-15, Chapter 3 now considers how these variables can be in ve sti­

gated in such a way as to identify the relationship between the variable

classes and subclasses and p r o fita b ility . The Chapter presents the

methodology designed to co lle ct, analyze, te st, and validate the data

needed to determine i f re ta il decisions can be improved by identifying the

relative importance of the financial and marketing dimensions o f the

decisions made by re ta il managers. CHAPTER 3

THE RESEARCH METHODOLOGY

Introduction

Chapter 3 is designed to sa tisfy the following objective: (1) to identify the dependent variable of the research; (2) to identify and describe the data sources u tiliz e d in the study; (3) to describe the sam­ ple of firms which are examined; and (4) to present a step-by-step description o f the methodology which is used to investigate the impact which financial structure and marketing performance have on re ta il p r o fit­ a b ility .

The basic goal of the chapter is to present the methodology used in the study in a clear and concise manner so that attention is directed toward the findings of the research rather than towards the s ta tis tic a l tools u tiliz e d by the project. Such a presentation aids in meeting the objectives id e n tifie d fo r the study in Chapter 1.

For review, the specific objectives of the research are as follows:

1. To identify the appropriate surrogate measures to represent each of the six subclassifications.

2. To determine which of the two general variables, financial struc­ ture or marketing performance, has the greatest impact on the p ro fita b il­ ity of retailing firms.

3. To determine the relative impact which each of the six decision areas contained in the financial structure and marketing performance classifications has on re ta il p ro fit performance.

61 62

4. To determine i f the relationships between the predictor variables specified as surrogate measures for the six variable types and retail profitability are stable over time.

5. To Investigate the effect which variations in selected managerial strategies have on the a b ility of independent variables to predict re ta il p ro fit performance. The strategies investigated are geographic disper­ sion, pricing, and line of trade diversification.

6. To develop a descriptive model which id e n tifie s the specific fin ­ ancial structure and marketing performance variables to which re ta il mea­ sures should direct p a rticu la r attention.

7. To identify a model to predict the profitability of the retail firms included 1n the sample which allows managers to predict, a priori, the possible p ro fit impact o f proposed changes in financial and marketing activities,

8. To Investigate the nature of the relationship which may exist between the different variable types and profitability, / 9. To identify areas for future research.

The remainder o f Chapter 3 1s presented in the following sections:

1, The Dependent Variable of The Research, 2. The Independent Variables,

3. The Methodological Framework, 4, The Specific Data Analysis Procedures,

5. The R e lia b ility and V a lid ity Checks, 6. The Data Sources, 7. The

V a lid ity o f The Data, 8. The Treatment of Data Irre g u la ritie s , 9. The

Sample, and 10, The Summary.

The logic of presenting the chapter in this fashion 1s based on firs t identifying the variables to be investigated which is done in the f ir s t two sections. Then sections three and four detail how the variables are to be Investigated, and five identifies the means to check the legitimacy 63 of the analysis. Next, the sources of the data fo r the research variables is ide ntified and the means to check the v a lid ity of those sources is pro­ vided. In the ninth section, the specific firms who the research inve sti­ gates are noted,and fin a lly a synopsis of the research process is presented.

Chapter 3 now continues with a consideration of the dependent variable.

The Dependent Variable of The Research

The t i t l e of the research immediately suggests that the study is of profitability, however the exact specifications of that criteria has yet to be resolved. Several measures of p r o fita b ility are suggested in the l i t e r ­ ature reviewed in Chapter 2, Including the net d o lla r p ro fit and the fo l­ lowing ratios; return on total assets, return on net worth, and return on total liabilities. In judging the appropriateness of each of the ratios for the study, net profit dollars seems the least viable because of the apparent dependency of the measure on the size of the firm . Even a highly efficient small corporation has trouble making as large a dollar profit as a much less efficient large firm. Therefore, net dollar profit, is eliminated as a potential dependent measure.

In analyzing the three aforementioned ratio variables in order to iso­ late one for the present research, return on total liabilities appears less appropriate for the study than the other two measures of profitability because the ratio is not actually revealing profitability as a function of the investment in the firm. Effectively, the choice of a single dependent variable thus comes down to either the return on total assets or the return on net worth ratio. The return on total assets ratio is used because the measure is more of a measure of the efficiency of a firm 's management, whereas the return on net worth ratio is indicative of the return a company is making for investors. Because the goal of the research is to aid 64 merchandising managers in the improvement of p ro fit performance, the return

on total assets measure appears to have greater validity for the study ar>

the ra tio reflects the efficie ncy of managerial e ffo rts .

The second issue involved with the choice of a dependent measure is

the question of whether to consider revenues before or a fte r taxes. In

line with the concept of contribution accounting, and in order to avoid any

tax d iffe re n tia l which might exist among the sample corporations, the rate

of return on total assets ratio utilized as the dependent variable is cal­

culated using before tax revenues.

The Independent Variables

Based on past research aiio the a v a ila b ility of data, the following mea­

sures are u tiliz e d as Independent variables in the research study:

Financial Structure Variables

• Financial Management Variables

• Total Debt To Net Worth • Current Debt To Net Worth • Current Debt To Ending Inventory • Average Collection Period • Long Term Debt To Net Worth

• Liq uidity Management Variables

• Current Assets To Current Debt • Number Of Days Payables Outstanding • Cash + Marketable Securities + Accounts Receivables To Current Debt . Gross P rofits To Accounts Payable • Times Interest Earned

• Cash Flow Management Variables

• Net Sales To Cash Flow • Net Worth To Cash Flow • Total Liabilities To Cash Flow ■ Current Liabilities To Cash Flow • Asset Management Variables

• Net Sales To Cash + Marketable Securities • Net Sales To Fixed Assets • Net Sales To Ending Inventory • Net Sales To Net Worth • Net Sales To Accounts Receivables • Net Sales To Working Capital • Net Sales To Current Assets

Marketing Performance .Variables

• Marketing Management Variables

• Market Share • Net Sales Index < Advertising Expenditures To Net Sales « Net Sales Per Employee • Net Sales Per Square Foot of Selling Space . Number Of Sales Outlets

• Margin Management Variables

• Gross Margin To Net Sales • A ll Expenses To Net Sales • Operating Expenses To Net Sales • Gross Margin Per Employee , Gross Margin Return On Inventory Investment • Turn And Earn Profitability Index; ratio is the inventory turnover mutHplied by the gross margin.

In the process of choosing the measures which represent the six sub­ classifications of variables, two criteria are used: first is conformity to the definition of the variable subclassifications; and second, is ability to be controlled by managers. The first criteria represents an attempt to isolate the impact which each o f the six variable classifications has on profit performance, whereas the second criteria is designed to lim it the scope of the research project to measures which have manipulatable impact on profits. In particular, the second criteria is designed to eliminate such environmental factors as In fla tio n , Interest rates, and fluctuations in such output measures as the Gross National 66

Product. While such variables obviously can have an Impact on the a b ility of a firm to generate profits, individual managers have little ability to control the factors.

Individually, each of the six variable classifications designated for investigation is designed to re fle c t how a pa rticula r portion of a firm 's managerial strategy is designed. For example, the Margin Management mea­ sures are u tilize d to id e n tify the p ro fit margin strategies of the sample companies. The Financial Management measures are intended to re fle c t the role of debt in the financial strategy of the firms. Likewise, the Liquidity

Management variables are intended to assess the ability of the firms to maintain short-term solvency. Each of the measures included is therefore related to the ability of the companies to meet short-term obligations.

S im ilarly, the Asset Management variable cla ssifica tio n 1s designed to reflect the strategy employed in the determination of the companies' asset base.

The Cash Flow variable type is intended to assess the efficiency with which the sample firms meet the need fo r cash. Each measure, therefore, reflects the cash flow characteristics of the firms. Marketing Management variables, the final classification, are proposed to reflect the marketing strategy employed by the firms. Thus, such areas as market share, adver­ tis in g expenditure, sales magnitude, and the number of outlets are in ve sti­ gated.

The intent 1n each case, is to focus the research project towards the id e n tifica tio n of measures which are subject to modification or manip­ ulation by managers, I f such measures can be proven to be related to the p ro fit performance of re ta il companies, obvious implications for managerial decision making can be derived. In order to explain how the relationship 67 between the independent variables and p ro fit performance is investigated by the study, then the research methodology is considered next.

The Methodological Framework

The research design of the study is a fiv e phased process as is depicted in Figure 3-1. Each o f the phases is considered in depth in the present section beginning with Phase 1: The Data Collection.

Phase 1: The Data Collection

The four data sources used in the research, the COMPUSTAT Data Tapes,

The Retail Yearbook, the Internal Revenue Supplementary 10-K Reports, and the Supermarket News1 D istribution Study o f Grocery Sales are described completely in a later section entitled The Data Sources. The variables fo r which data 1s gathered is also specified elsewhere; 1n the Immediately preceding section. Therefore, the present section concentrates on the means used to organize the data collection process.

For sim plifica tion purposes the thirty-three independent variables are refered to as variables V-j to V^g, while the dependent measure is refered to as variable D-j. Readers desiring the exact specification of the thlrty-fourvariables are refered to the sections entitled The Dependent

Variable and The Independent Variables.

The format illu s tra te d in Table 3-1 1s used to fa c ilia te the data gathering portion of the study. Specifically, the format first lists the dependent measure which is then followed by the th irty-th re e independent variables. The identical format is used in the coding of the data on IBM cards fo r the data processing requirements of the research. Data for thirty-five companies for the years 1969 through 1977 is included in the data base. Each year's data fo r the individual firms is considered PHASE 1 1 PHASE 2 PHASE 3 PHASE 4 PHASE 5 THE DATA COLLECTION , THE INVESTIGATION THE DATA ANALYSIS THE IDENTIFICATION THE VALIDATION 1 OF OF THE OF 1 THE DATA BASE RESEARCH MODELS THE RESEARCH 1 1 FRIEDMAN i TEST COMPUSTA' 1 DATA 1 CORRELATION! TAPES i MATRIX 1 EXAMINATION THE 1 STEPWISE REGRESSION TEST------RETAIL 1 MULTIPLE ANALYSIS COMPARISONS YEARBOOK REGRESSION 1 CROSS 1 VALIDATION THE 1 OK TRC 1 STUDY Z-Y'EST 1 REPORTS 1 1 i SUPER 1 MARKET 1 NEWS

Figure 3.1. The Methodological Framework TABLE 3-1

THE DATA ORGANIZATION FORMAT

The Research Variables

COMPANIES V1 Vn • 3 Ul,l C1,2 C1 >3 Cl,4 Cl,5 Cl,6 Cl,7 C1,8 C1,9

'2 ,')

D-j = the dependent measure Vi. . .V = variables 1 through 33 1 n C = company m, year n; year 1 = 1969. m,n year 2 = 1970, . .. year 9 = 1977. 70 equivalent to one observation, meaning there are 315 observations in the complete data base.

Phase 2: The Investigation o f The Data Base

The second phase of the research involves examining the sample popu­ lation data in an attempt to identify any characteristics of the data which have an impact on the selection of the s ta tis tic a l techniques incorporated into the study. In order to determine whether a regression based analysis is appropriate, the Investigation focuses on two major issues: ( 1) the existence of multicollinearity 1 among the predictor variables; and ( 2) the precision of the estimation of the regression coefficients .2 Either prob­ lem impedes the use of regression based analysis by causing erroneous con­ clusions to be reached.

Phase two's investigation of the data base takes place in two steps.

F irs t, a correlation matrix 1s generated to reveal the level of correlation exhibited by the predictor variables used in the proposed study. The matrix appears as depicted in Figure 3-2. Each entry 1n the matrix indi­ cates the correlation existing between two of the variables.

The s ta tis tic a l package routine, SAS CORR is used to produce the matrix. Once the information is available, all pairs of variables having a correlation of .3 or higher are isolated and the variable of the two

^ u ltic o llin e a r ity refers to the situation where the independent v a ri­ ables in a linear analysis are highly correlated with each other thus making i t d iffic u lt to id e n tify the true source of the accrued variance,

2This refers to the inability of coefficients identified from one sample to generate accurate predictions from data obtained from a second sample. 71

CMN = che correlation C i i C 12 C l 3 existing between C 2 1 variables M and N.

C 3 1

c n33 n i

Figure 3-2, An Illustration of the Correlation Matrix in Phase 2. explaining the least amount of observed variation in dependent criterion 3 is eliminated from future consideration in the study.1*

The second step in Phase 2 Involves examining the precision o f the estimated correlation coefficients. Green and Tull suggest the use of a cross validation procedure fo r such a te s t . 5 Stop two thus Incorporates the split-halves cross validation capability of the BMDP9R routine^

A finding of stability 1n the estimation of the correlation coefficients, as indicated by a high cross validity for any of the analysis levels, indicates that a regression analysis has validity for the sample. The

3The amount of observed variance explained w ill be id e n tifie d using a stepwise regression technique such as SAS STEPWISE or BMDP2R employing return on net worth as the independent variable and the fu ll set of Inde­ pendent variables identified in the chapter.

1*This criteria for eliminating multicollinearity is based on tra­ d itio n according to Paul E. Green and Donald S, T u ll, Research fo r Market- ing Decisions (Fourth Edition; Englewood C liffs, New Jersey: Prentice H all, Inc,’, 1978), pp. 333-334.

5Same reference as 6, pp. 335-336. 72 exact v a lid ity is reported in Chapter 4.

An alternative technique for dealing with the problem of multicol- linearicy is principal components analysis. The technique creates a set of new variables from the original measures which are, by d e fin itio n uncorrelated. However, a po ten tially severe problem also can occur depend­ ing on the in te rp re ta b ility of the factor scores produced in the analysis . 6

In general, factor scores are represented by a column vector which contains the weights applied to each of the original variables in order to create the new variable. The major problem then lie s in interpreting the meaning of the combination of the old variables which defines the new composite variable. In some cases, where the factor scores are uninterpretable, rotating or reordering the factor scores is of assistance in understanding the meaning of the new variable. In such cases, a VARIMAX ro ta tio n 7 can be u tiliz e d . The SAS FACTOR and BMDP 8M routines each provide such a procedure. However, even the rotation does not guarantee in te rp re ta b ility so the principal components’ alternative is to be incorporated into the research only if the regression approach is rejected. If a' multicollinearity problem is found among the independent variables, Phase 2 1s to in ve sti­ gate the in te rp re ta b ility of the new composite variables created by a p rin c i­ pal components analysis, To the degree in te rp re ta b ility is found, the new composite variables are then used in a regression analysis identical to the procedure incorporating the th irty-fo u r individual variables described previously.

60p. C it. , Green and T u ll, see pp. 423-428, fo r a more complete expla­ nation of factor score interpretation.

7For an explanation of factor rotating procedures in general and the VARIMAX technique in specific see Green and T u ll, (Same reference as # 6, pp. 430-435. Phase 3: Data Analysis

The purpose of Phase 3 is to determine which of the variables under study best predict the p ro fit performance of individual re ta il firms. In order to accomplish the goal, the phase incorporates the predictor v a ri­ ables not eliminated by Phase 2 into a number of s ta tis tic a l procedures.

For the purpose of organizational clarity, the analysis technique^ incor­ porated into the proposed study are reviewed on a hypothesis-to-hypothesis basis . 8

H.: Marketing Performance Variables are not significant predictors of retail profitability.

Hypothesis 1 calls for the determination of whether any of the v a ri­ ables which are refered to as Marketing Performance Variables are sig­ n ifica n t predictors of re ta il p r o fita b ility . To accomplish the goal, the

Marketing Performance Variables not deleted by the multlcollInearity con­ tro ls o f Phase I 9 are regressed against p r o fita b ility . Variables explain­ ing variance which is sig n ifica n tly greater than zero at the ninety five percent level of probability are considered refutations of the null hypothesis . 10

H-; Financial Structure Variables are not significant predictors of retail profitability

“The research hypotheses are the same as o rig in a lly presented in Chap­ te r 1,

9After those variables responsible for any multicollinearity prob­ lems have been removed,

J°A sample t-test analysis is used to test for the significance of the explained variance. 74

Hypothesis 2 calls for the same analysis format as hypothesis 1 with the substitution of the Financial Structure Variables for the Marketing

Performance Variables. Reference can be made to the immediately preceeding subsection for the analysis details, so no further elaboration is offered.

H~: Market Performance Variables are not better predictors of re ta il p r o fita b ility than Financial Structure Variables.

Hypothesis 3 calls for the simultaneous consideration of both v a ri­ able classifications. However, the comparison is complicated due to the independent variables as well as the independent variables' correlation with the dependent measure. When two variable sets composed of m ultiple measures are used, there is no statistical means available to calculate the composite level o f correlation which exists between the separate sets of independent variables. Therefore, a two staged procedure is incorporated into the study to attempt to circumvent the problem.

The f ir s t step in stage one is a stepwise multiple regression analysis of all the variables not eliminated in the investigation of the data base.

All the variables are considered simultaneously and all of the measures which explain an incremental amount of variance which is s ta tis tic a lly sig­ n ific a n t at the 95 percent probability level are then accumulated into the

Marketing Performance and Financial Structure categories based on the assign­ ments made in the section of the present chapter entitled The Independent

Variables.

A fter the sign ifica nt predictors are identified and placed into the proper classification, the rank order of each variable's entry into the stepwise m ultiple regression analysis is noted and the Wilcoxon Test is 75

Hypothesis 2 calls for the same analysis format as hypothesis 1 with the substitution of the Financial Structure Variables for the Marketing

Performance Variables, Reference can be made to the immediately proceed­ ing subsection for the analysis details, so no further elaboration is offered.

H_: Market Performance Variables are not better predictors of retail profitability than Financial Structure Variables

Hypothesis 3 calls for the simultaneous consideration of both v a ri­ able classifications. However, the comparison is complicated due to the characteristics of the independent variables as well as the variables' correlation with the dependent measure. When two variable sets composed of multiple measures are used, there is no statistical means available to calculate the composite level of correlation which exists between the separate sets of independent variables. Therefore, a two staged procedure is incorporated into the study to attempt to circumvent the problem.

The f ir s t step in stage one is a stepwise multiple regression analysis of all the variables not eliminated in the investigation of the data base.

All the variables are considered simultaneously and all of the measures which explain an incremental amount of variance which is s ta tis tic a lly sig n ifica n t at the 95 percent probability level are then accumulated into the Marketing Performance and Financial Structure categories based on the assignments made in the section of the present chapter en title d The Inde­ pendent Variables.

A fter the sign ifica nt predictors are ide ntified and placed into the proper classification, the rank order of each variable's entry into the stepwise multiple regression analysis is noted and the Wilcoxon Test is 76

FINANCIAL STRUCTURE VARIABLES

Variables Name Entry Rank

1. ______

2 .

N ______

E of Ranks

MARKETING PERFORMANCE VARIABLES

Variables Name Entry Rank

1. ______2 .

M______

E of Ranks

Figure 3-3. The Wilcoxon Test Design For Hypothesis 3 statistically significant at the 95 percent probability level, hypothesis

3 is rejected provided that the Marketing Performance variable set is the more significant of the two classifications of variables.

H.: The amount of variance explained by the variables used as surrogate measures fo r each of the six variable subclassifications is not significantly greater than zero.

The analysis of hypothesis 4 takes place in two stages. In stage one, a stepwise m ultiple regression analysis of the entire research sample of 11 variables is used to identify the measures which explain an amount of incre­ mental variance which is s ta tis tic a lly sig n ifica n t at the 95 percent prob­ ability level. The F-test is employed to assess the statistical signifi­ cance of the variables' incremental contribution.

Once the significant Variables are identified, the measures are accumulated into the six subclassifications based on the assignments pre­ sented in the section e n title d The Independent Variables of the present chapter. The amount of incremental variance explained by each subclassifi­ cation is then ascertained by summing the amount of incremental variance accounted for by each subclassification. Figure 3-4 identifies the format used to assess the amount of incremental variance explained by each subclas­ sification. Six such forms are necessary; one for each subclassification.

Variable Subclassification

2 Variable Name Incremental R

1. ______2 . ______

3. ______

4.

Figure 3-4. The Calculation of The Incremental Variance Explained By Each Variable Subclassification 79 Stage two of the analysis again employs stepwise multiple regression to determine the variance explained by each subclassification, but the six groups of variables are considered separately. Six separate multi­ ple regression analysis are made; one for each of the subclassifications.

In each of the analyses, a ll of the measures assigned to the pa rticula r subclassification in the e a rlie r section e n title d The Independent Vari­ ables are incorporated into the regression. The amount of variance explained by each subclassification is then tested for statistical sig­ nificance at the 95 percent probability level through the use of the F- test. The variables which make statistically significant incremental con­ tribution to explained variance within the subclassifications are also noted.

The two stages are included for several reasons. F irs t, the six subclassifications are the theoretical strategy areas which the research

is designed to investigate. Stage two id e n tifie s the variables which are able to make a significant incremental contribution to explaining the

variance in p ro fit performance within the individual subclassifications.

Having two variables within the same subclassification explain the same

variance is of no managerial value, so stage two aids in reducing the

variables o f concern to managers.

In addition, having two variables explain common variance when the

measures are even in separate subclassifications is of no value in a pre­

d ictive sense. One needs only one of the two variables to be equally

effective in a predictive model. Thus, the stage one analysis aids in the

id e n tific a tio n of the predictive model which is developed in Chapter 5. Stage two of the analysis again employs stepwise multiple regression to determine the variance explained by each subclassification, but the six groups of variables are considered separately. Six separate multi­ ple regression analysis are made; one for each of the subclassifications.

In each of the analyses, all of the measures assigned to the particular subclassification In the earlier section entitled The Independent Vari­ ables are incorporated into the regression. The amount of variance explained by each subclassification is then tested for statistical sig­ nificance at the 95 percent probability level through the use of the F- test. The variables which make statistically significant incremental con­ tribution to explained variance within the subclassifications are also noted.

The two stages are included fo r several reasons. F irs t, the six subclassifications are the theoretical strategy areas which the research is designed to investigate. Stage two id e n tifie s the variables which are able to make a significant Incremental contribution to explaining the variance in profit performance with the individual subclassifications.

Having two variables within the same subclassification explain the same variance is o f no managerial value, so stage two aids in reducing the variables o f concern to managers.

In addition, having two variables explain common variance is of no value in a predictive sense. The second variable only adds to the data required because;, one needs only one of the two variables to be equally effective in a predictive model. Thus, the stage one analysis aids in the id e n tifica tio n of the predictive model which is developed in Chapter 5. 80

F in a lly, by examining the differences between the variables id e n ti­ fied as significant predictors in the two stages, the interactions between the variables can be studied. When a variable enters significantly 1n one of the stages and in s ig n ific a n tly in the other, logic suggests that where significance is obtained some subset of the variables are interacting to produce different results. An examination of the order of entry of the variables and the affect of the variablds entry Into the stepwise regres­ sion analysis on the significance of the variance explained by the other measures thus can be investigated.

Hr? The profile of significant predictor variables is not stable over time

The investigation of hypothesis 5 takes place in three stages. Stage one involves separating the total data base into three year segments.

Three year segments are used to allow enough observations 1n each sample to have a valid examination of a number of variables. Each three year seg­ ment contains 115 observations. Figure 3-5 illustrates the division of the data base into the three segments; each cell representing one of the segments.

1969 1970 1971 1972 1973 1974 1975 1976 1977 COMPANY 1

35

Figure 3-5. The Three Time Segments

The second stage requires performing a stepwise m ultiple regression analysis of the data for each three year segments in order to ascertain 81 i f the variables which are sig n ifica n t predictors of re ta il p ro fit per­ formance are stable over time. In order to preserve the theoretical basis o f the research which is designed to investigate the re la tive impact of

Financial Structure and Marketing Performance on re ta il p ro fit performance and to eliminate the possibility of having too few observations per inde­ pendent variable, the variables Identified as the significant predictors of profitability for each of the six subclassifications of the two major variable types are considered. The variables are identified in the second stage analysis o f hypothesis 4, which is based upon the separate analysis of each of the six subclassifications. The identification of the signi­ ficant variables results in the designation of a profile of variables id e n tifie d as s ig n ific a n tly correlated with p ro fit performance in the separ­ ate stepwise analysis of the six subclassifications and ts hereafter refer­

red to as the Significant Predictor Profile.

V“ V : are the independent variables explaining a~n a statistically significant amount of/ * incremental variance 1n the separate n analysis of the six subclassifications.

Figure 3-6. The Significant Predictor Profile

The third phase of the analysis of hypothesis 5 is the actual sta­

tistical analysis of the stability of the Significant Predictor Profile.

Step one of the third phase considers the statistical significance of the

amount of variance explained by the entire Profile in each of the three

time segments. The F-test is used to determine if the variance explained

in each segment is statistically significant with a 95 percent level of

confidence. If the variance is significant in all three segments, the Pro­

f ile as a whole is considered stable and the hypothesis refuted. 52

if the variables which are significant predictors of retail profit per­ formance are stable over time. In order to preserve the theoretical basis of the research which is designed to Investigate the re la tive impact of

Financial Structure and Marketing Performance on re ta il p ro fit performance and to eliminate the possibility of having too few observations per inde­ pendent variable, the variables identified as the significant predictors of profitability for each of the six subclassifications of the two major variable types are considered. The variables are identified in the second stage analysis of hypothesis 4, which 1s based upon the separate analysis of each of the six subclassifications. The Identification of the signi­ ficant variables results 1n the designation of a profile of variables

Ide ntifie d as s ig n ific a n tly correlated with p ro fit performance in the separ ate stepwise analysis of the six subclassifications which is referred to as the Significant Predictor Profile.

~ V. “ Vu ^a-n: are indePenc|ent variables explaining a statistically significant amount of i incremental variance 1n the separate n analysis of the six subclasslflcations.

Figure 3-6. The Significant Predictor Profile

The third phase of the analysis of hypothesis 5 is the actual sta­ tistical analysis of the stability of the Significant Predictor Profile.

Step one of the third phase considers the statistical significance of the amount of variance explained by the entire Profile in each of the three time segments. The F-test is used to determine if the variance explained in each segment is statistically significant with a 95 percent level of confidence. I f the variance is sig n ifica n t in a ll three segments, the Pro­ file as a whole is considered stable and the hypothesis refuted. 33

Step two of the third phase considers the stability of the individual measures making up the Significant Predictor Profile, The individual measures in each time segment which explain a sign ifica nt amount of incre­ mental variance are noted. If a measure is statistically significant at the 95 percent confidence level 1n each period, the individual variable is considered stable.

In the final step of the third phase, the Friedman Test is used to ascertain whether the individual variables within each time segment actually havea significantly differing ability to explain the variance in p ro fit performance. The Friedman Test determines whether the mean entry rank of the individual measures within the Significant Predictor Profile into the three stepwise multiple regression analysis actually differ. A significant finding means that the abilities of the individual measures do d iffe r. Figure 3-7 summarizes the Friedman Test procedure.

The Significant Predictor Profile

t 1 • V1 h V3 The S1 1 2 3 N Cell entries are Data the ranks, or S2 2 3 N 1 Segments the predictors' S3 3 N 1 2 order of entry 1n the stepwise S, = 1969, 1970, 1971 multiple regres­ So = 1972, 1973, 1974 sion analysis. S3 = 1975, 1976, 1977

Figure 3-7. The Friedman Test Design For Investigating Hypothesis 5

The profile of significant predictor variables does not d iffe r between more and less profitable firms

The analysis for hypothesis 6 is broken into three parts. First, a ll observations are ranked according to the magnitude of the p r o fit­ ability of the firms. Each year is considered an observation so there 84 are three hundred fifte e n observations as the data base covers nine years and thirty-five corporations. The ranking allows the upper half and lower half of the sample, based on p ro fit performance, to be id e n tifie d .

A fter the sample is divided into the two subsamples, the same te s t­ ing procedure is used as fo r hypothesis fiv e . F irs t, the S ignificant Pre­ dictor Profile variables are incorporated into separate stepwise multiple regression analyses of the two halves. The statistical significance of the total variance explained by the Profile in each sample half is examined through the use of an F-test. ilext the individual measures within the

Profile which explain a significant amount of incremental variance in each half are noted and recorded. Finally the Friedman Test which is summarized in Figure 3-8 is used to ascertain whether the predictive power of the in d i­ vidual measures in the Profile are in fact different. Thus again, the reliability of the Profile as a unit and the reliability of the individual measures w ithin the P rofile are tested.

P • • • a Pb Pc Pn upper half Cell entires are ranks of the 1 2 3 n order predictors under of entry. lower half 2 3 n 1

P = Predict Variables a through n a-n 3 upper 11 ower, half = profitability class

Figure 3-8. The Friedman Test Design U tilized To Analyze Hypothesis 6.

H7: The profile of significant predictors does not d iffe r between d iffe re n t managerial strateaies.

The identification of the strategies adopted by each of the corpor­

ations included in the research sample is accomplished through secondary 8b sources. Pricing strategies are based on the firm s' gross margins; in fo r­ mation which is collected as part of the research project. The id e n ti­ fication of the geographic diversification and line of trade strategies employed by the sample companies relies on the Securities Exchange Commission

Supplementary 10-K Reports. In addition, the Retail Yearbook, a proprie­ tary service of Management Horizons of Columbus, Ohio also provides a similar type of information.

After the strategies utilized by each firm are Identified, the same three step statistical analysis as Is employed in the study of hypothesis

5 is engaged. F irs t, the F-test is u tiliz e d to determine whether the variance in the dependent measure which is explained by the Significant

Predictor Profile is statistically significant at the 95 percent probabil­ ity level 1n each of the eight samples. Next, the separate stepwise multiple regression analysis of the eight segments are consulted to deter­ mine which of the individual measures making up the Significant Predictor

Profile explain a statistically significant amount of incremental vari­ ance in each o f the strategy samples. For an individual measure to be considered stable, the variable must be significant in all the strategy segments.

Finally the Friedman Test is used, as before, to assess whether the variables within the Significant Predictor Profile actually have differ­ ent levels of a b ility t.o explain the variance 1n re ta il p ro fit performance through the comparison of the individual measures mean rank of entry into the stepwise regression analysis. Figure 3-9 identifies the subdivision

of the sample into the strategy subsamples. Figure 3-10 ide ntifies the

Friedman Test design fo r analyzing hypothesis 6. 8 fi

Locally Operating Companies Comparison 2 Regionally Operating Companies Total Sample Nationwide Operating Companies

Upper half Gross Margin Comparison 1 Lower ha lf Gross Margin Total Sample

f.upermarket Corporations Comparison 3 Convenience Store Corporations Total Sample Diversified Corporations

Figure 3-9 . The Sample Divisions Used In The Investigation of Hypothesis 7.

p • • • a Pb Pc Pn Local Companies 3 n ' 1 ' 2 Regional Companies 1 2 3 n National Companies 2 3 n ,1

Upper-half Margins 1 3 2 n Cell entries repre­ Lower-half Margins 2 3 1 n sent the order of the entry of the significant predic­ Supermarket Companies n 1 2 3 tor variables Convenience Companies 1 n 3 2 Diversified Companies 3 2 n 1

= The Predictors Achieving Significance In a-n The Stepwise Analysis

Figure 3-10. The Friedman Test Design Employed In The Analysis of Hypothesis 7.

Hfi: The relationship between the individual measures found sign ifica nt and p r o fita b ility is not linear.

The investigation of hypothesis 8 requires the use of a multiple regression analysis incorporating the predictors identified as the Signi­ fica n t Predictor P rofile in the e a rlie r stages of the data analysis. The 37

regression coefficients are then tested for significance. If the coef­

ficients are significant at the ninety-five percent probability level,the

hypothesis is refuted. In addition, the plots of the residuals for the

for the Significant Predictor Profile are examined. If the relationship

is lin e a r, the residuals are scattered randomly around the vertical axis

of the plot .15

Phase 4. Id e n tifica tio n of The Research Models

Two research models are identified by the research. First, a

descriptive model is developed from the various procedures which are used

to identify which of the variables of the ones examined best differentiate

between the p ro fit performance of re ta ilin g firms and how the magnitude

of such variables impact p r o fita b ility . The f i r s t model is of a con­

ceptual nature, designed only to suggest which variables should be of con­

cern to the re ta il managers responsible fo r company performance.

The second model is of a predictive nature, using the magnitude of

the sig n ifica n t predictor variables to forecast re ta ilin g p ro fit performance.

The inforamtion fo r the predictive model again comes from the investiga­ tions of the eight hypotheses. Basically the design is as follows:

profitabilitv = a (variable 1) + b (variable 2) + n (variable z) + k : where a through n = weighting coefficients k = a constant

For a more detailed explanation see N.R. Draper and H.Smith, Applied Regression Analyis (New York City: John Wiley & Sons, Inc., 1966), pp. 86 - 1 0 0 . Phase 5: The Validation Procedure

The value of the findings of any research project depends on the reliability and validity of the study. Carmines and Zeller define the two constructs as follows :16

1. R e lia b ility concerns the extent to which an experiment, te s t, or any measuring pro­ cedure yields the same results on repeated tr ia l s.

2. V a lid ity concerns the a b ility of any measuring device to measure what i t pur­ ports to measure.

Concerns about the re lia bility of the research project are directed towards the accuracy of the secondary data used. To ascertain the r e li­ a b ility of the data base, the m ultiple sources from which the data is a va il­ able are u tiliz e d . Unfortunately, of the marketing performance variables, only the sales and gross margin return on inventory variables are available from multiple sources. Therefore, the reliability assessment is limited to the extent of the information a v a ila b ility . To complete the r e li­ a b ility assessment, one years data fo r each company is randomly chosen fo r investigation. Alternative public sources, primarily the companies' annual reports, are used to check the accuracy of the data. The original data and the second data sources are examined aaainst each other in order to discover discrepancies greater taan what might be attributed to round­ ing errors. Any such problems with the data are reported in Chapter 4.

16Edward G. Carmines and Richard A. Z e lle r, R e lia b ility And V a lid ity Assessment (Beverly H ills, California: SAGE Publications, Inc., 1979), pp. 11-13. 89

The validity of the project is ascertained in several ways. First, the content validity is addressed through the assessment of the underlying theory behind the study which is offered in Chapter I. Second, research findings must be reliable if the study is to have validity so the re li­ ability investigations reveal at least the minimum conditions for validity.

Finally, through the use of multiple test methods the research design investigates the existence of statistical conclusion validity. Consistent findings by multiple measures indicates that the measures are more likely to be measuring what is intended

The Specific Data Analysis Procedures

The consideration of the individual data analysis techniques is organized around the questions each procedure is designed to answer. The present section identifies the statistical packages available for per­ forming the necessary analysis.

The M ultiple Regression Programs

Both stepwise m ultiple regression and simple m ultiple regression are u tiliz e d . Any number of s ta tis tic a l computer packages can f u l f i l l the requirements. Examples include BMD-02R, SAS-GLM, and the SPSS subpro­ gram REGRESSION.17 Significance tests for each variable are included in both packages.

The Cross Validation Program

The s ta tis tic a l package BMDP9R provides the necessary cross valida­ tion program. The procedure calculates a goodness of f i t between separ­ ate regressions drawn from two random samples which represents a s p lit halves analysis of the original sample.

17Norman H. Nie, et a K , SPSS: S ta tistica l Package fo r the Social Sciences (New York: McGraw-Hill Book Company, 1975). Nonparametric S tatistics The Friedman Test and the Wilcoxon Test are the only such tests used in the proposed study and both are re la tive ly simple procedures and require no computer software.

The Data Sources

Three major data bases u tiliz e d for the proposed research. The f ir s t o f the three bases, the COMPUSTAT Data Tapes, are a service of The

Standard S ta tistics Co., Inc. which is a subsidiary of the Standard &

Poor Corporation. Basically, the COMPUSTAT Data Tapes are composed of nine individual data files each of which contains a set of financial infor­ mation pertaining to one of the approximately 2700 corporations listed on tapes .18 Option or file five, the Computerized Financial Analysis (CFA),

is the specific data f ile which is employed in the research.

The CFA data f ile contains 125 pieces of financial and market per­

formance information in addition to presenting approximately 50 additional

measures for a period covering 1959 through 1977.19 Access to the CFA

f ile is provided through the interactive computer fa c ilitie s of The

Ohio State University. The sources of the data contained in the CFA data

file s , as well as for the remainder of the COMPUSTAT Data Tapes, includes

the following:

1. The 10-K and 10-Q reports filed with the Securities and Exchange Commission.

2. Company Reports; such as annual and quarterly public information reports.

18A complete explanation of the individual data files is offered in an operations manual provided for subscribers of the COMPUSTAT services by the Standard S ta tistics Company.

19The information refered to is exhibited in Appendices A and B. 3. Company Contacts.

4. Interactive Data Services, Inc.: contains stock exchange information.

5. National Association of Security Dealers Automated Quotations; contains market data of primary over- the-counter companies.

6. Uniform S ta tistica l Reports; an annual report file d by E lectric U tility and Gas Companies.

7. Civil Aeronautics Board; quarterly financial statistics on air carriers.

8 . Dow Jones News and Business Service; provides sales, net income, and earnings per share information.

9. Wall Street Journal — Digest o f Earnings Reports.

10. Standard & Poor's Publications; used to augment annual reports.

The second source is the Retail Yearbook, which is an annual proprie­ tary service of management Horizons, Inc., of Columbus, Ohio. Appendix M reveals the information provided by the Retail Yearbook. The sources listed by Management Horizons for the data contained in the Retail Yearbook includes the following: /

1. 10-K reports of the Securities and Exchange Commission

2. Company Annual Reports

3. Moody's Industrial and Q-T-C Industrial Manuals

4. The Wall Street Journal

5. The Wall Street Transcript

6. Standard & Poor's Corporation Records

The third major source is the individual 10-K reports of the Securities and Exchange Commission. The 10-K's are used to ascertain the advertising expenditures of the individual corporations. All publically held companies must report the amount spend on advertising i f the figure exceeds cne percent o f the company's sales .20

The V a lid ity of The Data Sources

The COMPUSTAT Data Tapes are validated by Standard & Poor in three ways: f ir s t , spot checks of the raw data are randomly taken for accuracy; second, once the raw data is placed on computer tapes the consistency of the data is checked by analyzing the data for significant departures from normal trends which i f found are ind ivid ually analyzed for accuracy; and third, a computer check for data which occurs outside of specified para­ meters.

The Retail Yearbook does not contain a section on any validation of the data used, however the data is obtained from governmental and indus­ tr ia l sources the accuracy o f which can only be validated by auditing the actual financial records of the companies' included. Such an examination is beyond the scope o f the proposed research due to the cost of such an exercise. In addition, because the financial statements of publically held corporations are subject to external audits due to Internal Revenue

Service requirements errors are not lik e ly .

The Treatment o f Data Irre g u la ritie s

The major irre g u la rity which can be expected is the occurence of mergers and acquisitions during the time period considered by the research.

Such cases Will be ide ntified in two ways: f ir s t , the Retail Yearbook annually publishes a l i s t of re ta ilin g companies which have experienced a merger, an acquisition, name change, or other change during the previous

J0The fourth source, the Supermarket News' Annual D istribution of Grocery Store Sales is not considered major as i t provides only the Market Share Variable. ^ 93 year ; second, the actual data gathered from both sources are

examined fo r changes in total assets which exceed ten percent within one

year. In the la tte r case, the annual reports of the individual firms

experiencing such changes are analyzed for reports o f mergers, acquisitions,

or divestures.

To counter the changes in company financial and performance measures

arising because o f the changes in the asset base o f firm s, companies

experiencing mergers, acquisitions, or divestigers are considered as

separate e n titie s before and a fte r the changes. Such a procedure does not

complicate the analysis undertaken in the proposed research because the

technique only causes missing data values within the time period considered

for each firm and a ll the programs to be u tiliz e d are equipped to handle

such a problem.

The Data Collection Framework

The data for the research 1s gathered from four sources; the COMPUSTAT

Data Tapes, the Retail Yearbook, the 10-K Reports, and the Supermarket News.

Access to the COMPUSTAT tapes is provided through the interactive computer

fa c ilitie s o f The Ohio State University. The data for each company liste d

on the tapes is provided in an individual computer printout from where the

necessary information is taken.

The Retail Yearbook is an annual publication which lis ts in excess of four hundred-fifty re ta ilin g firms. Data from the yearbook is obtained

through the simple visual inspection of the publication. Data is likewise obtained from the 10-K Supplementary Reports.

See for example Thomas J. Noon, Retail Yearbook 1 979, Columbus, Ohio: The Management Horizons, Inc., 1979, page xiv. 94

A Summary

Chapter 3 provides the framework and presents the individual techniques used to perform the proposed research. In summary, hypothesis 1 through 7 are designed to identify the significant predictors of retail profitability and 8 defines the research models.

The hope is that the present chapter serves to identify the techniques used to organize, classify, reduce, and analyze the data generated by the study. A step-by-step diagram o f how the described procedures are employed in the research e ffo rt is presented in Figure 3-1. The reader is referred to the p icto ria l for a general summary of the overall research design. CHAPTER 4

THE PRESENTATION OF THE RESEARCH FINDINGS

Introduction

The purpose of Chapter 4 is to present the information needed to answer the research questions and hypotheses set forth in Chapter 1. In order to accomplish the goal a large amount of tabular data and appen­ dices are incorporated along with some description of the information.

The chapter proceeds with the following sections:

1. The Id e n tifica tio n of The Independent Variables

2. The Relative Impact of Financial Structure and Marketing Performance On P ro fita b ility

3. The Relative Impact of The Six Subcategories of Financial Structure and Marketing Performance On Profitability

4. The Reliability of The Significant Predictor Variables Over Time

5. The Reliability of The Significant Predictor Variables Over Firms of D iffering P ro fit Performance

6. The Reliability of The Significant Predictor Variables Across Pricing Strategies

7. The Reliability of The Significant Predictor Variables Over D iffering Geographical D iversification Strategies

8 . The Reliability of The Significant Predictor Variables Over Line of Trade Strategies

9. The Investigation of The Shape of The Relationship Between The Significant Predictor Variables and Retail Profitability

10. The Secondary Research Findings

95 96

The Identification of The Independent Variables

The f ir s t section of Chapter 4 is directed towards providing the answer to the f ir s t research question set forth In Chapter 1, which is as follows:

Q-,: From the available financial and marketing information, what are the most appropriate variables to use as surrogate measures for the six Individual variable classifications?

Answering the first research question requires two steps: firs t, a review of the marketing and financial literature; and second, a statisti­ cal analysis of the characteristics of the independent variables. The lite ra tu re review presented in Chapter 2 Ide ntifie s the preliminary lis t of variables to be considered for inclusion 1n the study. The lis t 1s modified by the remaining step 1n the present section, but fo r review the variables are as follows:

The Financial Management Variables

• Total Debt to Net Worth Current Debt to Net Worth • Current Debt to Ending Inventory • Average Collection Period • Long Term Debt to Net Worth

The Liq uidity Management Variables

• Current Assets to Current Debt . Number of Days Payables Outstanding . Cash + Marketable Securities + Accounts Receivables to Current Debt • Gross P rofits to Accounts Payable • Times Interest Earned

The Cash Flow Management Variables

. Net Sales to Cash Flow • Net Worth to Cash Flow . Total Liabilities to Cash Flow • Current Liabilities to Cash Flow 97

The Asset Management Variables

• Net Sales to Cash + Marketable Securities • Net Sales to Fixed Assets • Net Sales to Ending Inventory • Net Sales to Net Worth • Net Sales to Accounts Receivables • Net Sales to Working Capital • Net Sales to Current Assets

The Marketing Management Variables

• Market Share • Net Sales Index • Advertising Expenditures to Net Sales • Net Sales Per Employee • Net Sales for Square Foot o f Selling Space • Number of Sales Outlets

The Margin Management Variables

. Gross Margin to Net Sales . A ll Expenses to Net Sales • Operating Expenses to Net Sales • Gross Margin Per Employee • Gross Margin Return On Inventory • Turn and Earn P ro fita b ility Index

The research design specified 1n Chapter 3 requires that the lis t of

variables Identified by the literature review must be examined 1n order

to analyze the effect of two s ta tis tic a l problems; auto correlation 1 and

multi col lin e a rity .2 However, a visual analysis of the variable lis t

identifies another problem which needs to be eliminated before the statis­

tic a l investigation of the variables is undertaken.

*For a description of auto correlation see Erwin E. Nemmers and John H. Myers, Business Research (New York City: McGraw-Hill Book Com­ pany, 1966), p. 170.

2For a description of m u ltico l1inearity see Paul E. Green and Donald S. T u ll, Research For Marketing Decisions (Englewood C liffs , N.J.: Prentice-Hall, Inc., 1978), p. 332. 98

The dependent variable is Return on Total Assets which in the present study is calculated as follows:

Net P ro fit Beforfe Taxes Total Assets

Both the numerator and denominator o f the measure are common to several of the independent variables. Because of the commonality, correlation can result which 1s based on the sim ilarity of the way 1n which the variables are calculated and not on the performance of the sample firm s. The corre­ lation is therefore spurious and not relevant to the study.

Thus, the Independent variables which use to ta l assets, fixed assets, aggregate current assets, net p ro fits , or to ta l expenses are eliminated from the analysis. However, the individual components of current assets are retained on the assumption that the spurious correlation problem is not so severe in such cases due to the disaggregation of the current asset category Into smaller and smaller units. An examination of the cross­ variable correlation matrix tends to confirm the observation as the cor­ relations between the dependent measure and the Independent variables using ending Inventory, accounts receivable, and cash plus marketable securities as part of the measures 1s as follows:

Current Debt to Ending Inventory with ROTA -.2398 Net Sales to Ending Inventory with ROTA -.0533 Net Sales to Accounts Receivables with ROTA .1682 Net Sales to Cash plus Marketable Securities with ROTA -.1893

The variables which are eliminated from further analysis In order to reduce the level o f spurious correlation are the following:

1. Current Assets to Current Debt 2. Cash + Marketable Securities + Accounts Receivables to Current Debt 99

3. Net Sales to Current Assets 4. Net Sales to Fixed Assets 5. Times In te re s t Earned3 6. A ll Expenses to Net Sales 7. Operating Expenses to Net Sales

The final step in the analysis of research question one involves the investigation of two statistical problems, the firs t of which, autocorre­ lation, is defined as the situation in which one value of a variable in a time series is related to another value of the same variable at an earlier or later point in time.1* The Durbin-Watson Test represents one method to determine if autocorrelation does exist in the data base. The test value for the data base is 1.15314, indicating that positive autocorrelation does e x is t. No changes in the research design are necessary, however, because in logitudinal data one expects observations to be related. In addition, autocorrelation has been found to aid in performing predictive a n a ly s is .5

The second statistical problem, multicol linearity, refers to the situation where the predictor variables exhibit excessively high correla­ tion among themselves,6 The correlation matrix of the independent vari­ ables which is reproduced as Appendix A, identifies seven pairs of predictor variables which have a correlation of .8 or higher.7 For each

3TIE is calculated by d iv id in g net income before in te re s t expenses and taxes by total interest payments.

“Nemmers and Meyers, p. 170,

5Ib id ., p. 170.

6Green and T u ll, p. 332.

7Green and Tull suggest, as a rule of thumb, that one of all pairs of variables with a correlation of .9 or more be eliminated. The present research adopts a more conservative rule of .8 to further control the problem and to eliminate differentiating between value falling around .9. 100 pair, the variable having the lowest level of correlation with the dependent measure is eliminated from further study in order to control the level of multicol1inearity which exists. Table 4-1 identifies the exact level of pairwise correlation and the correlation with the depend­ ent varia ble fo r the seven p a irs o f independent measures which have a correlation of .8 or more. Based on the research criteria of having a cor­ relation of .8 or higher, the member of the pair having the lowest cor­ relation with the dependent measure is to be eliminated. The following variables are omitted from further analysis on the basis:

• Current Debt to Net Worth • Long Term Debt to Net Worth • Earn & Turn . Net Sales to Net Worth • Current Liabilities to Cash Flow • Cash + Marketable Securities + Accounts Receivables to Current Debt

The elimination of independent variables to minimize the spurious correlation and multi collInearity problems leaves the measures presented in Table 4-2 for inclusion 1n the study, ,

As one final check for the level of multi col linearity which exists in the data base, the twenty-one Independent variables which are identi­ fied in Table 4-2, are incorporated into a cross-validation procedure.0

Using the s ta tis tic a l package BMDP9R9 to compute the goodness o f f i t between separate regression equations drawn from two random samples

8Cross-Validatlon is suggested by Green and Tull, pp. 335-336, as one of the safest procedures for checking for the existence of m ulti- collinearity.

9W. J. Dixon and M. B. Brown (e d ito rs ), BMPP Manual (Berkeley, California: University of California Press, 1979, Program Revision Data, November 1978), p. 418. 101

TABLE 4-1

THE MULTICOLLINEARITY ANALYSIS: STEP 1

B_ TD/NW w ith CD/NW GMROI w ith EARN & TURN -.4976* .4402 .0544 -.0129

CD/NW w ith NS/NW TD/NW w ith NS/NW -.4402 -.4113 -.4976 -.4113

TD/NW w ith LTD/NW TL/CF w ith CL/CF -.4976 -.3721 -.3210 -.2395

CD/NW w ith LTD/NW -.4402 -.3721

A = Pairs of Independent Variables with a correlation of .9 or higher B = Pairs of Independent Variables with a correlation of .8 or higher * = numbers represent correlation of the specific predictor variable with the dependent variable TD = Total Debt LTD = Long Term Debt CL = Current Liabilities NW = Net Worth GMROI = Gross Margin Return CF = Cash Flow CD = Current Debt on Inventory El = Ending Inventory NS = Net Sales EARN & TURN = Earn & Turn Index TL = Total Liabilities 102

TABLE 4-2

THE INDEPENDENT VARIABLES INCLUDED AFTER THE INVESTIGATION OF THE DATA BASE

Financial Management Variables L iq u id ity Management Variables

• Total Debt to Net Worth • Number of Days Payable Outstanding • Current Debt to Ending • Gross Profits to Accounts Payable Inventory • Average Collection Period

Cash Flow Management Variables Asset Management Variables

• Net Worth to Cash Flow • Net Sales to Cash + Marketable • Total Liabilities to Cash S e cu ritie s Flow • Net Sales to Accounts Receivables • Net Sales to Cash Flow • Net Sales to Working Capital • Net Sales to Ending Inventory

Marketing Management Variables Margin Management Variables

• Market Share Average • Gross Margin to Net Sales • Net Sales Index . Gross Margin fo r Employee • Advertising Expenditures to • Gross Margin Return On Inventory Net Sales • Net Sales Per Employee • Net Sales Per Square Foot o f S e llin g Space • Number of Sales Outlets 103 which represent the s p litin g o f the o rig in a l sample in to two halves, the following information is revealed:

F-Statistic 1.57 Numerator Degrees o f Freedom 164 164 Denominator Degrees o f Freedoms 120 S ignificance .0048

The sig n ific a n c e level o f .0048 means th a t there is a .48 percent chance that the estimated regression coefficients from sample-to-sample do not have a good fit. That is, the estimated regression coefficients show sample-to-sample stab ility at a 99,52% confidence level. Such a finding suggests that no significant m ulticollinearity problems exist in the data base, and thus the variables listed 1n Table 4-2 represent the final group of independent variables for Inclusion 1n the research.

The Relative Impact of Financial Structure and Marketing Performance On Profitability

The second section of the present chapter is devoted to answering the second research question set forth 1n Chapter 1, which reads as fo llo w s:

Q?: In general, are Financial Structure c Variables or Marketing Performance Variables more highly correlated with profitability?

Research question two s p e c ific a lly c a lls fo r the In ve stig a tio n o f the following hypotheses:

H.: Marketing Performance Variables are not significant predictors of retail profitability.

H2: Financial Structure Variables are not significant predictors of retail profitability 104 H3: Marketing Performance Variables are not better predictors of retail profitability than Financial Structure Variables

In order to investigate the three hypotheses, the three lists of variables presented in Table 4-3 are employed. The second financial structure category is simply the firs t Financial Structure group of variables with the Asset Management Variable category removed.

The firs t hypothesis, which for review 1s stated as follows;

H.: Marketing Performance Variables are not significant predictors of retail profitability. calls for the simultaneous consideration of all the Marketing Performance

Variables, which includes both the Margin Management and Marketing Man­ agement variable categories, 1n a stepwise regression analysis. The step- wise regression procedure provides three pieces of Information; one, the order in which the individual Marketing Performance Variables enter the so lu tio n based on each v a ria b le 's a b ilit y to explain variance 1n the dependent measure Independent of the other Independent measures; two, the amount and statistical significance of the total variance explained by all the Marketing Performance Variables; and three, the amount and sta­ tistica l significance of the Incremental variance accounted for by each o f the in d iv id u a l Marketing Performance V ariables, Table 4-4 summarizes the firs t piece of information; Table 4-5 the latter two.

Table 4-4 simply identifies the order 1n which the nine Marketing

Performance Variables enter Into a stepwise regression analysis employing the nine variables listed as independent variables and return on total assets as the dependent measure. Table 4-5, however, provides a greater variety of information. Columns A and C respectively provide the cum- mulative R and R2 values for the Marketing Performance Variables, which TABLE 4-3

THE VARIABLES INCLUDED IN THE MARKETING PERFORMANCE AND FINANCIAL STRUCTURE CLASSIFICATIONS

FINANCIAL STRUCTURE1 MARKETING PERFORMANCE FINANCIAL STRUCTURE:

Total Debt To Net Worth Gross Margin To Net Sales Total Debt To Net Worth Current Debt To Ending Inventory Gross Margin Per Employee Current Debt To Ending Inventory Average C ollection Period Gross Margin Return On Inventory Average C ollection Period Days Payables Outstanding Net Sales Index Days Payables Outstanding Gross P ro fits To Accounts Payable Net Sales Per Employee Gross P ro fits To Accounts Net Sales To Cash + Marketable Net Sales Per Square Foot Payable Securities Number of Sales Outlets Net Sales To Cash Flow Net Sales To Ending Inventory Average Market Share Net Worth To Cash Flow Net Sales To Accounts Receivable Net Sales To Advertising Total Liabilities To Net Sales To Working Capital Cash Flow Net Sales To Cash Flow Net Worth To Cash Flow Total Liabilities To Cash Flow 106

TABLE 4-4

THE ORDER OF ENTRY OF THF MARKETING PERFORMANCE VARIABLES IN THE STEPWISE REGRESSION ANALYSIS

ORDER

1 . NET SALES INDEX* 2. GROSS MARGIN TO NET SALES* 3. NET SALES TO ADVERTISING EXPENDITURES 4. AVERAGE MARKET SHARE* 5. NUMBER OF SALES OUTLETS 6. NET SALES PER SQUARE FOOT 7. GROSS MARGIN PER EMPLOYEE 8. NET SALES PER EMPLOYEE 9. GROSS MARGIN RETURN ON INVENTORY

♦Variables whose incremental contribution to explained variance is statistically significant at a 95 percent level of confidence. 107

TABLE 4-5

THE STEPWISE ANALYSIS OF THE RELATIONSHIP BETWEEN MARKETING PERFORMANCE VARIABLES AND PROFITABILITY

STEP VARIABLES ENTERING A* B CD EF G

1 Net Sales Index .41994 .41994 .17635 .17635 67.02 3.84 2 Gross Margin To Net Sales .47684 .05690 .22738 .05103 45.91 2.99 3 Net Sales To Advertising Expenditures .51387 .03703 .26407 .03669 37.20 2.60 4 Average Market Share .53218 .01831 .28322 .01915 30.62 2.37 5 Net Sales Index 46.90 Gross Margin To Net Sales 23.04 Net Sales To Advertising Expenditures 15.19 Average Market Share 7.45 Number o f Sales O utlets .53571 .00353 .28699 .0377 24.87 2.21 1.63 6 Net Sales Per Square Foot .53827 .00256 .28974 .00275 20.94 2.99 7 Gross Margin Per Employee .54335 .00508 .29523 .00549 18.37 2.01 8 Net Sales Per Employee .54786 .00451 .30015 .00492 16.40 1.94 9 Gross Margin Return On Inventory .54803 .00017 .30034 .00019 14.55 1.89

*A = The Multiple Correlation Coefficient B = The Incremental Change in the Multiple Correlation Coefficient C = The Squared M u ltip le C o rrelatio n C o e ffic ie n t (The C o e ffic ie n t of Determination) D = The Incremental Change in the Squared M u ltip le C orrelatio n C o e ffic ie n t E = The F-Test Value For the Multiple Correlation Coefficient F = The Significant F-Test Value at the 95 percent Confidence Level G = The F-Test Value for the Incremental Variance Explained by Each Predictor Variable 108 fo r the complete set o f nine measures is found to be .54803 and .30034.

Because the F-value for the multiple correlation coefficient found in column E exceeds the F-value fo r s ig n ifica n ce a t the n in e ty -fiv e percent confidence le v e l, found in column F, the two c o e ffic ie n ts are both s ta ­ tistically significant.

The incremental variance explained by the introduction of each of the nine variables Into the stepwise regression analysis and the statistical significance of the incremental contribution to explained variance is also presented in Table 4-5. Column D identifies the incremental explained variance for the Marketing Performance Variable Introduced in each of the nine steps of the stepwise regression analysis. The entry of the variables in the nine steps follows the order presented in Table

4-4. In addition, the statistical significance of each variable's incre­ mental contribution to explained variance can be ascertained by comparing columns F and Gj 1f the value in G is greate r than the value 1n F the contribution is statistically significant at the ninety-five percent level of confidence. Table 4-5 also reproduces the firs t step, step five, at which the entering Market Performance Variables has a sta tistica lly insignificant Incremental contribution to explained variance. The entire stepwise analysis of the Marketing Performance Variable is reproduced in

Appendix B,

The second hypothesis which must be inve stigate d in order to address research question two is stated as follows:

H?; Financial Structure Variables are not significant predictors of retail profitability. 109 Much like the consideration of hypothesis one, hypothesis two requires the simultaneous consideration of an entire set of variables in a step- wise regression analysis. In the case of hypothesis two, however, financial structure variables are analyzed in order to obtain three pieces of information: one, the order in which the individual Financial

S tructure Variables enter the s o lu tio n based on each v a ria b le ’ s a b ilit y to explain variance in the dependent measure; two, the amount and sta­ tistical significance of the total variance explained by the entire set of Financial Structure Variables; and three, the amount and statistical s ig n ific a n c e o f the incremental variance accounted fo r by each o f the

Individual Financial Structure Variables.

As is depicted in Table 4-3, two sets of Financial Structure Vari­ ables are considered. The firs t set 1s composed of the Financial Manage­ ment, L iq u id ity Management, Cash Flow Management, and Asset Management variable categories. The second set of Financial Structure Variables omits the Asset Management Variables. Table 4-6, Table 4-7, and Table

4-8 summarize the re s u lts o f the analysis o f the f i r s t set o f Financial

Structure Variables; Table 4-9, Table 4-10, and Table 4-11, the second set.

Table 4-6 identifies the order In which the twelve variables Included in the firs t set of Financial Structure Variables enter Into the stepwise regression analysis which utilizes return on total assets as the dependent measure. Table 4-7 provides the information relative to the statistical significance of the total variance explained by the firs t set of Financial

Structure Variables. Columns A and C respectively reveal the multiple correlation coefficient for the twelve variable set and the total vari­ ance explained by the same group of variables. For the Financial Structure-] 110 TABLE 4-6

THE ORDER OF ENTRY OF THE FINANCIAL STRUCTURE VARIABLES] IN THE STEPWISE REGRESSION ANALYSIS

ORDER OF ENTRY VARIABLE

1 NET WORTH TO CASH FLOW* 2 TOTAL LIABILITIES TO CASH FLOW* 3 DAYS PAYABLES OUTSTANDING* 4 TOTAL DEBT TO NET WORTH* 5 AVERAGE COLLECTION PERIOD* 6 CURRENT DEBT TO ENDING INVENTORY* 7 NET SALES TO CASH FLOW* 8 NET SALES TO ACCOUNTS RECEIVABLES* 9 GROSS PROFITS TO ACCOUNTS PAYABLE 10 NET SALES TO CASH + MARKETABLE SECURITIES

11 NET SALES TO ENDING INVENTORY

12 NET SALES TO WORKING CAPITAL**

*Variables whose incremental contribution to explained variance is statistically significant at a 95 percent level of confidence. **Variable did not enter into stepwise solution as F-Test value was 0. 000. m

TABLE 4-7

THE OBSERVED RELATIONSHIP BETWEEN FINANCIAL STRUCTURE] VARIABLES AND PROFITABILITY

VARIABLE A* B C D** E Net Worth to Cash Flow .29875 .08925 .08925 30.67 3.84 Total L ia b ilitie s To Cash Flow .40796 .16643 .07718 31.15 2.99 Days Payables Outstanding .44941 .20197 .03553 26.24 2.60 Total Debt To Net Worth .49494 .24466 .04300 25.14 2.37 Average Collection Period .52831 .27911 .03414 23.93 2.21 Current Debt to Ending Inventory .54000 .29160 .01249 21.13 2.09 Net Sales To Cash Flow .54538 .29744 .00584 18.57 2.01 Net Sales to Accounts Receivables .55133 .30396 .00652 16.70 1 .94 Gross Profits To Accounts Payable .55231 .30505 .00109 14.88 1 .885 Net Sales To Cash + Marketable S e cu ritie s .55331 .30615 .00111 13.41 1 .83 Net Sales To Ending Inventory .55362 .30649 .00034 12.17 1 .79

A = The Cumulative Multiple Correlation Coefficient B = The Cumulative Squared M u ltip le C o rre la tio n C o e ffic ie n t (The Coefficient of Determination) C * The Incremental Change in the Squared Multiple Correlation Coefficient D = The F-Test Value for the Multiple Regression Equation Containing the Corresponding Variable and the Previous Variables E = The F-Test Value for Significance at a 95% Confidence Level * * If the value in column D exceeds the value in column E, the multiple regression equation composed of the variable corresponding to that value plus all variables preceeding that variable is statistically significant. TABLE 4-8

THE STEPWISE ANALYSIS OF THE RELATIONSHIP BETWEEN FINANCIAL STRUCTURE VARIABLES] AND PROFITABILITY

VARIABLES ENTERING A* BC D E F G

NET WORTH TO CASH FLOW .55231 .00098 .30505 .00109 74.88 1.885 24.44 TOTAL LIABILITIES TO CASH FLOW 3.87 DAYS PAYABLES OUTSTANDING 25.45 TOTAL DEBT TO NET WORTH 25.93 AVERAGE COLLECTION PERIOD 13.67 CURRENT DEBT TO ENDING INVENTORY 3.73 NET SALES TO CASH FLOW 3.38 NET SALES TO ACCOUNTS RECEIVABLES 2.76 GROSS PROFITS TO ACCOUNTS PAYABLE .48

A = The Multiple Correlation Coefficient B = The Incremental Change in the Multiple Correlation Coefficient C = The Squared Multiple Correlation Coefficient (The Coefficient of Determination) D = The Incremental Change in the Squared Multiple Correlation Coefficient E = The F-Test Value for the Multiple Correlation Coefficient F = The F-Test Valuefor Significance at The 95 Percent Confidence Level G = The F-Test Value for the Incremental Variance Explained by Each Predictor Variable ,113

TABLE 4-9

THE ORDER OF ENTRY OF THE FINANCIAL STRUCTURE2VARIABLES IN THE STEPWISE REGRESSION ANA! YSIS

ORDER OF ENTRY VARIABLE

1 NET WORTH TO CASH FLOW* 2 TOTAL LIABILITIES TO CASH FLOW* 3 DAYS PAYABLES OUTSTANDING* 4 TOTAL DEBT TO NET WORTH* 5 AVERAGE COLLECTION PERIOD* 6 CURRENT DEBT TO ENDING INVENTORY* 7 GROSS PROFITS TO ACCOUNTS PAYABLE

♦Variables whose incremental contribution to explained variance is statistically significant at a 95 percent level of confidence. 114

TABLE 4-10

THE OBSERVED RELATIONSHIP BETWEEN FINANCIAL STRUCTURE- VARIABLES AND PROFITABILITY

VARIABLE A* BC D** E

NET WORTH TO CASH FLOW .29875 .08925 .08925 30.67 3.84 TOTAL LIABILITIES TO CASH FLOW .40796 .16643 .07718 31 .15 2.99 DAYS PAYABLES OUTSTANDING .44941 .201 97 .03553 26.24 2.60 TOTAL DEBT TO NET WORTH .49494 .24496 .04300 25.14 2.37 AVERAGE COLLECTION PERIOD .52831 .27911 .03414 23.93 2.21 ' CURRENT DEBT TO ENDING INVENTORY .54000 .29160 .01249 21 .13 2.09 GROSS PROFITS TO ACCOUNTS PAYABLE .54135 .29306 .00146 18.18 2.01

*A = The Multiple Correlation Coefficient B = The Squared Multiple Correlation Coefficient (The Coefficient of Determination) C = The Incremental Change in the Squared Multiple Correlation Coefficient **D = The F-Test Value for The Multiple Regression Equation E = The F-Test Value for Significance at a 95% Confidence Level * I f the value in column D exceeds the value in column E, the m u ltip le regression equation composed of the variable corresponding to that value plus all variables preceeding that variable is statistically significant. 115

TABLE 4-11

THE STEPWISE ANALYSIS OF THE RELATIONSHIP BETWEEN FINANCIAL STRUCTURE2VARIABLES AND PROFITABILITY

STEP VARIABLES ENTERING A* B C D E F G

Net Worth to Cash .54135 .00135 .29306 .00146 18.18 2.01 24.24 Flow Total Liabilities To Cash Flow 14.25 Days Payables Outstanding 25.51 Total Debt to Net Worth 24.60 Average Collection Period 16.19 Current Debt To Ending Inventory 4.58 Gross P ro fits To Accounts Payable 0.64

A = The Multiple Correlation Coefficient B = The Incremental Change in The Multiple Correlational Coefficient C = The Squared Multiple Correlation Coefficient (The Coefficient of Determination) D = The Incremental Change in The Squared M u ltip le C o rre la tio n C o e ffic ie n t E = The F-Test Value For The Multiple Correlation Coefficient F = The F-Test Value for Significance at The 95 Percent Confidence Level G = The F-Test Value for The Incremental Variance Explained by Each Predictor Variable 116 Variables, the multiple correlation is .55362 and the amount of variance explained is .30649. Both are statistically significant at the ninety- five percent confidence level as evidenced by the fact that the F-value for the two measures, which is presented in column D, is greater than the F-test value for significance at the ninety-five percent level of confidence, which is presented in column E.

The incremental variance explained by the introduction of each of the twelve variables in the Financial Structure^ set is also identified in

Table 4-8. Column C presents the information. The statistical signifi­ cance of each variable's incremental contribution to explained variance is examined in Table 4-9. In Table 4-9, the firs t step where the incre­ mental contribution to explained variance of the Financial Structure^ vari­ able entering the solution at that point is insignificant is presented.

The statistical significance, again at the ninety-five percent level of confidence, can be ascertained by comparing the F-test value for signi­ ficance which is presented in column F to the F-value for the incremental contribution of the individual variable which is presented in column G.

I f the value o f column G exceeds the value in column F, the incremental contribution of the corresponding variable is statistically significant at the ninety-five percent level of confidence. The entire stepwise regression analysis procedure for the Financial Structure^ Variables is produced as Appendix C.

The analysis for the second set of Financial Structure Variables is identical to what has just been presented for the Financial Structure^ set. Table 4-9 identifies the order of entry of the variables into the stepwise regression analysis. The statistical significance of the R and 117 2 R value for the variable set is examined in Table 4-10. The multiple correlation of .54135 and the coefficient of determination of .29306 are both shown to be statistically significant at the ninety-five per­ cent confidence level as the F-value fo r the two measures presented in column D, is greater than the F-test value for significance, presented in column E. Table 4-11 presents the f i r s t step where the incremental contribution to explained variance of the Financial S tructu^ variable entering the solution is insignificant. The statistical significance at the ninety-five percent confidence level can be ascertained by comparing the F-test value for significance which is presented in column F to the

F-value for the incremental contribution of the individual variable which is presented in column G. When the value in column G exceeds the value in column F, the Incremental contribution of the variable is sta­ tistica lly significant at the ninety-five percent confidence level. The entire stepwise regression analysis procedure for the Financial S tructu^

Variables is reproduced as Appendix D.

The final step in answering research question two is the consider­ ation of the following hypothesis:

H3; Marketing Performance Variables are not better predictors of retail profitability than Financial Structure Variables.

However, the treatment of hypothesis three 1s complicated by the fact that there is no parametric test to ascertain whether the variance explained by one set of variables 1s greater than the variance accounted for by a second set. The basic reason for the problem is the fact that the traditional technique for testing whether one variable correlates more h ig h ly w ith a dependent measure than a second v a ria b le , the t - t e s t , 118 requires the determination of the level of correlation which exists between the two independent measures,10 When the two variables under con­ sideration are composed of multiple measures, there is no means available to c a lcu la te the c o rre la tio n between the two sets o f va ria b le s.

Therefore, a two stage process is employed in the attempt to in v e s ti­ gate hypothesis three without the aid of a direct means to access the statistical significance of the difference in the variance accounted for by each of the two variable sets. First, a stepwise multiple regression analysis and the nonparametric Wllcoxon Test are employed. Secondly, a hierarchical multiple regression procedure is utilized in an effort to validate the nonparametric findings.

Thus, the firs t step in the analysis of hypothesis three is a step­ wise regression analysis employing the entire research set of twenty-one independent variables as identified in Table 4-12. In the regression analysis the twenty-one variables are a ll included in the procedure with­ out d iffe re n tia tio n based upon membership in e ith e r the Financial S truc­ ture or Marketing Performance classifications. In so doing, the twelve variables which are listed in Table 4-12 each explain an Incremental amount of variance which is statistically significant at the ninety-five percent level of confidence. Table 4-12 also identifies the cumulative correlation between the twelve significant independent variables and the dependent measure, return on total assets; the cumulative explained vari­ ance; the incremental variance explained by each of the twelve; and the

10Jacob Cohen and P a tric ia Cohen, Applled M u ltip le Regression/Cor­ relation Analyses for the Behavioral Sciences (New York City: John Wiley & Sons, 1975), p. 53. 119 relevant F-statistic for testing the statistical significance of the cumu­ lative and incremental explained variance. Appendix E presents the entire regression procedure.

Next the twelve measures found to be s ig n ific a n t are accumulated in to either the Financial Structure or Marketing Performance variable classi­ fic a tio n based on the memberships established in Table 4-3, and the rank order of entry for each measure's introduction into the stepwise multiple regression analysis and the amount of incremental variance explained by the two major classifications is determined. Thus Table 4-13 presents the information necessary for the performance of the Wllcoxon test, which b a s ic a lly compares the mean rank order o f e n try fo r the two categories.

As is shown 1n Table 4-13, the difference in the mean rank of the Financial

Structure^ Variables and the Marketing Performance Variables is s ta tis ti­ cally significant at the 92.6 percent level of confidence: the differ­ ence in the Financial Structure2 and Marketing Performance variable classification is shown to be statistically significant at the 91,1 per­ cent level of confidence.11

The second step in the analysis of hypothesis three requires the use of a hierarchical regression procedure. Basically, the two variable sets are compared through the use of hierarchical regression which deter­ mines whether one group when added to a second group of variables explains an incremental amount of variance which is statistically

11 For table values required for significance see Leonard A. Maras- culo and Maryellen McSweeney, Nonparametric and P istri bution-Free Methods for the Social Sciences (Monterey, California; Brooks/Cole Publishing Company, 1977), p p .504-508, 120

TABLE 4-12

THE SIGNIFICANT PREDICTORS IDENTIFIED WHEN ALL THE RESEARCH VARIABLES ARE CONSIDERED SIMULTANEOUSLY

VARIABLE ENTERING A* B C D E F G

NET SALES INDEX .41994 .41994 .17635 .17635 67.02 58.48 3.87 TOTAL DEBT TO NET WORTH .50856 .09862 .25864 .08229 54.42 20.61 2.99 GROSS MARGIN TO NET SALES .60159 .09303 .36191 .10327 58.80 28.65 2.60 ADVERTISING EXPENDI­ TURES TO NET SALES .62733 .02574 .39354 .03163 50.29 12.05 2.37 AVERAGE COLLECTION PERIOD .65522 .02789 .42932 .03578 46.49 32.22 2.21 AVERAGE MARKET SHARE .67174 .01652 .45123 .02191 42.21 11.19 2.09 NET SALES TO CASH + MARKETABLE SECURITIES .67829 .00655 .46007 .00884 37.37 3.88 2.01 DAYS PAYABLES OUT­ STANDING .68361 .00532 .46733 .00726 33.5b 4.77 1.94 TOTAL LIABILITIES TO CASH FLOW .68853 .00492 .47407 .00674 30.55 2.49 1.885 NET WORTH TO CASH FLOW .69697 .00844 .48577 .01170 28.72 13.40 1.83 NET SALES TO CASH FLOW .70197 .00500 .49276 .00698 26.76 4.97 1.79 GROSS MARGIN PER EMPLOYEE .70719 .00522 .50012 .00736 25.18 4.45 1 .75

*A = Cumulative Multiple Correlation Coefficient B = Incremental Change in the Cumulative Multiple Correlation Coefficient C = The Cumulative Squared M u ltip le C o rre la tio n C o e ffic ie n t (The C o e ffic ie n t of Determination) D = The Incremental Change in the Cumulative Squared Multiple Correlation Coefficient E = The F-Test Value for The Statistical Significance of the Cumulative Multiple Correlation Coefficient at a 95 percent Confidence Level F = The F-Test Value For the Incremental Variance Explained by each predictor in the last iteration of the stepwise procedure where the increment was significant at the .95 level (Significant F-value = 1.75) G = The F-Test Value for a .95 Significance Level for the Cumulative Variance Explained TABLE 4-13

THE AMOUNT OF INCREMENTAL VARIANCE EXPLAINED BY THE SIGNIFICANT FINANCIAL STRUCTURE AND MARKETING PERFORMANCE VARIABLES

COMPARISON 1: FINANCIAL STRUCTURE-! VARIABLES MARKETING PERFORMANCE

VARIABLE NAME Incremental R Rank Order VARIABLE NAME Incremental Rank Order

Total Debt To Net Sales Index .17635 1 Net Worth .08228 3 Gross Margin To Averaae Collec­ Net Sales .10327 2 tion Period .03578 A Advertising Ex­ Net Sales To penditures To Cash Marketable Net Sales .03163 5 Securities .00884 8 Average Market Days Paybles Share .02191 6 Outstanding .00726 10 Gross Margin Per Total Liabilities Employee .00736 JL To Cash Flow .00674 12 Totals .34052 23' Net Worth To Cash Flow .01170 7 Net Sales To Cash Flow .00698 11 Totals .15959 55 TABLE 4-13 (continued)

COMPARISON 2: FINANCIAL STRUCTURE?VARIABLES MARKETING PERFORMANCE VARIABLES 2 VARIABLE NAME Incremental R Rank Order VARIABLE NAME Incremental R2 Rank Order

Total Debt To Net Sales Index .17635 1 Net Worth .08228 3 Gross Margin To Average Collec­ Net Sales .10327 2 tion Period .03578 4 Advertising Expendi­ Days Payables tures to Net Sales .03163 5 Outstanding ' .00726 9 Average Market Share .02191 6 Total Liabilities Gross Margin Per To Cash Flow .00674 11 Employee .00736 _ 8 * Net Worth To Cash Totals .34052 7 2 * Flow .01170 7 Net Sales to Cash Flow .00698 10 Totals .15075 44

Statistically significant at a 92.6 percent level of confidence. 2 Statistically significant at a 91.1 percent level of confidence. 123 significant after the variance explained by the firs t variable group is partialed out.12 Table 4-14 summarizes the hierarchical analysis of the significance of the incremental variance explained by the Marketing Per­ formance Variables when added to the Financial Structure Variables indi­ cating that the Marketing Performance Variables do explain a significant amount of additional variance when the effects of the Financial Struc­ ture Variables are partialed out.

The Relative Impact of The Six SubCTassificatlOns of financial Structure and Marketing Performance On P ro fita b ility

The third section of Chapter 4 1s directed towards the investigation of the third research question set forth in Chapter 1, which 1s stated as follows:

Q,: Which of the six variable classifications have a significant correlation with retail profitability?

Specifically, research question three requires the analysis of hypothesis a

which is:

H,: The amount of variance explained by the varia bles used as surrogate measures fo r each o f the s ix va ria b le types is not significantly greater than zero.

In addition, the present section examines the relative relationship of each o f the s ix va ria b le c la s s ific a tio n s by comparing the variance explained by each set o f measures.

The consideration of research question three thus takes place in two stages: firs t, hypothesis 4 is investigated through the use of

12Cohen and Cohen, pp. 127-129. 124

TABLE 4-14

THE HIERARCHICAL COMPARISON OF THE FINANCIAL STRUCTURE AND MARKETING PERFORMANCE VARIABLES

ENTRY ORDER VARIABLE SET CUMULATIVE RL F-VALUE d f F-SIGNIFICANCE

COMPARISON ONE

1 Financial S tructure ] .30649 2 Marketing Performance .51303 13.107* 12,293 2.252

COMPARISON TWO

1 Financial S tru ctu re 2 .29875 2 Marketing Performance .50055 13.333* 8,297 2.571

F-value = the calculated F-test value for the statistical significance of the incremental variance explained by the second variable set df = degrees of freedom F-Significance = the F-test value required for statistical significance at the 95 percent confidence level * If F-Value exceeds F-Significance, the incremental variance explained by the introduction of the Marketing Performance Variables is statistically siqnificant at the 95 percent level of confidence **For explanation of the statistical test see Jacob Cohen and Patricia Cohen, Applied Multiple Regression/Correlation/Analysis for the Behavioral Sciences (New York City: John Wiley & Sons, 1975), pp. 135-138. stepwise multiple regression; and then, stepwise hierarchical regression

is used to analyze the relative amount of variance explained by each of

the six subclasslflcatlons of Financial Structure and Marketing Performance

va ria b le s.

Stage one of the analysis 1s performed 1n two phases; firs t, stepwise

multiple regression 1s used to consider the Incremental variance explained

by each va ria b le when a ll the twenty-one measures belonging to the s ix

subclassifications are considered simultaneously. The total variance

explained by each o f the s ix s u b c la s s lflc a tlo n s is then determined by

sunmlng the incremental variance explained the measures belonging to each

of the six categories. The statistical significance of the Incremental

variance explained by each group is then ascertained through the use of

the F-test.13 Table 4-15 identifies the amount of variance explained by

each of the six subclassifications and the twelve individual measures which are accounting for a statistically significant amount of incremental

explained variance. Table 4-16 identifies the order of entry of the

twelve significant variables, and Table 4-17 reveals the three subclassi­

fications which explain a statistically significant amount of the variance

in the dependent measure, return on total assets. The three subclassifi­

cations which are statistically significant in terms of the amount of

incremental variance explained are the Financial Management, Marketing

Management, and Margin Management categories. The L iq u id ity , Cash Flow,

and Asset Management subclassifications dd not explain a statistically

significant amount of Incremental explained variance.

1 The F-test formulation 1s presented as Appendix F. TABLE 4-15

THE AMOUNT OF INCREMENTAL VARIANCE EXPLAINED BY EACH OF THE SIX SUBCLASSIFICATIONS OF THE MARKETING PERFORMANCE AND FINANCIAL STRUCTURE VARIABLES

FINANCIAL MANAGEMENT JT LIQUIDITY MANAGEMENT Total Debt To Net Worth .08228* Number of Days Payables Outstanding .00726* Average C ollection Period .03578* Gross P ro fits to Accounts Payable .00037 Total .10969 Current Debt To Ending Inventory .00093 Total .01693

CASH FLOW MANAGEMENT JT ASSET MANAGEMENT Net Worth To Cash Flow .01170* Net Sales To Cash + Marketable Total Liabilities To Cash Flow .00674* Securities .00884* Net Sales To Cash Flow .00699* Net Sales To Accounts Receivables .00006 Total .02543 Net Sales To Working Capital .00251 Net Sales To Ending Inventory .00093 Total .01288

MARKETING MANAGEMENT j r MARGIN MANAGEMENT Market Share Average .02191* Gross Margin To Net Sales .10327* Net Sales Index .17635* Gross Margin Per Employee .00736* Advertising Expenditures To Net Sales .03163* Gross Margin Return On Inventory .00008 Net Sales Per Employee .00146 Total .11071 Net Sales-Per Square Foot o f S elling Space .00032 Number o f Sales O utlets .00159 Total .24713

♦Variables achieved significance at the 95 percent level of confidence. TABLE 4-16

THE ORDER OF ENTRY OF THE SIGNIFICANT INDEPENDENT VARIABLES IN THE OVERALL STEPWISE MULTIPLE REGRESSION ANALYSIS

OF ENTRY VARIABLE 1 NET SALES INDEX 2 TOTAL DEBT TO NET WORTH 3 GROSS MARGIN TO NET SALES 4 NET SALES TO ADVERTISING EXPENDITURES 5 AVERAGE COLLECTION PERIOD 6 AVERAGE MARKET SHARE 7 NET SALES TO CASH + MERCHANDISE SECURITIES 8 DAYS PAYABLES OUTSTANDING 9 TOTAL LIABILITIES TO CASH FLOW 10 NET WORTH TO CASH FLOW 11 NET SALES TO CASH FLOW 12 GROSS MARGIN PER EMPLOYEE 1?P TABLE 4-17 THE STATISTICAL SIGNIFICANCE OF THE VARIANCE EXPLAINED BY THE SIX VARIABLE SUBCATEGORIES WHEN ALL ARE CON­ SIDERED SIMULTANEOUSLY

VARIABLE SUBCATEGORY R2 F-VALUE F-STATISTIC FINANCIAL MANAGEMENT .110 19.28* 4.88 LIQUIDITY MANAGEMENT .017 1.79 3.85 CASH FLOW MANAGEMENT .025 2.66 3.85 ASSET MANAGEMENT .013 1.03 3.38 MARKETING MANAGEMENT .233 15.89* 2.86 MARGIN MANAGEMENT .111 12.94* 4.68

* = The variable subcategories incremental contribution to explained variance is statistically significant at the ninety-five percent level of confidence. R = The coefficient of determination for the respective variable set and the dependent measure; return on total assets. See Table 4-15 for calculation. 2 F-value = The calculated F-value for the amount of variance explained (R ) by each set of variables. See Appendixes for explanation of calculation. F-statistic = The value which the calculated F-value must exceed if the amount of variance explained (R2) by the respective variable set is to be statistically significant at the ninety-five percent confidence level 129 The second phase o f the f i r s t p a rt o f the analysis o f research question three again employs stepwise multiple regression, but each of the six subclassification’s relationship with the dependent measure 1s considered separately. Specifically, six separate stepwise multiple regression analyses are used; oiie for each of the six subclassifications.

The statistical significance of the variance explained by each of the six subclassifications, independent of the others. Is again considered through the use of the F-test.11+

Table 4-18 id e n tifie s the amount o f variance explained by each sub­ c la s s ific a tio n and each measure w ith in the s u b c la s s ific a tio n , when each of the six individual groups of variables are considered individually.

Table 4-18 also id e n tifie s the nine measures which explain a s ta t is t ic a lly significant amount of variance within the six separate analyses of the subclassifications. Table 4-19 then reveals that only the Liquidity

Management subclassification fa ils to explain an amount of variance signi­ ficantly greater than zero when the six subclassifications of variables are considered separately.

The second stage o f the analysis o f research question three uses hierarchical regression to investigate the relative amounts of variance explained by each of the six subclassifications. Essentially hierarchical regression analysis is a system of determining partial correlation coefficients; a second set is added to a firs t, and the statistical sig­ n ific a n c e o f the a d d itio n a l variance explained by the second w ith the

1<4The F-test formulation is presented as Appendix F, TABLE.4-18

THE TOTAL VARIANCE ACCOUNTED FOR BY EACH OF THE SIX VARIABLE SUBCLASSIFICATIONS WHEN CONSIDERED INDIVIDUALLY

FINANCIAL MANAGEMENT R_ LIQUIDITY MANAGEMENT Total Debt To Net Worth .08137* Number o f Days Payables Outstanding .01453* Average C ollection Period .00070 Gross P ro fits To Accounts Payable .00105 Total ~M2UT Current Debt To Ending Inventory .00012 Total CASH FLOW MANAGEMENT .01570 Net Worth To Cash Flow .08925* ASSET MANAGEMENT Total Liabilities To Cash Flow .07718* Net Sales To Cash + Marketable Secur­ Net Sales To Cash Flow .00332 itie s .00731 Total .16975 Net Sales To Accounts Receivables .02935* Net Sales To Working Capital .00030 MARKETING MANAGEMENT Net Sales To Ending Inventory .00951 Market Share Average .01886* Total .04647 Net Sales Index .17635* Advertising Expenditures To Net Sales .03759* MARGIN MANAGEMENT Net Sales Per Employee .00066 Gross Margin To Net Sales .10719* Net Sales Per Square Foot o f S ellin g Space .00269 Gross Margin Per Employee .00742 Number of Sale Outlets .00138 Gross Margin Return On Inventory 0 Total .23753 Total .11461

©variable would not enter due to statistical tolerance level being too low. *variables achieve significance at the 95 percent level of confidence 131

TABLE 4-19

THE STATISTICAL SIGNIFICANCE OF THE VARIANCE EXPLAINED BY THE SIX VARIABLE SUBCLASSIFICATIONS WHEN EACH IS CONSIDERED SEPARATELY

VARIABLE SUBCATEGORY R2 F-VALUE F-STATISTIC

FINANCIAL MANAGEMENT .082 13.95* 4.68 LIQUIDITY MANAGEMENT .016 1.65 3.85 CASH FLOW MANAGEMENT .170 21.19* 3.85 ASSET MANAGEMENT .046 3.78* 3.38 MARKETING MANAGEMENT .238 15.99* 2.86 MARGIN MANAGEMENT .115 20.19* 4.68

* = The variable subcategories contribution for explained variance is statistically significant at the ninety-five percent level of confidence. O R = The coefficient of determination for the respective variable set and the dependent measure; return on total assets. o F-value = The calculated F-value for the amount of variance explained (R ) by each set o f v a ria b le s. F-statistic = The value which the calculated E-value must exceed if the amount of variance explained (R ) by the respective variable set is to be statistically significant at the ninety-five percent confidence level. 132 effect of the firs t set partialed out is determined,15 In the present study the criteria for establishing the order of entry is the magnitude of the variance explained by each of the variable sets, which 1s identified in Table 4-19, The order is:

1, Marketing Management 2, Cash Flow Management 3. Margin Management 4. Financial Management 5, Asset Management 6. L iq u id ity Management

Table 4-20 presents the results of the second stage analysis.

Basically, comparison one in the Table seeks to ascertain whether the addition of each successive group of variables explainsan incremental amount of variance which 1s statistically significant at the ninety- five percent confidence level. Specifically, the F-value of 20.16 is a measure of the statistical significance of the Incremental variance explained by the Cash Flow Management Variables when th a t group is added to the Marketing Management Variables; the 9,13 is a measure of the statistical significance of the incremental variance explained by the addition of just the Margin Management Variables to the Marketing Man­ agement Variables; and so on. As can be seen from Table 4-20, only the

Liquidity Management Variables fa ll to explain a significant amount of incremental variance when compared with the Marketing Management Variables which independently explain the greatest amqunt o f variance among the six variable subclassifications. Appendix G summarizes the F-test calculations for the firs t comparison.

15Cohen and Cohen, p. 98. 1T3

TABLE 4-20

THE HIERARCHICAL ANALYSIS OF THE INCREMENTAL VARIANCE EXPLAINED BY THE SIX SUBCLASSIFICATIONS

COMPARISON 1: CFM MM FM AM LM MKM 20,16* 9,13 20. 99 3,11 1.03

SIGNIFICANCE VALUE 2.64 2.64 3. 03 2.40 2.64

COMPARISON 2 :**

MM FM AM LM MKM+CFM 9.66

MKM+CFM+MM 21.86

MKM+CFM+MM+FM 1.49

MCM+C FM+MM+FM+AM 1.78

SIGNIFICANCE VALUE 2.64 3.03 2.40 3.03

MKM = Marketing Management Variables CFM = Cash Flow Management Variables MM = Margin Management Variables FM = Financial Management Variables AM = Asset Management Variables LM = L iq u id ity Management Variables Significance Value = The minimum F-value for statistical significance at the 95 percent level of confidence * = the numbers in the box represent the F-values for the comparison of the incremental variance explained by the variable listed on the horizontal axis when it is added to the variables listed on the vertical axis **= calculations presented in Appendix H 134 The R eliability of The Significant Predictor Variables Over time

The fourth section of Chapter four details, in part, the investiga­ tion of the fourth research question outlined in Chapter 1. For review research question four is stated as follows:

Q.: Can a profile or composite of Financial Structure and Marketing Performance variables, which 1s significantly correlated with profitability and stable over time, be Identified? Over pricing strategies? Over geographic diversification strategies? Over line of trade strategies?

Specifically, the in itia l phase of research question four requires the analysis of hypothesis five which is:

H5: The profile of slgnficant predictor variables Is not stable over time.

Hypothesis five 1s examined 1n two parts, but firs t the profile of sig­ nificant predictors must be identified. Table 4-18 identifies the nine

Individual ratios which explain a significant amount of incremental vari­ ance w ith in each o f the s ix va ria b le s u b c la s s ific a tio n . By sub jecting each of the six subclassifications to an Independent stepwise multiple regres­ sion analysis, only the variables which account for a substantial portion of the variance in profitability within the particular subclassification to which the variable belongs are Identified. Thus, variables within the same subclassification which explain overlapping variance are eliminated.

Based upon the analysis presented in Table 4-18 the variables which constitute the Significant Predictor Profile are identified in Table

4-21. TABLE 4-21

THE SIGNIFICANT PREDICTOR PROFILE

1. GROSS MARGIN TO NET SALES 2. TOTAL DEBT TO NET WORTH 3. DAYS PAYABLES OUTSTANDING 4. NET SALES TO ACCOUNTS RECEIVABLES 5. NET WORTH TO CASH FLOW 6. TOTAL LIABILITIES TO CASH FLOW 7. NET SALES INDEX 8. AVERAGE MARKET SHARE 9. ADVERTISING EXPENDITURES TO NET SALES

The stability of the Significant Predictor Profile is then investi- gated in three phases. First, the amount of variance explained by the p r o file is examined fo r sig n ifica n ce in each o f the three time segments.

Table 4-22 reveals the variance explained in each of the three time seg­ ments which re sp e ctive ly have F-values o f 7,70, 6.37, and 18.13. The f i r s t

two segments have degrees o f freedom equal to 9 and 95, w hile the th ir d

has 8 and 96 degrees of freedom. For the firs t two time segments, the F-

value necessary for statistical significance at the 95 percent probability

level is 1.985; for the third segment, 2.034, Thus, the variance explained

by the Significant Predictor Profile is statistically significant over the

three time segments.

The second phase o f the In v e s tig a tio n o f hypothesis fiv e s p e c ific a lly

considers the individual variables within the Significant Predictor Pro­

file to determine which ones consistently explain a statistically signi­

ficant portion of the incremental variance across the three time segments.

Table 4-23 reveals that only the Total Debt to Net Worth variable achieves

statistical significance in all three time periods. The Gross Margin To

Sales, Average Market Share, and Advertising To Net Sales variables are

significant in two of the three time periods. TABLE 4-22

PREDICTOR RELIABILITY OVER TIME

Time-]4 Tirr^S Tim e^

VARIABLES R21 Rank2 R3 R21 Rank2 R3 R21 Rank2 R3 1. Gross Margin To Net Sales .158* 2 .360 . 021 ♦ 5 .172 .000 7 .391 2. Total Debt To Net Worth .174* 1 -.417 .054* 3 -.396 .010* 4 - .191 3. Days Payables Outstanding .010 6 -.044 .000 9 -.139 .026* 5 .352 4. Net Sales To Account Receivables .001 9 .190 .002 8 .099 .000 8 .273 5. Net Worth To Cash Flow .001 7 .239 .007 6 .239 .074* 2 .465 6. Total Liabilities To Cash Flow .001 8 -.095 .013 7 -.212 .085* 3 .089 7. Net Sales Index .040 5 .211 .167* 1 .410 .402* 1 .634 8. Average Market Share . 017 * 4 -.205 .041* 4 .279 .004 6 .155 9. Advertising Expenditures To Net Sales .044* 3 -.222 .071* 2 -.351 DNE Total Variance Explained .422** .376** .602**

♦Incremental variance explained is statistically significant at the 95 percent level. ♦♦Column totals may not sum exactly due to rounding error. 1 2 R is the incremental variance explained by the variable's introduction into the stepwise multiple 2 regression analysis incorporating all nine variables. Rank is the relative order in which each variable is introduced into the stepwise multiple regression .s o lu tio n . ?R is the simple regression coefficient for each variable. rTime 1 incorporates a ll data fo r the years 1970, 1971, 1972. gTime 2 incorporates a ll data fo r the years 1973, 1974, 1975. Time 3 incorporates a ll data fo r the years 1976, 1977, 1978. TABLE 4-23

A SUMMARY OF THE RELIABILITY OF THE SIGNIFICANT PREDICTOR VARIABLES P P VARIABLES w 3 12 ¥ 2 G1G2G3 L1L2L3 zs °:S 1. Gross Margin To Net Sales S S N N S N N S S N S S N 7 54 2. Total Debt To Net Worth s s s S S s s S S S s s s 13 100 3. Days Payables Outstanding N N S N S s N S S S s N S 8 62 4. Net Sales To Accounts Receivables N N N N S S N N S N N N S 4 31 5. Net Worth To Cash Flow N N S S N S N N S N N N N 4 31 6. Total Liabilities To Cash Flow N N S S s S N N S N N N N 5 38 7. Net Sales Index N S S S S S N S S S S N S 10 77 8. Average Market Share S S N s s s s S S N N S N 9 69 9. Advertising Expenditures To Net Sales S S N s s N S S S S S s S 11 85

S = Statistically significant incremental contribution to explained variance at the 95 probability level N = Non statistically significant incremental contribution to explained variance at the 95 percent probability level T] = Sample subset o f years 1970, 1971, 1972 T2 = Sample subset o f years 1973, 1974, 1975 T3 = Sample subset o f years 1976, 1977, 1978 P} = Subset o f the sample observations where return on to ta l asset is above the median = Subset o f the sample observations where return on to ta l assets is below the median R] = Subset of the sample observations where the gross margin to net sales is above the median R2 = Subset o f the sample observations where the gross margin to net sales is below the median G-j = Subset o f the sample firm s where a ll r e ta il o u tle ts are located in two or fewer states G2 = Subset o f the sample firm s where a ll re ta il o u tle ts are located in three to ten states G3 = Subset of the sample firms where all retail outlets are located in eleven or more states L-| = Subset of the sample firms consisting of convenience store operations and related support fa cilitie s L2 = Subset o f the sample firm s consisting o f firm s operating supermarkets and related support f a c ilit ie s L3 = Subset o f the sample firm s consisting o f d iv e rs ifie d food companies; those engaged in both the retail food industry and non-food related businesses zS = number of times variable is found to be a significant predictor °^S = percentage of time variable is found to be a significant predictor.

U> vj 138 Phase three of the investigation of hypothesis five employs the

Friedman Test to consider the relative rank orders of the variables within the Significant Predictor Profile over the three time segments. The analysis, which is presented in Table 4-24, indicates that the mean ranks o f each varia ble over the three time segments are s ig n ific a n tly d iffe re n t from variable to variable.

The R eliability of The Significant Predictor Variables Over Firms of Differing Profit Performance

The present section continues the investigation of the fourth research question through the analysis of hypothesis 6i which is as follows: '

Hg; The profile of significant predictors does not d if f e r between more and less p ro fita b le firm s .

Again, the same three phased approach as was used in the immediately pre­ ceding section is utilized. However,first the data base 1s divided into two halves, using the median return on to ta l assets as the d iv id in g p o in t.

In order to have equal samples, the median observation was not Included in either sample half. The Significant Predictor Profile Is the same as is presented in Table 4-21.

Step one in the investigation of hypothesis 6 involves the examina­ tion of the amount of variance accounted for by the Significant Predictor

Profile in each of the profitability halves. As Is Indicated in Table 4-25, the nine variable set explains 49.2 percent of the tQtal variance in the upper-half profitab ility subsample and 24.6 percent 1n the lower half.

Respectively the F-values for the two figures are 13,64 and 4.61. With 9 and 127 degrees of freedom, both are significant at the ninety-five percent 139 TABLE 4-24

THE FRIEDMAN TEST OF HYPOTHESIS 5

The Significant Predictor Profile

V1 V2 V3 V4 V5 V6 V7 V8 V9 T1 2 1 6 9 7 8 5 4 3

T2 5 3 9 8 6 7 1 3 2

7 4 5 8 2 3 1 6 9

Rk 14 8 20 23 15 18 7 14 14 Ry 196 64 400 529 225 324 49 196 196

H v 1 = V 0 = V „ = 2 *3 "4 V5 " V6 " '7 V8 ’ 8 = *9 HA: viV] t uV2 f UV3 f pV4 f yV5 * yVg * yV? j* yVg * yVg k 12 Test: Q = R^ - 3n(k+l); n=3, k=9 nK (k+l) 1 k=l

Q = 6.84 df = 2; X 5.99 2 d f,.05

T, = Incorporates data for the years 1970, 1971, 1972 Tp = Incorporates data for the years 1973, 1974, and 1975 T^ = Incorporates data for the years 1976, 1977, 1978 V , = Gross Margin To Net Sales V2 = Total Debt To Net Worth V3 = Days Payables Outstanding V* = Net Sales To Accounts Receivables Vf- = Net Worth To Cash Flow V fi = Total Liabilities To Cash Flow Vy = Net Sales Index Vg = Average Market Share V q = Advertising Expenditures To Net Sales Rj\ = The Sum of the ranks for variable across the three years yV^ = The mean rank o f va ria b le k

= number in the individual cells represent the rank order of entry of the va ria b le indicated in the time segment designated 140 probability level in that the values exceed the critical F-value for the degrees of freedom of 1.96. Table 4-25 summarizes the procedure.

Phase two of the analysis of hypothesis 6 again seeks to identify the individual variables within the Significant Predictor Profile which explain a s ig n ific a n t amount o f the incremental variance in each of the two profitab ility subsamples. As is Indicated by Table 4-23, the following five variables individually explain a significant portion of the incre­ mental variance in each of the subsamples:

1. Total Debt To Net Worth 2. Total Liabilities To Cash Flow 3. Net Sales Index 4. Average Market Share 5. Advertising Expenditures To Net Sales

The third stage in the analysis of hypothesis 6 utilizes the Friedman

Test to determine that the relative rank order of the variables within the

S ig n ific a n t P re d icto r P ro file d if f e r between subsamples as shown in Table 4-26.

The R eliability of The Significant Predictor Variables Across Pricing strategies'

The present section begins the investigation of hypothesis seven which for review is stated as follows:

H^: The profile of significant predictors does not d if f e r between d iffe re n t managerial strategies.

The firs t strategy investigated is pricing strategy, for which the sample is divided in to equal halves based on the median gross margin o f the firm. A three stage analysis 1s again employed beginning with an investi­ gation of the variance explained by the significant predictor profile in each of the sample halves. Again, the median firm is omitted to insure equal halves. TABLE 4-25

PREDICTOR RELIABILITY OVER PROFITABILITY GROUPS

Upper-Half^ Lower-Half5

VARIABLES r 21 Rank? R3 R21 Rank2 R3

1. Gross Margin To Net Sales .000 9 .007 .092* 1 .303 2. Total Debt To Net Worth .389* 1 -.623 .018* 5 -.153 3. Days Payables Outstanding .005 7 -.198 .034* 4 .173 4. Net Sales To Accounts Receivables .002 8 .081 .020* 3 .077 5. Net Worth To Cash Flow .016 5 .198 .001 9 .162 6. Total Liabilities To Cash Flow .020* 4 -.333 .029* 6 .165 7. Net Sales Index .023* 2 .203 .013* 8 .162 8. Average Market Share .023* 3 .130 .027* 2 .221 9. Expenditures To Net Sales .013* 6 -.038 .013* 7 -.053 Total Variance Explained .492** .246**

♦Incremental variance explained is statistically significant at the 95 percent level |*Column totals may not sum exactly due to rounding error R^ is the incremental variance explained by the variables introduction into the stepwise multiple

2 regression analysis incorporating all nine variables Rank is the relative order in which each variable is introduced into the stepwise multiple regression -so lu tio n .R is the simple regression coefficient for each variable The upper-half sample incorporates the data from a ll observations where the p r o fit performance is 5above the median p r o fit performance, as measured by the return on total assets The lower-half sample incorporates the data from a ll observations where the p r o fit performance is below the median p r o fit performance, as measured by the return on total assets 142

TABLE 4-26

THE FRIEDMAN ANALYSIS OF HYPOTHESIS 6

The Significant Predictor Profile

V1 V2 V3 V4 V5 V6 V7 V8 V9

9 1 7 8 5 4 2 3 6

1 5 4 3 9 6 8 2 7 i Rk 10 6 11 11 14 10 10 5 13

Rk 100 36 121 121 196 100 100 25 169

Hq: yV-j = yV 2 = yVg = yV^ = yV,- = yVg = yV^ = yVg = yVg

Ha : V, f V2 i V3 f v„ t v 5 * V6 t V7 t v8 # V9

Test: Q ’ n.kfk+l5 K~1 * 3o(k+1); nz.k = 9

Q = 4.86 df > 1; X2ld f__05 . 3.84

P, = Incorporates data for those observations where the profitability as measured by re turn on to ta l assets is above the median P2 = Incorporates data for those observations where the profitability as measured by re tu rn on to ta l assets is below the median V, = Gross Margin To Net Sales v l = Total Debt To Net Worth vi = Days Payables Outstanding V. = Net Sales To Accounts Receivables V, = Net Worth To Cash Flow Vfi = Total Lia bilities To Cash Flow Vy = Net Sales Index Vo = Average Market Share Vq = Advertising Expenditures To Net Sales R. = The sum of the ranks for variable k across the two profitability subsamples = The mean rank of variable k

| | = number in the individual cells represents the rank order of entry of the variable indicated in the profitability subsample designated 143

Table 4-27 reveals that the nine variables contained in the Signifi­ cant Predictor Profile explain 62.9 percent of the total variance in the sample subset comprised o f the observations w ith a gross margin above the sample median and 36.9 percent o f the variance In the below median subsample. The explained variance for the upper-half margin sample has an

F-value of 24.85, while the lower-half F-value is 8.59. Each sample has 9 and 132 degrees of freedom, making the critica l F-value for the 95 percent profitability level 1.95.

The second step in the analysis again analyzes the individual vari­ ables of the Significant Predictor Profile to determine which measures explain a s ig n ific a n t amount o f incremental variance in each o f the sample halves. Table 4-23 summarizes the in v e s tig a tio n , revealing th a t only the

Total Debt to Net Worth and Average Market Share measures meet the c r ite r ia .

Stage three then employs the Friedman Test to determine 1f the rank order of entry of the variables in the Significant Predictor Profile varies from one sample h a lf to the other based on separate stepwise m u ltip le regression analyses performed on each subsample. Table 4-28 presents the analysis and suggests that the significance rank of the nine variables con­ tained in the S ig n ific a n t P re d icto r P ro file does vary between the in d i­ vidual measures.

The R eliability of The Significant Predictor Variables Over D iffe r 1nq Geographic Diversification Strategies'

The following section continues the Investigation of hypothesis seven, which is :

H7: The profile of significant predictor variables does not d if f e r between d iffe re n t managerial stra te g ie s TABLE 4-27

PREDICTOR RELIABILITY OVER PRICING STRATEGIES

Upper-Half^ Lower-Half5 VARIABLES R21 Rank2 R3 R2 Rank2 R3

1. Gross Margin To Net Sales .001 8 .146 .000 9 -.237 2. Total Debt To Net Worth .528* 1 -.727 .057* 2 -.333 3. Days Payables Outstanding .009* 6 -.307 .004 6 -.095 4. Net Sales To Accounts Receivables.030* 2 .329 .001 7 .103 5. Net Worth To Cash Flow .030* 4 .431 .001 8 .058 6. Total Liabilities To Cash Flow .022* 3 -.459 .009 5 -.135 7. Net Sales Index .007* 5 .185 .213 1 .461 8. Average Market Share .000 9 -.032 .048* 3 .249 9. Advertising Expenditures To Net Sales .002 7 -.302 .037* 4 -.189 Total Variance Explained .629** .369**

incremental variance explained is statistically significant at the 95 percent probability level |*Column to ta ls may not sum exactly due to rounding e rro r R^ is the incremental variance explained by the variable's introduction into the stepwise multiple 2 regression analysis incorporating all nine variables Rank is the relative order in which each variable is introduced into the stepwise multiple regression ^solution 4R is the simple regression coefficient for each variable The upper-half sample incorporates the data for a ll observation where the sample firm 's gross margin gon merchandise sold is above the median The lower-half sample incorporates the data for a ll observations where the sample firm 's gross margin on merchandise sold is below the median 145

TABLE 4-28

THE FRIEDMAN ANALYSIS OF THE PRICING STRATEGY PORTION OF HYPOTHESIS 7 The Significant Predictor Profile

V1 V2 V3 V4 V5 V6 V7 V8 V9 8 1 6 2 4 3 5 9 7 *1 r 2 9 2 6 7 8 5 1 3 4 1 1 12 9 12 8 6 12 11 Rk 17 3 9 144 81 144 64 36 144 121 Rk 289

= V V h = V2 3 = V4 = V5 = V6 = V7 = V8

Hfl: uV-j t yV2 f yV3 yV4 t uv5 t yv6 t uV? t uVg

Test 1 -N-klk+l) J l Rk - 3N ; »-2, k=9

Q = 8.80, df = 1; df Q5 = 3.84

R-. = Incorporates data for those observations where the gross margin in percentage is above the median R2 = Incorporates data for those observations where the gross margin per­ centage is below the median V, = Gross Margin To Net Sales V2 = Total Debt To Net Worth Vo = Days Payables Outstanding V? = Net Sales To Accounts Receivables Vr = Net Worth To Cash Flow = Total Liabilities To Cash Flow V7 = Net Sales Index Vg = Average Market Share Vg = Advertising Expenditures To Net Sales R|^ = The sum of the ranks for variable k across the two pricing strategy subsamples yV^ = The mean rank o f v a ria b le k

| [ = The number in the individual cells represents the rank order of entry of the variable indicated in the pricing strategy subsample designated Specifically, the impact which a firm 's decision as to the extent of geographic dispersion is considered. For the purpose of the analysis, tne total data base is divided into the following three groups:

G, - the observation where the firm has retail operations in two or fewer states G« - the observation where the f 1 rrr. has retail operations 1s from three through ten states G3 - the observation where the firm has retail operations in eleven or more states

Appendix I Identifies the firm ’s assignment to the three groups.

The remainder of the section uses the identical Investigative format as the immediately preceeding sections, Step one Involves the consider­ ation of the amount of variance explained by the Significant Predictor

Profile in each of the three geographic subsamples. Table 4-29 reveals the amounts to be 77.4 percent, 61.5 percent, and 38.5 percent for the groups one through three. Respectively the F-values for assessing the sig n ifi­ cance of the variance explained by the Significant Predictor Profile 1n the three groups is 27.06, 18,78, and 6.19. With degrees of freedom of 8 and

72, 9 and 106, and 9 and 89, the F-values needed for statistical sig n ifi­ cance for each of the groups at the 95 percent probability level are 2.77,

2.58, and 2,61 respectively. In each case the amount of variance explained by the Significant Predictor Profile Is statistically significant.

Step two requires the consideration of the individual variables con­ tained in the Significant Predictor Profile 1n order to ascertain which of the in d iv id u a l measures c o n s is te n tly explain a s ig n ific a n t amount o f in c re ­ mental variance in the stepwise multiple regression analyses of each sub­ sample, The information from the analysis which is summarized in Table

4-23, indicates that the following four variables explain an amount of TABLE 4-29

PREDICTOR RELIABILITY OVER GEOGRAPHY

Subdivision 1^ Subdivision 2^ Subdivision 3^

VARIABLES R21 Rank2 R3 R2 Rank2 R3 R2 Rank2 R3

1. Gross Margin To Net Sales .056* 5 .248 .100* 3 .469 .002 8 .188 2. Total Debt To Net Worth .134* 2 -.264 .016* 7 -.362 .036* 4 -.030 3. Days Payables Outstanding .219* 3 .290 .053* 4 .013 .041* 3 .271 4. Net Sales To Accounts Payables .003 7 -.029 .011* 8 .363 .010 5 .164 5. Net Worth To Cash Flow .000 9 .174 .273* 1 .522 .001 7 .052 6. Total L ia b ilitie s To Cash Flow .002 8 -.050 .101* 2 -.125 .002 8 .025 7. Net Sales Index .263* 1 .513 .022* 5 .329 .213* 1 .461 8. Average Market Share .059* 4 .250 .010* 9 .032 .001 9 .005 9. Advertising Expenditures To Net Sales .037* 6 -.377 .030* 6 -.245 .079* 2 -.427 Total Variance Explained .774** .615** .385**

♦Incremental variance explained is statistically significant at the 95 percent level T*Column totals may not sum exactly due to rounding error R^ is the incremental variance explained by the variables introduction into the stepwise multiple 2 regression analysis incorporating all nine variables Rank is the relative order in which each variable is introduced into the stepwise multiple regression 3solution •R is the simple regression coefficient for each variable rSubdivision 1 incorporates the data for all firms having retail outlets in two or fewer states ^Subdivision 2 incorporates the data for all firms having retail outlets in three to ten states Subdivision 3 incorporates the data for all firms having retail outlets in eleven or more states incremental variance which is statistically significant at the 95 percent probability level in each of the three geographic diversification sub­ samples:

1. Total Debt To Net Worth 2. Days Payables Outstanding 3. Net Sales Index 4. Advertising Expenditures To Net Sales

The third step again employs the Friedman Test to consider whether the

rank order of entry of the variables contained in the Significant Predictor

P ro file varies between the three samples. Table 4-30 summarizes the in v e s ti­ gation, and indicates that the rank of the explanatory power of the nine component variables does vary.

The R eliability of The Significant Predictor Variables Over Line of Trade Strategies

The present section concludes the Investigation of hypothesis 1 by

assessing the impact on the Significant Predictor Profile of a firm ’s

choice of product line emphasis. For the analysis the following three

sample groups are Id e n tifie d :

L, - the observations where the firm Is operating convenience food stores and related support f a c ilit ie s Lp - the observations where the firm 1s operating supermarkets and related support fa c ilitie s Lo - the observations where the firm 1s d iv e rs ifie d , operating in the retail food Industry and non­ food related businesses

In each case the food re la te d support f a c ilit ie s re fe r to such a c tiv itie s

and food processing operations, food growing or raising fa cilitie s , and

distribution operations. Appendix I reveals the assignments to each of the

three groups. TABLE 4-30

THE FRIEDMAN ANALYSIS OF THE GEOGRAPHIC DIVERSIFICATION PORTION OF HYPOTHESIS 7

The Significant Predictor Profile

V1 V2 V3 V4 V5 V6 V7 V8 V9 5 2 3 7 9 8 1 4 6 G1 3 7 4 8 1 2 5 9 6 G2 6 8 1 9 . 2 G3 4 3 5 7

18 22 14 Rk 14 13 14 20 17 7 R2 196 169 196 324 49 484 196 k 400 289

yV1 = yV^ = y V3 = yV uV? = uV8 V 4 = * V5 = yv6

Ha: uV, ¥ uV2 ¥ uVo ¥ uV4 ^ u V 5 f yV6 f yV7 ¥ uV8 12 2 Test Q = \ . W - 1 = 9 N“ k(k+1) k=1 Rk

2 12.36, df 5.99 Q = = 2; Xz d f,. 05

G, = Incorporates data from those observations where the same firm has retail operations in two or fewer states G2 = Incorporates data from those observations where the sample firm has retail operations in from three through ten states Gq = Incorporates data from those observations where the sample firm has retail operations in eleven or more states V, = Gross M artin To Net Sales Vi = Total Debt To Net Worth Vq = Days Payable Outstanding V? = Net Sales To Accounts Receivables Vc = Net Worth To Cash Flow V® = Total Liabilities To Cash Flow V7 = Net Sales Index Vg = Average Market Share Vq = Advertising Expenditures To Net Sales R? = The sum of the ranks for variable k across the three geographic diversi­ fication strategies yVk = The mean rank o f va ria b le k

| | = The number in the cell represents the rank order of entry of the variable indicated in the geographic diversification strategy subsample designated 150 Aqain the same format as is used to ascertain the re lia b ility of the Siqnificant Predictor Profile over the Time, P rofitability, Pricing

Strateaies, and Geoqraphic Diversification Strategies subsamples is used. Table 4-31 reveals the amount of variance individually explained in each of the three subsamples to be 92.0 percent, 66.9 percent and 61.7 percent. Respectively the F-values and degrees of freedom for the three samples are:

L1 = 33.40; 9,26; L2 = 18,14; 8,179; L3 = 14,52; 8,72;

The F*values required for statistical significance at the 95 percent

probability level are:

df = 9,26 - 2.27 df = 8,179-1.99 df = 8,72 - 2.07

In each case the variance explained by the Significant Predictor Profile

1s statistically significant at the 95 percent probability level.

The second step in the analysis calls for a consideration of the abil­

ity of each of the individual variables contained in the Significant Predic­

tor Profile to account for a statistically significant amount of incre­

mental variance across the three line of trade categories. Table 4-23

summarizes the analysis and indicates that only the total debt to net worth

and advertising expenditures to net sales measures are statistical sign ifi­

cant in all three samples.

Step three uses the Friedman Test to determine i f the order o f entry

of the nine variables belonging to the Significant Predictor Profile in the

stepwise analysis changes among the three samples. Table 4-32 summarizes TABLE 4-31

PREDICTOR RELIABILITY OVER LINES OF TRADE

LOT l4 LOT 25 LOT 36 VARIABLES 21 2 P. Rank R3 R21 Rank 2 R3 R21 Rank 2 1. Gross Margin To Net Sales .376* 1 .613 .189* 1 .435 .004 7 -.169 2. Total Debt To Net Worth .368* 2 - .004 .166* 2 -.287 . 021* 4 -.169 3. Days Payables Outstanding . 012* 5 .324 .003 7 .068 . 012* 5 .234 4. Net Sales To Accounts Receivables .001 8 .442 .000 8 .162 .045* 3 .354 5. Net Worth To Cash Flow .002 7 .577 .008 6 .351 - DNE 6. Total Liabilities To Cash Flow .001 9 .110 .006 5 -.022 .008 6 -.091 7. Net Sales Index . 021* 4 .585 - DNE - .413* 1 .642 8. Average Market Share .002 6 - .238 .016* 4 .200 .001 8 -.094 9. Advertising Expenditures To Net Sales .139* 3 - .286 .060* 3 -.267 .114* 2 -.507 Total Variance Explained .920** .669** .617**

♦Incremental variance explained is statistically significant at the 95 percent level f*£olumn totals may not sum exactly due to rounding error R is the incremental variance explained by the variable's introduction into the stepwise multiple

2 regression analysis incorporating all nine variables Rank is the relative order in which each variable is introduced into the stepwise multiple regression ^solution 4R is the simple regression coefficient for each variable cLOT 1 incorporates the data for firms operating convenience food stores and related support fa cilitie s gLOT 2 incorporates the data for firms operating supermarkets and related support fa cilitie s LOT 3 incorporates the data for diversified food companies; those engaged in both the retail food industry and other non-food related businesses. 152

TABLE 4-32

THE FRIEDMAN ANALYSIS OF THE EFFECT OF DIFFERING LINE OF TRADE STRATEGIES ON HYPOTHESIS 7

The Significant Predictor Profile

V1 V2 V3 V4 V5 V6 V7 V8 V9 1 2 5 8 7 9 4 6 L1 3 1 2 7 8 6 5 9 4 3 L2 7 4 5 3 9 6 1 8 2 L3 17 19 22 20 14 18 8 Rk 9 8

R2 81 64 289 361 484 400 196 423 64 Rk

H Q : u V i - yV2 - yV3 = yV^ = yVg = yVg = yV7 = yV g = y V g

Ha: yV1 7* yV2 yV3 * yV4 t yVg * yVg * yV? f y V g * y V g

Test ^ k=l Rk-3N(k+l);N=3, k-9

0 = 10.58, df = 2; X2df Q5 = 5.99

L, = incorporates data for observations where the firm operates convenience food stores and related support fa cilitie s L? = incorporates data for observations where the firm operates supermarkets and related support facilities Lo = incorporporates data for observations where the firm is diversified; those engaged in both the food in d u strie s V, = Gross Margin To Net Sales Vo = Total Debt To Net Worth Vo = Days Payables Outstanding V« = Net Sales To Accounts Receivables Vr = Net Worth To Cash Flow Vfi = Total L ia b ilitie s To Cash Flow Vo = Net Sales Index Vo = Average Market Share Vo = Advertising Expenditures To Net Sales R? = The sum of the ranks for variable k across the three line of trade strategies yV^ = The mean rank o f va ria b le k

| | = The number in the cell represents the rank order of entry of the vari­ able indicated in the line of trade strategy designated 153 the test and reveals that the mean rank of the Individual variables is d iffe re n t.

The In ve stig a tio n o f The Shape o f The Relationship Between The Significant Predictor Variables and Retail P rofitability

The final section of Chapter 4 seeks to provide the information neces­ sary to respond to the fifth research question which 1s stated in Chapter 1 as follows:

Q5; Does the re la tio n s h ip between the in d iv id u a l measures used to represent the six variable types and profit­ ab ility assume an identifiable pattern?

Specifically, the answer is provided through the consideration of the eighth and final research hypothesis:

Hg? The re la tio n s h ip between the In d ivid u a l measures found s ig n ific a n t and p r o f it a b ilit y is not linear.

Two steps are called for; firs t, the Investigation of the regression beta weights and second, an analysis of the plots of the standardized regres sion residuals for the nine measures contained in the Significant Predictor

P ro file , Table 4-33 summarizes the beta weight in v e s tig a tio n , revealing

TABLE 4-33

AN ANALYSIS OF THE BETA WEIGHTS OF THE SIGNIFICANT PREDICTOR PROFILE F-Test for 95 VARIABLE NAME Beta F-Value percent significance 1. Gross Margin To Net Sales .287 20,162 3.87 2. Total Debt To Net Worth -.261 18.304 3.87 3. Days Payables Outstanding .013 0.037 3.87 4. Net Sales to Accounts Receivables .044 0.887 3.87 5. Net Worth To Cash Flow .205 9.886 3.87 6. Total Liabilities To Cash Flow -.181 7.389 3.87 7. Net Sales Index ,310 44,784 3.87 8. Average Marekt Share .135 9.712 3.87 9. Advertising Expenditures To Net Sales -.156 12,095 3,87 154 th a t seven o f the nine component measures have a s ig n ific a n t lin e a r re la ­ tionship with retail profitability. Appendix J presents the residual plots which generally reveals a random variation around the observed dependent measure, indicating the presence of a linear relationship.

The Secondary Research Findings

The secondary findings are observations which are not the direct result of the investigation of any of the research hypotheses; but never­ theless provide some useful information. The comparison of the two basic stepwise multiple regression analyses employed in the research provides the firs t of the secondary findings.

In the study the twenty-one research variables which comprise the six subclassifications of Financial Structure and Marketing Performance measures are subjected to stepwise analyses of the six subclassifications independently and aggregately. Table 4-18 summarizes the former procedure,

Table 4-15 the latter.

The major observations emerging from the comparison of the two analyses is that (1) the amount of variance explained by the individual subclassifcations diffe r, and (2) the Individual rieasures obtaining statistical significance in terms of the variance explained also differ.

In terms of the firs t finding, an analysis of Tables 4-15 and 4-18 reveals that the only sizable change in the amount of variance explained by the six individual subclassifications resides 1n the Cash Flow

Management category. When considered independently, the subclassifica­ tion explains approximately 17 percent of the total variance in profit performance as compared to 2.5 percent when a ll the subclassifications are compared simultaneously. 155

The implications appear to be that one, or some combination of the remaining five subclassifications, explains variance 1n the dependent mea­ sure which overlaps the variance explained by Cash Flow Management group.

Because Inadequate cash flow management often necessitates the employment of additional short-term debt, the Financial Management subclass1f1cat1on seems a like ly covariant. Appendix M presents a simultaneous stepwise analysis of all twenty-one variables with current debt to net worth sub­ stituted for the total debt to net worth ratio. The main result appears to be that the average collection period, number of days payables and net sales to cash plus marketable securities variables Increase in importance.

A ll three measures are Financial S tructure V a ria bles, but each also belongs to a different subclassification as can be seen from Table 4-18.

Therefore, no concrete implications can be surmised as to which variable subclass1f1cat1ons may be explaining variance 1n p ro fita b ility which is common to the Cash Flow Management subclassif1cat1on.

The second observation concerns the individual variables within the six subclass1f1cat1ons which obtain statistical significance in terms of the amount o f variance explained 1n each o f the two separate stepwise analyses. When all twenty-one variables contained 1n the six subclassifi­ cations are considered simultaneously the following twelve variables explain a statistically significant amount of Incremental variance:

1. Net Sales Index 2. Total Debt To Net Worth 3. Gross Margin To Net Sales 4. Advertising Expenditures To Net Sales 5. Average C o lle ctio n Period 156

6. Average Market Share 7. Net Sales To Cash & Marketable Securities 8. Number of Days Payables Outstanding 9. Total Liabilities To Cash Flow 10. Net Worth To Cash Flow 11. Net Sales To Cash Flow 12. Gross Margin Per Employee

When the six subclassifications are regressed against profitab ility one-at-a-t1me, the stepwise analyses reveal that the following nine mea­ sures make a statistically significant incremental contribution to explained variance,

1. Net Sales Index 2. Gross Margin To Net Sales 3. Net Worth To Cash Flow 4. Total Debt To Net Worth 5. Total Liabilities To Cash Flow 6. Advertising Expenditures To Net Sales 7. Net Sales To Accounts Receivables 8. Market Share Average 9. Number of Days Payables Outstanding

In the firs t case the twelve variables are listed 1n the order in which the measures are Introduced in to the stepwise m u ltip le regression solution. The nine variables listed 1n the second set are presented according to the amount of Incremental variance explained by the Intro­ duction o f each measure beginning w ith the greatest amount o f variance explained. An examination of the two lists indicates that when all the variables are considered simultaneously as opposed to the separate analysis of the six subclassifications, the Individual variables whose

Incremental contribution to explained variance decline 1n magnitude are again the Cash Flow Management measures which f a ll from th ird and f i f t h in importance to ninth, tenth, and eleventh. In addition, the total debt 157 to net worth ratio's position improves and average collection period gains significance. Again, the variance explained by the Cash Flow Man­ agement subclassification appears to explain variance in profit performance which is also accounted for by other Financial Structure Variables par­ ticularly the Financial Management subclassification.

One other interesting secondary finding is the ability of the current debt to net worth measure to achieve equal success in explaining the v a ri­ ance in profitability as the total debt to net worth variable. The appropriate conclusion appears to be that current debt probably accounts for most of the variance explained by the total debt measure. Such is hardly a surprising finding as Appendix N reveals that on average current debt represents 57,2 percent of the total debt held by the sample firms.

However, the finding does have sign ifica nt managerial importance due to the greater control managers may have over the short-term portion of debt

vis-a-vis funds already borrowed and invested in long term assets. CHAPTER 5

THE SUMMARY AND CONCLUSIONS

Introduction

The purpose of Chapter 5 is to summarize and interpret the findings of the research. The next section reviews the purposes of the research and summarizes the general findings emerging from the study. Section three summarizes the specific investigation of each of the research hypotheses. The fourth section id e n tifie s the descriptive research model; the f if t h section, the predictive research model. Section six presents the implications of the research for the management of retail operations. The seventh section offers some concluding comments on how the findings of the research might improve the management of re ta il firms. The fin a l section id e n tifie s the sugciestions made by the study for possible future research e ffo rts .

A Summary of The Research

The basic purpose of the research is to compare the relative effect of marketing and financial strategies on p ro fit performance in re ta il operations. The research process takes place in five stages beginning with the data selection. Secondary marketing and financial information forms the basis fo r the study and is gathered from four sources; the

COMPUSTAT Data Tapes, Management Horizon's Retail Yearbook, the Securities and Exchange Commission 1 0 - K Supplementary Reports, and the Supermarket

158 News D istribution of Grocery Sales. The data base is formed from in fo r­ mation gathered fo r the years 1969 to 1977 fo r each of the th irty -fiv e corporations identified in Appendix K.

Phase two of the research involves the investigation of the data collected fo r any s ta tis tic a l characteristics which might impact the use of the regression based analysis used in the study. Specifically, the data base is examined for spurious correlation, auto correlation, and m u ltic o llin e a rity . In cases where such problems are found, variables are deleted in order to correct the situation.

The actual statistical analysis of the relationship between the inde­ pendent variables and re ta il p ro fit performance, which is measured as the pre-tax return on total assets, takes place in phase three. Because the purpose of the study is to examine the re la tive impact of each of the two strategy areas on profitability, incremental regression techniques which examine the independent effects of the individual variables are u tiliz e d .

Phase four id e n tifie s the research models which serve as a basis for the managerial contribution of the study. The analyses of phase three are used to identify the individual predictor variables which have a significant relationship with profitability. The correlation of the sign ifica nt independent variables with p ro fit performance forms the basis of the relationship which is identified as the descriptive model. By examining the strength of the relationship, as well as its direction, the descriptive model offers a variety of implications for managers which are discussed in the Implications and Concluding Note sections of the present chapter. A predictive model is also developed from the consideration of the findings of the th ird phase. The basic purpose of the second model 160 is to provide the means by which managers can consider the effect of strategy changes on profitability.

The final phase of the research is the validation phase which attempts to ascertain the reliability and validity of the study's find­ ings. In essence, the consistency of the research findings over a variety of differing circumstances and strategies are examined. In addition, the accuracy of the predictive model developed in the fourth phase is con­ sidered through the use of a classification test. This test simply uses additional data to determine how accurately the model is able to predict actual p ro fit performance using the variables selected fo r inclusion in the model.

The findings which emerge fo r the five-phased research process are summarized in Table 5-1.

In a brief synopsis, the research findings show the following:

1. Marketing Performance Variables, as a group, are statistically significant predictors of retail p ro fit performance.

2. Financial Structure Variables, as a group, are statistically significant predictors of retail p ro fit performance.

3. Marketing Performance Variables are better pre­ dictors of re ta il p ro fit performance than Financial Structure Variables, when each is con­ sidered as a set.

4. Of the six variable subcategories, which are as follows;

A. Marketing Management B. Margin Management C. Financial Management D. Cash Flow Management E. Asset Management F. Liq uidity Management c

Table 5-1

A SUMMARY OF THE RESEARCH FINDINGS

FINDING SPECIFIC RESULT

1. Marketing Performance Variables Variables significant at the Are Significant Predictors of 95 percent level are: net sales Retail Profitability index, gross margin to net sales, advertising expenditures to net sales, average market share.

2. Financial Structure Variables Variables significant at the 95 Are S ignificant Predictors of percent level are: net worth to Retail Profitability cash flow, total liabilities to cash flow, days payables out­ standing, total debt to net worth, average collection period, current debt to ending inven­ tory, net sales to cash flow, and net sales to accounts receiv­ ables.

3. Marketing Performance Variables Marketing Performance Variables Are Better Predictors of account fo r 34.05 percent of Retail Profitability Than the total variance in profit­ Financial Structure Variables ability; Financial Structure Variables 15.08 percent.

4. The Six Subclassifications of The rank order of the six in Marketing Performance and terms of the variance in profit­ Financial Structure Variables a b ility explained is 1. Market­ Have Differing Abilities To ing Management 2. Cash Flow Predict Retail Profitability Management 3. Margin Management 4. Financial Management 5. Asset Management 6. Liquidity Manage­ ment. FINDING SPECIFIC RESULT

The Significant Predictor Profile* The profile consists of the Consistently Explains A Statis­ following nine variables: gross tically Significant Amount of margin to net sales, total debt Variance in Profitability Over to net worth, days payables out­ Tiino standing, net sales to accounts receivables, net worth to cash flow, total liabilities to casn flow, net sales index, average market share, advertising expen­ ditures to net sales. Variables compared over three three-year segments.

The Significant Predictor Profile The profile as a unit explains a Consistently Explains A Statis­ significant amount of variance tically Significant Amount of in both the upper and lower Variance in Profitability Over profitability halves. As indi­ Firms of Differing Profitability vidual measures the following five consistently explain a Sig­ nificant amount of variance: total debt to net worth, total liabilities to cash flow, net sales index, average market share, advertising expenditures to net sales.

The Significant Predictor Profile The profile as a unit explains a Explains A Statistically Signi­ significant amount of variance ir, ficant Amount of Variance in both the upper and lower gross Profitability Over Pricing margin halves. Of the in d iv i­ Strategies dual measures, only total debt to net worth is sig n ifica n t in both halves.

The Significant Predictor Profile The profile as a unit explains a Explains A Statistically Signi­ significant amount of variance ficant Amount of Variance in in the p ro fit performance of Profitability Over Differing local, regional, and national Geographic D iversification retail operations. The four indi­ Strategies vidual measures achieving signi­ ficance in all three segments are total debt to net worth, days pay­ ables outstanding, net sales index, advertising expenditures to net sales. 163

FINDING SPECIFIC RESULT

9. The Significant Predictor The profile as a unit explains Profile Explains A Statistic­ a significant amount of the vari­ ally Significant Amount of ance in p ro fit performance in Variance in Profitability Over convenience stores, supermarkets, Line of Trade Strategies and dive rsifie d re ta il food operations. The individual mea­ sures achieving significance in all three segments are total debt to net worth and advertis­ ing expenditures to net sales.

10. The Relationship Between The All individual measures, except Individual Variables in The days payables outstanding and Significant Predictor Profile net sales to accounts receiv­ and Profitability is Linear. ables, have a signficiant linear relationship with profit per­ formance.

*The Significant Predictor Profile refers to the nine variables identified as significant predictors of retail profit performance in the individual stepwise regression analysis of each of the six subclassifi- cations' relationship with profitability. Table 4-21 first identifies the nine measures. 164

the f i r s t three are the most e ffic ie n t predictors of re ta il p ro fit performance because the la tte r three cannot explain a s ta tis tic a lly sign ifica nt amount of incremental variance when all six sub­ categories are considered simultaneously.

5. The variables which are the best predictors of re ta il p ro fit performance, referred to as the Sig­ nificant Predictor Variables, have reliability.

6. The relationship between the nine variables con­ tained in the Significant Predictor Profile and p ro fit performance is linear.

The chapter now continues with the consideration of the specific findings which result from the analysis of the research hypotheses.

The Research Findings

The specific findings generated by the study's investigation of the eight research hypotheses are organized into ten research findings. The ten are now presented ind ivid ually.

Finding 1: Marketing Performance Variables Are Significant Predictors of Retail Profitability

The firs t research conclusion stems from the investigation of the f ir s t research hypothesis which is:

H,: Marketing Performance Variables are not significant predictors of retail profitability.

Hypothesis 1 is rejected through the stepwise m ultiple regression analysis presented in Table 4-5. Marketing Performance Variables explain 30.03 percent of the total variation in retail profitability when the variable set is independently analyzed. Thus, with a greater than 95 percent s ta tis tic a l confidence, one can suggest that the Marketing Performance

Variables are significant predictors of retail profitability. In addition, 16 of the nine variables which constitute the set, four account fo r 28.32

percent of the total variance in profitability and the four are the

only variables whose incremental explained variance is statistically

significant at the 95 percent level. The four significant Marketing

Performance Variables are:

1. Net Sales Index 2. Gross Margin To Net Sales 3. Net Sales To Advertising Expenditures 4. Average Market Share

Finding 2: Financial Structure Variables Are Significant Predictors of Retail Profitability

The second research finding is generated from the investigation of hypothesis 2, which is :

H«: Financial Structure Variables are not significant predictors of retail profitability.

Hypothesis 2 is also rejected in the two phase analysis which uses two sets of Financial Structure Variables; the second simply the firs t set with the four Asset Management Variables removed. Table 4-3 identifies the measures which comprise each of the two sets.

The entire set of Financial Structure Variables referred to as

Financial Structure^ Variables, explain 30.65 percent of the variation in profitability with the following eight variables making a significant incremental contribution.

1. Net Worth To Cash Flow 2. Total L ia b ilitie s To Cash Flow 3. Days Payables Outstanding 4. Total Debt To Net Worth 5. Average Collection Period 6. Current Debt To Ending Inventory 7. Net Sales To Cash Flow 8 . Net Sales To Accounts Receivables

The eight Financial Structure-) Variables making a significant incremental contribution explain 30.40 percent of the total variance in profitability

Both amounts of explained variance are statistically significant at the

95 percent level.

The Financial Structureg Variables explain 29.31 percent of the tota variance; the six variables of the set whose incremental contribution is statistically significant, 29.16 percent. The six variables are:

1. Net Worth To Cash Flow 2. Total L ia b ilitie s To Cash Flow 3. Days Payables Outstanding 4. Total Debt To Net Worth 5. Average Collection Period 6. Current Debt To Ending Inventory

Both amounts of explained variance are statistically significant at the

95 percent level. Because both sets of Financial Structure Variables explain statistically significant amounts of variance in profitability, hypothesis 2 is refuted; Financial Structure Variables are significant predictors of retail profitability.

Finding 3: Marketing Performance Variables are Better Predictors of Retail Profitability Than Financial Structure Variables.

The third research finding results from the consideration of hypoth­ esis 3, which is:

H~: Marketing Performance Variables are not better predictors of retail profitability than Financial Structure Variables.

Completely accepting or rejecting hypothesis 3 is complicated by the fact that no direct means exists to assess the statistical significance 167 of the difference in the amount of variance explained by the two vari­ able groups, as is explained in Chapter 4. However, by using stepwise m ultiple regression and the Wilcoxon te s t, the re la tive independent con­ tribution of the two variable sets to explaining the variance in profit­ a b ility can be examined. Table 4-13 indicates that the fiv e Marketing

Performance Variables which make a statistically significant contribution to explaining the variance in re ta il p r o fita b ility explain 34.05 percent of the total incremental variance. The same table shows that the seven sig n ifica n t Financial Structure^ measures account fo r 15.96 percent of the incremental variance and the Financial Structure2 measures, 15.08 percent.

In the analysis of the research hypotheses, incremental variance is con­ sidered so as not to incorporate variables into the research models which merely reaccount fo r variance which is also explained by another measure.

When using a stepwise multiple regression analysis,when more than, one vari­ able explains the same portion of the total variance in the dependent mea­ sure, the variable accounting for the largest portion of the total variance enters the solution set firs t; the second measure to be introduced is the one explaining the greatest amount of the remaining variance and so on until the introduction of another variable does not explain a statistically sig n ifica n t amount of additional variance in the dependent measure. Thus? the chance of the significant variables explaining the same portion of the variance in the dependent measure is minimized and the research model is made more e ffic ie n t by the reduction of the number of independent v a ri­ ables. Figure 5-1 illustrates this phenomena. 168

Figure 5-1. A Ballantine Portrayal of Incremental Variance

In the illustration, the circle Y represents the total variance of

the dependent measure; circles X-j through X^ represent four independent

variables. I f variables X^ through X^ are entered in to a stepwise multiple regression analysis which is attempting to explain the variance

in the dependent measure Y, variable X-| enters the solution set firs t

because the amount of the dependent variance explained by X-j, which is

represented by the area shaded in the diagonal lines, is the largest of

the four variables. The second variable to be entered is because the

amount of independent variance in Y explained by X 2 is greater than that

explained by X^ or X^; the area shaded by the horizontal lines represents

the independent incremental variance explained by variable X^. Variable

X^ appears to explain about as large a percentage of the variance in the

dependent measure as X 2 . but nearly all of the variance has already been

explained by the introduction of X-j. Therefore unless the independent

variance explained by X^, represented by the black area, is statistically

sign ifica nt the variable does not enter the solution set and duplication

is reduced. In the example, explains the next largest amount of independent or incremental variance and the least. The entry of each of the last two variables, as well as the firs t two, is dependent on the incremental or independent variance being explained achieving s ta t is t i­ cal significance.1

Turning once again to the comparison of the Marketing Performance and Financial Structure variables, which is summarized in Table 4-10, no means exist to directly compare the amount of incremental variance explained by the sets of measures. Therefore alternative techniques must employed. F irs t, the Wilcoxon Test is used to determine i f the mean ranks of the variables in each of the sets is different. The ranks for each comparison, Financial Structure Variables^ versus Marketing Performance

Variables and Financial Structure VariableS 2 versus Marketing Performance

Variables, are the order of entry of each of the individual measures com­ prising the two variable classifications when all are entered into a step­ wise m ultiple regression analysis. Table 4-13, which summarizes the Wil­ coxon Test, suggests that when the Marketing Performance Variables are compared with the Financial Structure Variables the mean rank of the mar­ keting variables is found lower with a 92.6 percent level of statistical significance; when the Financial Structure^ Variables are substituted, the Marketing Performance Variables mean rank is found to be lower with a

91.1 percent level of confidence. Thus, the Wilcoxon Test finds that

Marketing Performance Variables enter the solution set sooner on average and are therefore more highly correlated with profitability on an incremen ta l basis.

^ o r a more detailed explanation see Jacob Cohen and P atricia Cohen, Applied Multiple Regression/Correlation Analysis for the Behavior Sci­ ences (New York: John Wiley & Sons, 1975), pp. 129-133. 1-70

The second step in the attempt to investigate hypothesis 3 involves the use of a hierarchical regression procedure. Hierarchical regression is used to validate the inherent suggestion of the stepwise m ultiple regression analysis that the Marketing Performance Variables explain a statistical significant incremental amount of the variance in the depend­ ent profitability measure when compared with Financial Structure Vari­ ables.2 Table 4-14 presents the hierarchical regression analysis and indicates that the additional variance in the dependent measure, when the variance explained by the Financial Structure Variables is partialled out, is statistically significant at the 95 percent confidence level.

Even though the Wilcoxon Test did not quite achieve a 95 percent con­ fidence level in finding that the mean rank of the variable sets are d if­ ferent, when combined with the findings of the hierarchical regression analysis, the suggestion that Marketing Performance Variables are better predictors of retail profitability than Financial Structure Variables appears ju s tifie d . The Marketing Performance Variables explain most of the variance accounted for by the Financial Structure Variables plus a greater amount of the incremental or independent3 variance.

Finding 4: The Six Subclassifications of Marketing Performance and Financial Structure Variables Have Differing Abilities To Predict Retail Profitability

The fourth conclusion emerges from the analysis of hypothesis 4 which is as follows:

2For a detailed explanation of the hierarchical regression see Cohen and Cohen (same reference 1), pp. 123-135.

independent variance refers to variance in the dependent measures which is explained or accounted for by only one of the independent variables. 171 H.: The amount of variance explained by the variables used as surrogate measures fo r each of the six variable types is not significantly greater than zero.

Hypothesis 4 is investigated on a variable subclassification to subclassi­ fication basis, with the amount of incremental variance explained by the

Marketing Management, Financial Management, and Margin Management achiev­ ing statistical significance at the 95 percent level when all six sub­ classifications are considered simultaneously. Table 4-17 summarizes the analysis.

A second stepwise m ultiple regression procedure is also used, with each of the six variable subclassifications being ind ivid ually regressed against p r o fita b ility . In the analysis, which is presented in Tables

4-18 and 4-19, only the Liquidity Management variable subclassification fails to explain a significant amount of the variance in the dependent measure. The main intent of the second analysis is to id e n tify the mea­ sures within the six variable subclassifications which are to be included in the initial research model as the Significant Predictor Profile vari­ ables. All six subclassifications are to be represented in the analysis, but in order to allow a parsimonious model, the within subclassification overlap of explain variance needs to be minimized. Therefore, the Incre­ mental approach of the stepwise multiple regression analysis is utilized to eliminate variables within a subclassification which explain approxi­ mately the same variance 1n the dependent measure as another variable within the set. Table 4-21 identifies the following nine variables as the

Significant Predictor Profile:

I. Gross Margin To Net Sales 2. Total Debt To Net Worth 3. Days Payables Outstanding 4. Net Sales To Accounts Receivables 5. Net Worth To Cash Flow 6. Total Liabilities to Cash Flow 7. Net Sales Index 8 . Average Market Share 9. Advertising Expenditures To Net Sales

Table 4-18 ide ntifies the variable subclassifications to which each

of the nine belong.

In order to further explore the significance of the amount of inde­

pendent or incremental variance explained by each of the six subclassifi­

cations, a hierarchical multiple regression analysis is also employed. As

is shown in Table 4-20, only the Liquidity Management Variables fa il to

explain a significant amount of the variance in the profitability of the

sample firms when the variance explained by the othervariable classifica­

tions is partial led out. The hierarchicalregression analysis therefore

agrees with the separate stepwise multiple regression analysis of the six

variable classifications. There is also a problem with simply summing the

amounts of incremental variance explained by the individual measures, as

is done in the f ir s t of the three procedures presented in the present sec­

tion. The amount of explained variance credited to each variable is the

incremental amount accounted fo r by the measures introduction into the

solution set. Therefore, when the firs t variable to enter explains X per­

cent that variable is continually credited with X percent even though the

second and succeeding variables may explain some variance in the dependent

measure which is common to the f ir s t variable. Thus, whenever an addi­

tional variable enters the solution set the amount of variance each vari­

able accounts for changes. I / b

In consideration of the problem with summing incremental variances,

the la tte r two procedures are relied on to conclude that in terms of the

magnitude of the variance explained, the six variable subclassifications

are ranked as follows:

1. Marketing Management Variables 2. Cash Flow Management Variables 3. Margin Management Variables 4. Financial Management Variables 5. Asset Management Variables 6. L iq u id ity Management Variables

Of the six, only the Liquidity Management subclassification fails to explain an amount of variance which is significantly greater than zero.

Findinq 5: The Significant Predictor Profile Consistency Explains A Statistically Significant Amount Of Variance in P ro fita b ility Over Time

The f if t h research finding results from the consideration of hypothe­ sis 5:

Hg: The profile of significant predictor variables is not stable over time.

In analyzing hypothesis 5 two interpretations appear relevant: one, is the Significant Predictor Profile able to reliably predict a statisti­ cally significant portion of the variance in the dependent variable? and two, do the variables which are in the S ignificant Predictor P rofile in d i­ vidually account fo r a s ta tis tic a lly sign ifica nt amount of variance?

Again, the variables included are those identified in the review of fin d ­

ing 4 within the present chapter.

In analyzing the f ir s t interpretation of hypothesis 5, the findings indicate that the nine variable set of signficant predictors does exhibit 174 stability in the profile's ability to explain a statistically significant amount o f the dependent measure's variance across d iffe re n t time periods.

Table 4-22 reveals that fo r the three time segments, the S ignificant

Predictor P rofile accounts fo r 42.2 percent, 37.6 percent, and 60.2 per­ cent of the total variance. Each is statistically significant at the 95 percent probability level.

The consideration of the second interpretation of hypothesis 5 involves two additional tests. First, the Friedman Test is employed to reveal that the mean ranks of the nine variables over time does exhibit differences. That is , a ll the nine variables are not equally good or sig­ nificant, predictors of p r o fita b ility . Table 4-24 summarizes the procedure.

Second, the statistical significance of the variance explained by each of the nine individual measures is assessed to see how many are consistently s ta tis tic a lly sig n ifica n t predictors of p ro fita b ility . Table 4-23 in d i­ cates that only the Total Debt To Net Sales measure achieves statistical significance in all three time segments.

Therefore, the logical conclusion is that the Significant Predictor

Profile as a group is a reliable predictor of profitability over time.

However, the ability of the individual measures within the Profile to explain variance is not stable.

Finding 6: The Significant Predictor Profile Consistently Explains A Statistically Significant Amount of Variance In P ro fita b ility Over Firms o f D iffering Profitability

The sixth research conclusion is arrived at through the analysis of hypothesis 6, which is stated as follows:

Hfi: The profile of significant predictors does riot d iffe r between more and less profitable firms. 175

Again, two interpretations are employed in the investigation of the

hypothesis. The first is that the Significant Predictor Profile is con­

sistently able to achieve a statistically significant level of correlation

with the dependent measure. The second is that the m ajority of the in d i­

vidual measures making up the Significant Predictor Profile are consistently

able to achieve statistical significance 1n the separate stepwise multiple

regression analysis.

Where the sample is s p lit into the two p r o fita b ility groups based on

the median return on total assets, the nine variable profile explains 49.2

percent o f the variance in the upper p r o fita b ility ha lf and 24.6 percenc

in the lower half. Both amounts are statistically significant at the 95

percent probability level, as 1s shown in Table 4-25.

In the consideration of the second interpretation of hypothesis 6, the Friedman Test indicates that the mean ranks of the nine measures in the

Significant Predictor Profile do change between the profitability halves.

Therefore, one can state that the nine measures are not all equally e ffi­

cient in the predictor of the dependent variable. Table 4-26 summarizes

the test procedure.

The second stage of the analysis of the second interpretation relies on Table 4-23 for the information as to which of the measures in the Sig­ nificant Predictor Profile explain a statistically significant amount of

incremental variance in both profitability halves. Five of the measures do so and are id e n tifie d below:

1. Total Debt To Net Worth 2. Total L ia b ilitie s To Cash Flow 3. Net Sales Index 4. Average Market Share 5. Advertising Expenditures To Net Sales The information, therefore, supports the fact that Significant Pre­ dictor Profile as a whole is a reliable predictor set over firms of dif­ fering p ro fit performance. In addition, five of the nine individual mea­ sures contained in the P rofile are also re lia b le adding more support to the r e lia b ility of the measures.

Finding 7: The Significant Predictor Profile Explains A Statistically Significant Amount Of Variance In P ro fita b ility Over Pricing Strategies

The seventh research result is delivered through the investigation o hypothesis 7, which is:

H-: The profile of significant predictors does not d iffe r between d iffe re n t managerial strategies.

The strategy of interest in finding 7 is the differing pricing stra­ tegies, which are based on the division of the sample into halves based upon the gross margins secured by sample firm s. Again the same two inte r pretations are used as 1n findings 5 and 6. First as a group, the Signi­ ficant Predictor Profile explains 62.9 percent of the variance in the p ro fita b ility of the sample half of firms employing the largest price mar gins and 36.9 percent of the variance in the sample ha lf with the small­ est margins. Table 4-27 indicates that both amounts of explained v a ri­ ance are statistically significant at the 95 percent level.

As for the second interpretation, Table 4-26 suggests that the mean rank of the variables is significantly different. Basically, the nine measures in the profile are therefore not all equally as good predictors of p ro fita b ility . Table 4-23 indicates that only the total debt to net worth and average market share measures achieve s ta tis tic a l significance in

both sample halves.

Therefore, one can suggest that the Significant Predictor Profile as

a group is reliable over firms with differing pricing strategies. How­

ever, within the Profile, the ability of the individual measures to explain

d iffe rin g p ro fit performance across varying pricing strategies is not

reliable.

Finding 8: The Significant Predictor Profile Consistently Explains A Statistically Significant Amount Of The Variance In P ro fita b ility Over D iffering Geographic Diversification Strategies.

The eighth research finding also stems from the analysis of hypothesis

7, however the managerial strategy o f interest in finding eight is the

geographic diversification employed by the sample firms. In the analysis,

the total sample of firms is divided into the following three subgroups:

G. - the observations where the firm has retail operations in two or fewer states Qp - the observations where the firm has retail operations in from three through ten states G~ - the observations where the firm has retail operations in eleven or more states.

Appendix I lis ts the group memberships of the sample firms.

Again a two phased analysis of the hypothesis is used to determine if

the ability of the Significant Predictor Profile varies among the geo­

graphic d ive rsifica tio n group, which basically are designed to represent

lo ca lly operating companies, regionally operating companies, and companies operating on a national basis. Phase one evaluates the ability of the entire Profile to predict profits. Table 4-29 shows that the Significant 178

Predictor P rofile explains 77.4 percent, 61.5 percent, and 38.5 percent of the total variance in p r o fita b ility in the three geographic strategy groups. A ll three amounts are s ta tis tic a lly sig n ifica n t at the 95 per­ cent level.

Phase two investigates the ab ility of the individual measures con­ tained in the Profile to predict profitability across the differing geo­ graphic strategies. F irs t, the Friedman Test reproduced as Table 4-30 indicates that the nine variables do not have equal abilities to predict the p ro fit performance in the sample firm s. Table 4-23 then indicates that the following four measures are the only ones of the nine contained in the Significant Predictor Profile to explain a statistically signifi­ cant amount of the incremental variance in the dependent variables in all three groups:

1. Total Debt To Net Worth 2. Days Payables Outstanding 3. Net Sales Index 4. Advertising Expenditures To Net Sales

One, therefore, is able to suggest that the Significant Predictor

P rofile as a group is reliable over d iffe rin g geographic d ive rsifica tio n strategies. In addition, one can also state that of the measures making up the Profile, only the four mentioned above are re lia b le over the d iffe r ­ ing strategies.

Finding 9: The Significant Predictor Profile Consistently Explains A Statistically Significant Amount Of The Variance In P ro fita b ility Over Line Of Trade Strategies.

The ninth research finding also results from the analysis of hypothe­ sis 7, with the managerial strategy of interest being the line of trade 175 the firms employ. In the investigation, the sample is divided into the following three subgroups:

L, - the observations where the firm is operating convenience food stores and related support fa c ilitie s L* - the observations where the firm is operating supermarkets and related support fa c ilitie s L3 - the observations where the firm is diversified; operating in the retail food Industry and non­ food related businesses.

The same two phased analysis is again used with Table 4-31 indica­ ting that in the three subsamples the Significant Predictor Profile as a group explains 92.0 percent, 66.9 percent, and 61.7 percent of the total variance in p ro fit performance. A ll three amounts are s ta tis tic a lly sig­ nificant at the 95 percent probability level.

The second phase o f the analysis examines the individual measures within the P ro file beginning with Table 4-32, which reveals that the nine measures do not have equal a b ility in predicting p ro fit performance. Table

4-23 reveals that only the total debt to net worth and advertising expen­ ditures to net sales measures are able to explain a statistically signifi­ cant amount of incremental variance in all three subsamples.

Again the Significant Predictor Profile shows reliability over differ­ ing lines of trade as a whole, but the individual measures w ithin the

Profile are unstable.

Finding 10: The relationship between the individual measures in the Significant Predictor Profile and profitability is linear.

The tenth finding results from the investigation of hypothesis 8 which is: 180

Hr: The relationship between the individual measures found sign ifica nt and p ro fita b ility is not linear.

F irs t, Table 4-33 reveals that seven of the nine measures contained in the Significant Predictor Profile have a statistically significant linear relationship with p r o fita o ility . Only the days payables outstand­ ing and the net sales to accounts receivables measures fa il to have a statistically significant linear component. In addition, the residual analysis presented as Appendix J has the characteristics of a linear relationship as is explained in the la st section o f Chapter 4. Therefore, the suggestion is that the relationship between the Significant Predictor

P ro file and p ro fit performance in the re ta il food sector is linear.

The Identification of The Descriptive Model

The major goal of the research is to identify the relationship between financial and marketing strategies and re ta il p ro fit performance.

In the five-phased data analysis, the appropriate measures to be used to represent the strategy areas are chosen and statistically analyzed. The descriptive model then relies on that analysis to identify the individual measures most highly related to profitability, as well as to specify the strength and direction of the relationship. Thus, the present section f i r s t id e n tifie s the variables which comprise the descriptive model; then, it specifies the relationship between those variables and retail profitability.

The results presented in Chapter 4 id e n tify nine variables which explain a statistically significant amount of the incremental variance in the p ro fit performance of the sample firms when the six separate sub­ classifications of strategy variables are considered. However, when the 181 nine variables are considered simultaneously in a stepwise regression

analysis, the net sales to accounts receivables and number of days pay­

ables outstanding measures do not explain an amount of incremental

variance which is statistically significant at the 95 percent level.

Therefore, only the following seven variables are included in the

descriptive model:

1. Net Sales Index 2. Total Debt To Net Worth 3. Gross Margin To Net Sales 4. Advertising Expenditures To Net Sales 5. Average Market Share 6 . Net Worth To Cash Flow 7. Total L ia b ilitie s To Cash Flow

The seven variables are arranged in the order the measures enter the

stepwise multiple regression analysis of the variance explained by the

nine variables contained in the Significant Predictor Profile. A note also should be made that the dropping of the two variables does not affect the r e lia b ility of the study. The accounts receivables to net

sales measure accounts fo r .1 to 3 percent of the total variance in the r e lia b ility analyses of the Significant Predictor P rofile presented in

Chapter 4. Likewise, the number of days payables outstanding variable explains from .5 to 1.2 percent of the total variance in the reliability analyses with one exception; the analysis of the effect of geographic diversification, where the measure explains 21.9 percent of the variance in one sample. However, in the effected caseover 50 percent of the total variance in local operations is explained without the two variables; s till a statistically significant amount of explained variance.

Table 5-2 summarizes the specific relationship which exists between the variables in the descriptive model and re ta il p ro fit performance. TABLE 5-2

THE SIGNIFICANT PREDICTOR PROFILE VARIABLES

R^ Simple R RI MARKETING MANAGEMENT VARIABLES

Net Sales Index .17635 .41994 Advertising Expenditures To Net Sales .03163 -.24420 Average Market Share .01854 .12881

CASH FLOW MANAGEMENT VARIABLES

Net Worth To Cash Flow .00740 .29875 Total L ia b ilitie s To Cash Flow .01433 -.06212

MARGIN MANAGEMENT VARIABLES

Gross Margin To Net Sales .10327 .32741

FINANCIAL MANAGEMENT VARIABLES

Total Debt To Net Worth .08228 -.28525

ASSET MANAGEMENT VARIABLES

Net Sales To Accounts Receivables .00158 .17133

LIQUIDITY MANAGEMENT VARIABLES

Number Of Days Payables Outstanding .00007 .12055

? Rt = the amount of incremental variance explained by variable I when the nine variables are entered into a stepwise multiple regression analysis. 183

In essence, the relationship is of the form:

Retail P ro fit Performance - (THE DESCRIPTIVE MODEL VARIABLES)

Basically, the representation suggests that re ta il p ro fita b ility is a function of the seven strategic variables identified as the Descriptive

Model. Specifically, the descriptive model is as follows:

Retail P ro fit Performance = .363 + 2.449 (NSI) - .022 (TD/NW) + .359 (GM/NS) - 239.812 (AD/NS) + .142 (AMS) + .721 (NW/CF) - .638 (TL/CF) where:

NSI = Net Sales Index TD/NW = Total Debt To Net Worth GM/NS = Gross Margin To Net Sales AD/NS = Advertising Expenditures To Net Sales AMS = Average Market Share NW/CF = Net Worth To Cash Flow TL/CF = Total L ia b ilitie s To Cash Flow

As a unit the seven variable descriptive model accounts fo r 43.38 percent of the total variation in the p ro fit performance of the sample firm s.

Only .165 percent of the total variance in the sample is lost due to the elimination of the two variables. Thus as compared to the total nine variable Profile, the seven variable Descriptive Model is 95.2 percent e ffic ie n t.1* The importance of each of the variables to p ro fit performance, as well as the potential interrelationships existing between the different measures is discussed in a following section en title d The Managerial Im pli­

cations of The Research. The potential means to improve p ro fit performance, which are ide ntified by the descriptive model, are also presented at that

time. Therefore, the composition of the predictive model is now considered.

^Calculated by dividing the variance explained by the seven variable model by that explained by the nine variable profile. 184 The Identification of The Predictive Model

The intent in building a predictive model from the data analyzed as a part of the research is twofold: f i r s t , to enable re ta il managers to evaluate the potential impact of strategic decisions on company profit performance; and second, to allow investors to evaluate the potential of firms which might be under consideration fo r acquisition: In the f ir s t instance, part of the purpose is served by the descriptive model which consid­ ers the potential interrelationship of many of the strategic decisions regu­ la rly made by the managers of re ta il firms. However, in addition some way is needed is quantified the impact the strategic decisions have on the profitability of the affected firm.

The development of the predictive model proceeds unddr two guidelines; to make the model as accurate and as efficient as possible. The former objective translates into maximizing the variance explained by the model, while the latter seeks to minimize the number of variables contained in the paradigm. Cost minimization, both in terms of time and the cost required to collect and analyze data is the reason for the second objective. Obviously, the two objectives can not be simultaneously satisfied to the fullest extent so some qu alitative judgments are made regarding the variables to be included in the model.

The initial starting point for the consideration of variables to be included in the predictive model begins with the nine measures contained in the Significant Predictor P ro file ; which in the order the variables enter the stepwise multiple regression analysis are:

1. Net Sales Index 2. Total Debt To Net Worth 3. Gross Margin To Net Sales 185

4. Advertising Expenditures To Net Sales 5. Average Market Share 6 . Net Worth To Cash Flow 7. Total L ia b ilitie s To Cash Flow 8 . Net Sales To Accounts Receivables 9. Number Of Days Payables Outstanding

However, as is reported in the descriptive model section, the last two

measures fail to explain a statistically significant amount of incre­

mental variance. Therefore, both net sales to accounts receivables and

number of days payables outstanding are omitted from future consideration.

Next, the other variables are considered. Table 5-3 lis ts the seven

remaining variables, the incremental variance explained by the intro­

duction of each, the cumulative explained variance, and the simple R for

each measure. As can be seen from the Table, along with an analysis of

Figures 5-2 and 5-3, the f i r s t three variables account fo r a vast major­

ity of the variance explained by the Significant Predictor Profile. To

be exact, the three measures account for 36.2 percent of the total of

43.4 percent or 83.4 percent of the total variance explained by the model.

Therefore, the managerial significance of the three seems to be greater

than that of the remaining four. Thus, the predictive model uses the beta weights of the three,when only the three are analyzed in a m ultiple

regression analysis,to arrive at the following predictive model:

Retail P ro fit Performance = .887 + 2.562 (NSI) - .033 (TD/NW) + .419 (GM/NS) where:

NSI = Net Sales Index TD/NW = Total Debt To Net Worth GM/NS = Gross Margin To Net Sales Table 5-3

THE PREDICTIVE MODEL IDENTIFICATION ANALYSIS

2 VARIABLE R2 Incremental R Simple 1

1. Net Sales Index .17635 .17635 .41994

2. Total Debt To Net Worth .25864 .08228 -.28525

3. Gross Margin To Net Sales .36191 .10327 .32741

4. Advertising Expenditures To Net Sales .39354 .03163 -.24420

5. Average Market Share .41208 .01854 .12881

6. Net Worth To Cash Flow .41947 .00740 .29875

7. Total Liabilities To Cash Flow .43380 .01433 -.06212 187

.50 - 4-

.40

.30 -■

.20 - -

Figure 5-2. The Incremental Variance Explained By The Significant Predictor Variables

.60 t

50 --

.40 - -

.30 --

20 - -

Figure 5-3. The Cumulative Variance Explained By The Significant Predictor Variables 188

The predictive model is tested using a classification test, the results of which are presented as Appendix L. Basically, the test re­ veals th a t wher the sample is divided in to halves based on the observed profitability, the model correctly classifies sample observations 85.7 percent of the time. When the sample is split into thirds, the model accurately classifies observations 78.6 percent of the time and into sample quarters 60.7 percent of the tim e. In a d d itio n , when the sample observations are split into profitability thirds and the middle quarter eliminated, the model correctly classifies the upper and lower most quarters on 88.9 percent of the time.

The Managerial Implications of The Research

The sixth section deals with the relevance of the research findings for retail managers. In particular, attention is directed towards examining the suggestions emerging from the study for improving the profitability of retail operations. The section is divided into four parts dealing with the implications of: 1. the significant individual measures, 2. the total descriptive model, 3. the predictive model, and 4. the secondary research findings. Section six now continues with the firs t of these four.

The Im plications o f The In d ivid u a l Measures Id e n tifie d As Components o f The D escriptive Model

Seven variables are identified as members of the descriptive model.

Each is now considered individually.

Net Sales Index

The most significant predictor is the Net Sales Index. Basically, the variable is an index of sales growth over the nine year sample period 189 with the firs t year serving as the base point. The inherent implica­

tio n fo r r e ta il managers is th a t increased sales volumes w ill tend to be related to increases in company profits. In actuality, the

implication appears logical as increasing sales volumes seem to have several logical positive influences on the profit performance of r e ta ile r s .

First, increases in sales allow companies to purchase in larger lots which normally tends to reduce the unit cost of products thereby increas­ ing gross margins. In addition, increased volumes often allow fixed costs to be allocated over greater numbers of product. The result again is increased profit margins. Larger sales volumes also often allow the more efficient utilization of variable inputs. In particular labor and promotional productivity may be increased, again resulting in improved profit margins.

Therefore, an obvious implication for retail managers appears to be the need for sales growth as a corporate objective. However, sales growth often requires the investment of funds for marketing programs or assets which might also cause changes in a firm 's financial structure.

Therefore, the need for sales growth needs to be conditioned by the implications of the other six variables.

However, before attention is turned to the interpretation of the implications offered by the remaining research variables, it should be noted that the firs t implication is in basic agreement with the findings of the PIMS study. PIMS suggests that market growth has a positive impact on profitability, which is essentially the present research's finding with regards to the net sales index. The net sales index result, though, implies that increased profits can be gained from taking sales away from competitors while the market growth rate does not specifically

address that question. Nevertheless, if one defines the market as

referring to a single firm 's area of sales, the same interpretation is

im p lic it.

The consideration of the research implications for retail managers now continues w ith the analysis o f the second v a ria b le .

Total Debt To Net Worth

The second measure to enter the stepwise analysis is the to ta l debt to net worth ratio. The simple R for measure, which is exhibited in

Table 5-2, indicates that a negative relationship exists between the ratio and profit performance. Although not extraordinarily high, the

-.28525 simple R implies that increases in the debt ratio do not have a positive relationship with profitability. In fact, the finding indicates th a t increased debt usage tends to reduce p r o f it a b ilit y .

The rationale behind why increased debt usage might be related to declining profitability lies in the cost of the debt. As more debt is employed, firms generally assume a higher risk profile to their finan­ ciers. In turn, this higher risk profile leads to higher interest rates being charged which increases a company's interest expense. As interest expenses increase,the company's net margin is cut, causing an overall reduction in profitability.

Therefore, an obvious implication to the retail food sector is that management should be careful that the desire for more sales does not lead merely to the employment o f large amounts o f debt to finance improvements in physical fa c ilitie s , increased promotional expenditures and other marketing programs designed to increase sales. 191 Again, this finding also parallels the findings of the PIMS study

u. an extent. PIMS suggest that two key determinants of profitability

are what the study calls investment intensity and fixed capital

intensity. The two are defined as follows:

Investment Intensity > -FJ.* A .C a p ita l^ .WprMncL Capital

Fixed Capital Intensity = Fix|al esP

The im p lic a tio n o f the PIMS study is th a t the employment o f large amounts

of capital in order to generate sales has a detrimental effect on

profitability. The finding of the present research is more specific;

the employment of debt financing has a negative relationship with profit

performance. When the two results are combined, the message to retail managers is: one, do not allow the firm to become capital intense in the

desire to buy increased sales volumes through high levels of marketing

expenditures; and two, do not depend on debt to make those marketing

expenditures which are necessary to secure required sales volumes.

Attention is now turned to the third variable which is identified

in the present study as having relevance to improved pro fit performance

in the retail food sector.

Gross Margin To Net Sales

The third variable to enter the stepwise regression analysis is the gross margin to net sales measure. The simple R for the measure, which

is identified in Table 5-1, indicates that a positive relationship exists between the gross margin percentage and p r o f it performance. The d ire c t

implication is that increased margins are related to higher profits, but

indirectly the implication of this third variable is much the same as the 192 previous two: that is, the firm which is better able to control its cost of good sold is likely to have higher profits. The reason for this statement is based on the fact that the gross margin is simply the d iffe re n c e between net sales and the cost o f goods sold. Therefore, increases in profitab ility can result only from sales increases which are proportionately greater than the increase in the cost of goods sold or the inverse; sales declines which are less than the decline in the cost o f goods sold.

Therefore, the key to improved profit performance lies in the manage­ ment o f the cost o f goods sold. This means b e tte r buying performance from management personnel, better information concerning the needs and desires of consumers, and perhaps the better utilization of such market­ ing tools as product display. In essence, the message to the retail food sector appears to be to buy what the consumer wants as efficiently as possible and present the goods to purchasers in a manner which does not allow the product to escape notice.

The attention of the section is now focused on the fourth variable found to have a significant relationship with retail profit performance.

Advertising Expenditures To Net Sales

The fourth variable to enter, advertising expenditures to net sales, provides an interesting finding. With a negative simple R, the impli­ cation is that increases in advertising expenditures have a negative relationship with profitability. Several interpretations of this finding seem feasible, but firs t the fact that the data used in the research includes all types of promotional expenses in the advertising classifi­ cation should be noted. Thus, such things as in store promotions like d isp la ys, tra d in g stamps, coupon o ffe rs and other premiums, as w ell as give 193 away items are included in the advertising expenses incorporated into the study.

The firs t interpretation is that the promotions undertaken by food re ta ile rs is in e ffe c tiv e . I f one examines the present r e ta il environ­ ment, this interpretation does have some intuitive appeal. First, the retail food sector seems to be rather traditional in the advertising pro­ grams used. Newspaper ads have been the mainstay and few changes can re a lly be seen when one compares the formats used over the years. Only recently has the industry shown a desire to promote in other media, or to consider anyone other than grocery stores as competition. In addition, because a ll the ads tend to be very s im ila r, very l i t t l e d iffe r e n tia l advantage is gained by the food sector through their advertising. Thus, store selections are most likely made on such other criteria as location or store atmosphere and advertising becomes an expense with little or no benefit to the company. In essence, what is said by the finding is that the promotional efforts are not offset by sales increases. Because of the data, one can not ascertain whether the culprit is the advertising programs of the food retailers or their in-store promotions.

A second possible reason fo r the fin d in g is th a t the promotional e f­ forts of the retail food sector focus on price discounts which decrease margins and thus profit performance. The traditional emphasis of the retail food industry has been the use of price-off offers, specials, and loss lea­ ders in attempts to create a competitive advantage. Such is the case in both the media advertisements used and the in -s to re promotions. The heavy reliance on margin reducing promotional price offers may in fact be decreasing the gross margin of the firm , while not offering the hoped for benefits of increased turnover for the sales item and increased sales of 194

other items within the product mix.

Because the present research indicates that gross margin is more

highly related to profit performance than inventory turnover, the proper

strategy appears to be to concentrate on promotional methods which rely

less on margin reducing price discounts. Convenience of location,

quality and diversity of product offerings, and customer services such as

special meat cutting and party catering, might be logical considerations

to begin with.

One final Interpretation of the finding is that the market for food

products may in fact be close to a purely competitive markec. Such an

in te rp re ta tio n assumes th a t consumers have a ll the necessary knowledge tc make product decisions In their own best interests. The interpretation

thereby assumes th a t a ll promotional e ffo rts are wasted because consumers

simply make product choices based upon the ir own knowledge of the good and

their need. However unlikely the Interpretation seems, retailers should

consider it.

As a final note to the consideration of the managerial implications of the advertising findings, the PIMS study results in fact concur with

the implications. PIMS states that the three key determinants of the

profitability generated by the marketing efforts of firms are the quality of products and services, relative price, and new product introductions.

PIMS also states that high marketing expenditures for low market share companies, which many food retailers are, impairs profit performance.

Thus, combining the two sets of findings, retail food companies seemingly would be well advised to; 1. carefully control all promotional expendi­ tures, 2. emphasize product and service quality in promotions, 3. be

relatively price competitive, and 4. to focus attention on the selec­ tion of new and innovative products to offer to consumers. The price 195 competitive, quality, service oriented, innovative food retailer seems most lik e ly to have improved p r o fit performance based upon the re s u lts o f the study and the PIMS research.

Attention is now directed to the fifth variable to be found to have a significant relationship with profitability in the retail food sector, average market share.

Average Market Share

The fifth variable to enter the model is the average market share.

As was found by the PIMS study, the positive simple R identified in

Table 5-2 indicates that the present research also suggests that there is a positive relationship between market share and profitability. The

rationale behind why increased market share tends to be related to

increased profits also tends to confirm some of the earlier managerial

implications drawn from the research.

In particular, larger market shares usually mean higher sales volume which effectively allows the cost of goods sold to be lowered due to

increased purchase sizes. The larger sales volume also provides a larger

base over which to spread fixed costs. That is, fixed costs have a larger

volume o f sales c o n trib u tin g to th e ir payment so the costs decline on a per

product unit basis. Both impacts effectively increase profitability.

In addition, the larger market share usually gives the firm greater

competitive power so cost savings can often be obtained in product and

promotional purchases due to volume and market power. The promotional

costs are also spread over a larger relative sales volume with increases

in market share, so the unit cost again declines. Likewise, overall oper­

ational efficiency should increase with the larger relative sales volume

due to increased space and labor u tiliz a tio n . ' 196

To exploit the implications of increased market share, a retail food company could pursue several strategies. First, a firm could choose to enter markets where l i t t l e or weak com petition e x is ts . A firm could also choose to increase the number o f o u tle ts w ith in present mar­ kets, in the hope of attracting a greater percentage of the market's sales. However, the re la tio n s h ip between market share and p r o fit per­ formance exhibited by a simple R of .12881 is not that strong. Therefore, care must be taken not to employ strategies that might improve market share yet harm overall profitability due to related impacts. For instance, building new outlets in weakly competitive markets may require increases in the to ta l debt employed by the firm and the to ta l debt u t i l ­ ized has been shown by the present research to have a stronger negative relationship with profit performance than the positive market share in flu e n ce .

Likewise, attempting to buy market share by employing more promotion

is likely to have an overall negative impact on profitability given the e a rlie r im p lica tio n s o f the negative re la tio n s h ip between a d v e rtis in g and profit performance. Unless a firm has the capability of developing an

innovative and creative promotional program which can be financed from

current operations, the chances of improved p ro fit performance are ques­

tio n a b le .

The study now considers the implications of the next most significant

variables, the cash flow ratios.

Net Worth To Cash Flow Total L ia b ilitie s To Cash Flow The sixth and seventh variables to enter the stepwise regression

analysis both focus on the ability of firms to manage their cash flows. 197

The im p lic a tio n o f both measures is th a t good cash flow management is a

necessity for improving profit performance: the positive simple R for

the net worth to cash flo w im plies a p o s itiv e re la tio n s h ip between the m inim ization o f cash flow as compared to the owners' investment and

profitability; the negative simple R for the total liabilities to cash

flow ra tio suggest th a t increases in l i a b i l i t i e s , as compared to the cash

to pay the debts, have a negative relationship with profit performance.

The im p lic a tio n to managers is th a t the need fo r cash to pay o b lig a tio n s must be balanced with the opportunity costs of holding the funds. Again, good cash management is a necessity for success in the retail food sector.

The reasons underlying the findings are not all that obscure. First, poor cash management can increase interest charges by creating the need

for more use of current debt. Such a tactic reduces a firm 's net margin.

In addition poor cash management may represent a hidden increase in the cost of goods sold. First of a ll, lack of cash may force a firm to forego cash discounts for goods. In addition, the cash may not exist to buy in

large enough volumes to secure q u a n tity discounts. Lack o f cash may also hinder the timely purchase of goods, which may lead to increased mark- downs and reduced gross margins again. Thus, the overall impact of poor cash management by retailers may be reflected in reduced margins and d e clin in g p r o fit performance. Therefore, a ll r e ta il managers should be con stan tly aware o f the need to manage th e ir cash flow s itu a tio n .

The next section considers the overall implication of the descriptive model fo r r e ta il managers. 198

The Implications of The Total Descriptive Model*

Taken as a complete unit, the total descriptive model offers several

Implications for retail food managers which might be missed in the exam­ in a tio n o f the in d iv id u a l components o f the model. The o b je c tiv e o f the present section is to collect and synthesize the interrelationships which exist among the implications of the individual variables. Each of these implications are offered as suggestions implied by the research findings which should have a positive relationship with profit performance in the retail food sector.

The firs t Implication is to increase sales, possibly by expanding product offerings, but not to use debt in the financing of the sales increases. The Implication combines the findings of the net sales index, the total debt to net worth ratio, the advertising to net sales measure, and the PIMS study. Sales have a positive relationship with profitability, debt and promotion a negative one. PIMS suggests that new or innovative products increase profitability. Combining the findings yields the firs t implication,

A second implication is that particular attention should be paid to gross margins, particularly the cost of goods sold as a percentage of sales. The consideration of the individual variables Identifies a number of potential impacts on the gross margins of retail food companies.

Because the responsibilities for the impacts cut across traditional lines of departmental responsibility, a need for coordinated control among different management areas exists. For example, the management of cash

flows is shown to have a potential Impact on the a b ility to make timely and efficient purchases. Normally the buying and financing decisions are not made in the same responsibility areas, however, the interrelation­ ship suggested implies the need for the finance and marketing areas to

jointly control the planning and evaluation for their departments.

The th ir d im p lic a tio n drawn from the model revolves around the bene­ fits of increasing a firm 's market share. Given the interpretations of the individual results,three implications for designing strategies for increasing the market share of a food retailer appear relevant:

1. focus any new outlets in less competitive markets

2. position the firm to take advantage of the competition's weaknesses; or take advantage of the firm 's own differential advantages

3. do not over extend the firm and create a need for increased debt usage.

A fourth managerial implication revolves around the need to focus a tte n tio n on the company's promotional e ffo r ts . The combined im p lica ­ tions of the model are as follows:

1. reduce dependence on margin reducing sales o ffe rs

2. finance promotions out of operating revenues

3. carefully examine the u tility of in-store promotions which may not offer a differential advantage

4. tie promotions to increase sales volume.

In summary, the present study im plies th a t a good promotional campaign

or tool for a food retailer is one which has a definable positive impact

on sales which is generated by stressing the differential advantage of

the firm rather than a product price reduction and is paid for out of

operating revenues rather than through the utilization of debt. 200

One final summary implication drawn from the analysis is the impor­ tance of cash flow management. Just as the study Implies that marketing strategies can impact financial strategies through the need for debt, poor cash flow management can have severe im p lica tio n s fo r the perform­ ance o f marketing a c tiv itie s . Not only can the poor management o f cash have an impact on pro fit performance by creating larger interest expen­ ses, mistakes in cash management can also impede the success of mer­ chandise managers by causing poorly timed purchases and reduced p ro fit margins. The implication is that a need exists for company-wide con­ siderations in the designation of proper financial and marketing goals be­ cause the two sectors have apparently interrelated impacts on profit per­ formance.

The fifth chapter now continues with a consideration of the im pli­ cations of the predictive model.

The Implications of The Predictive Model

The overall implication of the predictive model 1s that the means e x is t to accurately re fle c t the impact o f changes in fin a n c ia l and mar­ keting strategies if the impacts on the relevant variables can be deter­ mined. In the present study, if the impact of proposed modifications to strategy on net sales, the total debt employed,and the firm 's gross mar­ gin can be identified, then the change in profit performance can be esti­ mated using the regression equation presented as the predictive model.

The classification test of the model further suggests that the estimate w ill be relatively accurate.

Beyond the overall implication, the model also reinforces the impor­ tance of sales growth as a determinant of profitability. Likewise, the 201 negative relationship between the use of debt and profit performance is also confirmed. Although the relationship looks weak based upon the small beta value for total debt to net worth, the magnitude of the ratio magnifies the importance. In addition, the model also supports the suggestion raised in the earlier implications that margin management is a key to success in food retailing.

The importance of pricing 1s not discussed in the earlier sections, but whenever margins are important pricing 1s equally as Important because pricing policies determine margins. Therefore, where earlier attention is devoted to the role of margin management, the term pricing management might be applied with equal relevancy. However, 1n addition to the impact price has on margins, these policies also can effect sales volumes.

Therefore, price setting becomes very Important due to a dual Impact; on turnover and on margins. The predictive model reflects the Importance by portraying the important impact which sales growth and gross margins have on profit performance 1n the retail food sector.

The next section considers the additional management Implications offered by the secondary research findings.

The Implications of The Secondary Research firtdingsT

The secondary research considerations are not extensive, but one important implication can be drawn. A major secondary finding is that current debt appears to be the more important portion of the total debt mixed. The finding resulted from the consideration that current debt represents an average of 57 percent of the total debt for the sample firms, and when the current debt to net worth ratio is substituted into the analysis for the total debt measure, the current debt ratio achieves 202 equal significance. Therefore, the implication is that financial manage­ ment should focus attention on the management of current debt. In

reality, this is intuitively obvious since long term debt is fixed and

cannot be managed in the short run.

A second im p lic a tio n drawn from the secondary fin d in g s is th a t the

cash flow management classification seems to explain variance which is

common to other categories. Again, this seems to be intuitive as the

impact of cash management on other functional areas has been treated else­ where in the im p lic a tio n s . Poor cash management obviously can have a

detrimental impact on p ro fita b ility which extends across departmental

boundaries.

A concluding note concerning the research findings is now offered.

A Concluding Note

A variety of suggestions have been offered in the implications sec­

tio n s about the re la tio n s h ip between a good number o f p o te n tia l s tra te ­

gies and retail profit performance, but a question remains as to how all

of the implications can be transformed into better management. The pre­

sent section attempts to offer some Insight into that question.

The basic scenario this research builds for the management of retail

food stores in the 1980's and beyond is that firs t of a ll, a great need

exists for information. Better and more timely information seems a neces­

sity. Retail promotions have generally been ineffective, probably largely

because re ta ile rs do not know what consumers want o r why they want 1 t.

As UPC markings become more prevalant, a massive amount of inventory in fo r­

mation w ill become available. Firms must decide how to process, store and

use this information. 203 If companies are to better plan promotions, timely information con­ cerning the e ffe c t o f present e ffo rts must be gathered. The UPC based information system allows for product movements to be traced. Thus, tracing promotional expenditures to increases in sales volumes should be feasible. Likewise, information relative to the impact of changes in interest rates, product costs, and operating expenses must be available so th a t margins can be adjusted through the p ric in g mechanism. Again, the hardware is available to gather such information i f management knows what s p e c ific data is needed.

Complicating a ll the aforementioned areas, however, w ill be the notion that the influences on profit performance are likely to be dynamic during the next few years. The influence of inflation, interest rates, energy and labor costs, and other factors are likely to roller coaster during the period. Therefore, all strategies designed by retail manage­ ment should be fle x ib le . Fixed commitments can impede the a b ilit y o f the

retail sector to respond to environmental influences, and this could have

a negative impact on profitability. Management needs to have the fle xi­

b ility to respond to the implications of the information which the present

study implies is necessary for successful management in the retail food

sector.

The basic conclusion 1s that retail food companies desiring

improved p ro fit performance in the next few years must develop sophisti­

cated information systems which w ill provide the pertinent and timely

information necessary to employ the flexible marketing and financial

strategies needed to cope with what is likely to be a dynamic environment. 204 Suggestions For Futu re Research

The suggestions for future research efforts are broken into three areas; the firs t relates efforts that might be undertaken within the retail food industry. The present research points out two major areas where study is needed, beginning with a consideration of the impact of promotional efforts on profitability. As is noted in the research, the inform ation used in th is study is flawed because a d v e rtis in g expendi­ tures could not be separated from other promotional efforts. Given the findings of the study, the different promotional areas need to be indi­ vidually considered in an attempt to discover how to achieve the maximum benefit for promotional expenditures.

Along the same lines, the response of consumers to the different promotional types needs to be analyzed. The present research finds a negative relationship between promotional expenditures and p ro fitab ility.

Food retailers need to know why the industry's promotional efforts are ineffective.

One final topic within the retail food industry which deserves attention is the mearrs available to control the cost of goods sold. The present study found the cost of goods to be a common denominator between several of the significant predictor variables and profit performance.

The identification of the relative impact of each of the areas found s ig n ific a n t on the cost o f goods would aid r e ta il food managers in identifying proper pricing and margin strategies as well as areas for potential cost control efforts.

A second general th ru s t area fo r fu tu re research is the expansion of the effort into other sectors. In particular the same effort could be applied to other industries, but perhaps a more interesting and valuable 205 study would emerge from the extension of the project across levels of the marketing channel. Such a study could identify such things as the impacts different channel members hold on the p ro fit performance of channel members a t d iffe re n t le v e ls . The impact o f channel power concen­ trations and channel conflict on profit performance are also fe rtile areas for research.

A third extension of the research effort involves a more micro approach. A study which examined only one company at a time could control for many more of the external influences such as differing geographic locations which add error to more macro studies. Interstore differences could also thereby be studied in order to access the impact which indi­ vidual managers have on performance. In general, much b e tte r data could also be obtained given the cooperation of the company being studied. APPENDIX A

CORRELATION MATRIX OF ALL SAMPLE VARIABLES

/

206 VARIABLE NUMBER VARIABLE NAME

001 Return On Total Assets 002 Gross Margin To Net Sales 003 A ll Expenses to Net Sales 004 Operating Expenses To Net Sales 005 Grpss Margin Per Employee 006 Gross Margin Return On Inventory 007 Earn & Turn Index 008 Total Debt To Net Worth 009 Current Debt To Net Worth 010 Current Debt To Ending Inventory on Average Collection Period 012 Current Assets To Current Debt 013 Days Payables Outstanding 014 Cash + Market Securities + Accounts Receivables To Current Debt 015 Gross P ro fits To Accounts Payable 016 Net Sales To Cash + Marketable Securities 017 Net Sales To Fixed Assets 018 Net Sales To Ending Inventory 019 Net Sales To Net Worth 020 Net Sales To Accounts Receivables 021 Net Sales To Working Capital 022 Net Sales To Cash Flow 023 Net Worth To Cash Flow 024 Total Liabilities To Cash Flow 025 Current Liabilities To Cash Flow 026 Net Sales Index 027 Net Sales Per Employee 028 Net Sales Per Sq. Ft. Of Selling Space 029 Number of Sales Outlets 030 Long Term Debt Net Worth 031 Times In te re s t Earned 032 Net Sales To Current Assets 208

VARIABLE NUMBER VARIABLE NAME

033 Average Market Shares For Top 1 to 5 Markets 034 Market Share Variance For Top 1 to 5 Markets 035 Advertising Expenditures From 10-K Forms 036 Advertising Expenditures From Advertising Age 037 Net Sales 038 Advertising Expenditures From 10-K Forms To Net Sales FILE KUrlUURE (CRUTIOR MIC > l l / M / M ) > ga l —•f*TT*T""T*'T'T*'T, TTTT ” f T * T ,* T ' * T ' T ' * T " " T * T T * f • — * jl I ••••••-•••••••••••••••••••*•••• • • • * • • • • • • • • • • • • • • • • f * • Y«f f • • • • • • • • • • • f - • • • • • • • • • • f » f * •••-••••••••••••••••••••••••••••ff•••• • • • f f • • • • • • • • • • • • • • • • • • • • • • • • • • • • - • • • » T .*..—..•..TT.?T*T.*T m r . Tf * TT. ?TTT* TTTr. . . • . . — . . * T. . . T. » ! Illiiliiliiiliiiiiliilliiliillilliilil B l i i i i i i i SSSS3223SSSS j ttis m m S S ijE s •TTTTTT TTTTT* »• ‘I

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VARS13 VARS14 VARS 18 VARS 14 VAR817 VARS18 VAR919 VAR929 VAR921 VAR922 VAR923 VAR924 VAR99I S.12S5S 8.48144 8.84417 -9 .8 8 8 7 9 9.88494 9.14713 -9 .2 9 3 8 3 9.17133 -9 .9 1 3 2 9 9.97441 9.29878 -9 .9 6 2 1 2 VARM2 S.43989 8.47349 8.24777 9.22797 9.38248 9.45942 9.27921 9.14364 9.95122 9.32931 9.39616 9.37777 VAR993 S .44182 S .32841 8.28444 9.28842 9.48839 9.48921 9.37588 9 . 12882 9.96839 9.37923 9.38471 9.44424 VAR994 S .43747 S .34993 8.24782 9.24329 8.48933 9.44481 9.34434 9.14239 9.98842 9.37939 9.37466 9.44266 VARMS • S .939 IB - S .87378 -8 .8 4 4 4 9 -8.93834 9.93888 9.99892 9.94489 -9 .9 9 9 9 7 9.92447 9.11878 9.19833 9.13218 VAMN S .31197 S .47499 8.38727 9.13848 9.88398 9.83337 9.49499 9.36891 9.97219 9.38119 9.33989 9.31448 VARM7 S .24948 S .48337 8 .2 9 8 M 9.13887 9.84894 9.89873 9.39484 9.39943 9.96140 8.37717 9.33142 9 .3 *8 1 8 VARMS S.8S349 - S . 13948 -8 .8 3 8 8 9 9.20944 9.13979 9.24114 9.88443 -9 .9 8 8 3 2 -9 .9 9 8 4 4 9 . I I 168 - 9 . 12888 9 .4 29 2 * VAR999 S .48422 -8 .1 3 9 8 2 -8 .8 7 9 3 4 9.29722 9.29348 9.29972 9.93292 -9 .9 2 9 9 9 9.94789 9.13899 -9 .9 8 8 7 9 9.34318 VARSIS S .48419 8.27338 9 . 18449 9.17383 8.43882 9.71712 9.89443 9.98861 -9 .9 2 1 8 3 9.17948 9.94671 9.24278 VARSII S.8SB6I S .28818 8.37888 9.97813 -8 .8 8 8 9 4 -9.91342 -9.94847 -9 .2 9 9 9 7 9.99974 -9.29431 - 9 . 13442 -9 .9 8 1 8 2 VARS12 S.4S444 S .81228 9.38482 9.88978 9.38823 9.41821 9 .94928 9.27849 9.93813 9.21639 9.43481 9.99177 VARS13 I.SSSSS S.22S2S 9.11478 9.28328 9.21881 9.24971 9.81999 -9 .1 1 4 9 8 -9 .9 4 9 4 6 9.12141 9.99493 9.39872 VARS 14 S.22S2S 1.88888 8.31113 -8 .2 8 9 3 3 9.12944 9.34989 -9. 19399 9 . 18411 -9.92 9 4 8 9.99332 9.24176 -9 .1 1 4 2 8 VARS 13 S . 11478 S .31113 1.88999 -9 .8 8 2 8 4 -8.83888 9.18792 -9.94192 -9.93831 9.92489 - 9 . 11234 -9 .9 4 4 1 3 - 9 . 19446 VARS 14 S .28328 -9 .2 9 9 3 3 -8 .8 8 2 8 4 1.88999 9.19187 9.12983 9.28886 -9 .9 9 1 2 9 9.93293 9.1812? 9.92244 9.27139 VARS17 S .2 IS 8 I S . 12944 -8 .8 3 8 8 8 9.19187 1.98899 9.44887 9.44828 9 .34898 9.19189 9.42863 9.24276 9.18819 VARSIB 8.24971 8.34988 8.18782 9.12983 9.44887 1.99999 9 .48232 9.49988 9.94924 9.38434 9.24413 9.28698 VARS19 S.3I9SS - 8 . 18399 -8 .8 4 1 4 2 9.28884 9.44828 9.48232 1.99999 9.13476 9.98329 9.27444 9.91313 9.38214 VARS29 - S . 11498 S . 18411 -8.83 8 31 - 9 .M I2 9 9.34898 9.49985 9.13474 1.99999 9.92329 9.28813 9.21883 9.97823 VARS2I ■8.84944 -9.82848 8.82484 9.83283 9.19189 9.94924 9 .98329 9.92329 1.99999 9.93928 9.91488 -9.92229 VARS22 S . 12141 8.88332 - 8 . 11234 9.18127 9.42843 9.38434 9.27444 9.28813 9.93928 1.99999 9.73741 9.78729 VARS23 S .M 4S 3 8.24174 -8 .8 4 4 1 3 9.92244 9.24274 9.24413 9.91313 9.21883 9.91488 9.73741 1.99999 9.86183 VARS24 S.3S872 - 8 . 11428 - 8 . 18444 8.27139 9.18819 9.28498 9.28214 9.97823 -9 .9 2 2 2 9 9.78729 9.84183 1.99999 VARS28 8.24388 -8 .8 8 4 4 2 - 8 . 13418 9 .2 4 8 M 9.34934 9.28933 9.34189 9.13494 9.99889 9.88487 9.79122 9 .9 1 8 4 * VARS24 8.24849 8.21342 9.89818 9.14119 9.24244 9.33998 9.97797 9.14447 9.98783 9.99318 9.11*48 9.98821 VARS27 8.89274 -8 .8 4 8 2 2 -9 .8 8 8 2 9 -8 .9 2 7 1 4 9.93472 9.91493 9.94444 -9.93921 9.91422 9.94886 9 .1 1 9 *9 9 .9 *4 2 8 VARS2B -8 .1 8 7 1 8 -8 .8 3 8 1 4 -8 .8 9 2 7 3 9.84787 9.82812 9.97839 -9 .9 2 4 9 9 9.17719 9.98994 9.12884 9.11318 9.1*49* VARS29 S.S2SI7 S .88791 9.41444 9.88244 -8.12387 -9.99947 -9 .1 9 2 3 3 -9 .9 4 4 8 4 9.91884 9.99737 9.94874 9.97974 VARS3S S .37311 -8 .1 8 8 8 8 -8 .8 8 1 8 3 9.18479 - 9 .M I7 2 9 . 14489 9 .79288 -9 .9 7 9 8 9 -9 .9 1 2 8 3 9.9388? -9.1 8 2 9 3 9.83999 VARS3I -9 .S 3 7 4 I 9.88324 9.88879 -8.84771 9.21887 9.9891Q -9 .9 3 4 4 7 9.19447 9.99921 9 . 18179 9.28949 9.99822 VARS32 8.29394 8.87878 8.84147 8.29892 9.74788 9.84849 9.49893 9.44419 9 . 19484 9.47738 9 .2 *7 4 2 9 .4 *4 1 8 VARS33 -8 .8 8 9 4 8 -8 .8 8 1 3 4 -8 .8 8 7 4 4 -8 .8 2 4 8 2 -8.89379 -9.98842 -9 .9 4 9 9 9 9.98934 9.98622 9.92*48 9.91234 9.91886 VARS34 8.81478 -8 .8 1 8 3 7 -9 .1 4 8 7 8 -8 .8 3 1 2 4 —9.M 8 8 B - 9 . 14724 9.92177 -9 .9 7 2 3 7 9 .9 3 2 M -9.99414 -9.974*4 -9 .9 9 9 6 6 VARS33 -8 .8 1 1 3 8 - 8 . 14489 -8 .8 9 8 8 7 9.83484 9.98247 -9.97982 9.92973 9.93844 9.92944 9.14214 9.11441 9.14243 VARS34 -8 .8 2 3 8 7 -8 .2 3 2 8 8 -8 .8 4 4 3 8 9 .13840 -8.98884 -9.99293 9.94197 9.98878 9.92894 9.98491 9.99*34 9.I48M VAR837 -8 .9 1 9 8 8 -8 .2 4 4 7 4 -8 .8 8 4 2 3 9.18794 9.91834 -9.99398 9.98919 9.94987 9.93749 9 .167 *1 9.18484 * .1 * 4 * 9 WAB91B -8 .1 1 8 8 8 - 8 . UMSS -8 .8 4 4 9 4 8.84493 9.17872 9.94972 9.97932 9.94894 9.98248 9 .1 9 8 4 * -9.8198* 9.1984* 8788 BATCH SYSTEM n ix sotbmaiuc icbeatiob bate * ii/h /m i

VABBM VABBM VA6427 VAB628 VAM624 VARB34 VAM31 VAM82 VA6433 VAMM VAB636 VAMM VAMBB1 B.BBI22 B.41444 -4.84484 6.44418 -4.31187 -8 .2 7 8 4 7 6 .42742 6.12483 4.12661 4.44431 - 8 . 1*832 -4 .4 4 2 1 4 VA6662 4 .3 8 BAA 4.M427 6.47334 4.43147 4.14477 4.1*344 6.48472 4.47248 -4 .6 1 2 7 3 -4 .4 8 7 3 8 4 .42768 4.41848 VAM43 4.41823 4.17448 4.480*8 4.44162 4.14318 4 .28738 -4 .6 1 4 4 7 4 .8 1 8 7 * -4 .4 4 8 2 7 -4 .4 8 3 M 4.44917 * .*4 7 6 8 VAM444 B .41441 4 . IM B 6 4.44486 4.44323 4.14464 4.M444 6 .44337 4.81443 -4.44984 -4.*8*4* 4 .4 7 4 *4 4 .4 4 4 9 * VAMM B. IBBM 4.41434 6.41883 6.41818 4.47741 -4.44711 -4.44744 4.41886 -4.16941 -4.46341 4 .1 *3 4 9 4 . 12448 VAMM 4.31441 4.26442 4.46884 4.18648 4.14344 4.14IM 6.44424 4.7444* -4 .1 *8 8 2 -4 .1 7 3 4 4 - * .* 6 4 * 7 -4 .4 4 8 2 2 VAM47 4.33137 B.S2644 -4.14386 4.24334 4.11843 4.146*6 4.43278 4.72324 -4 .4 8 4 4 7 - 4 . 18964 -4 .4 *4 8 6 -4 .4 4 8 *7 VAHBBB B .M 3 I4 8.44381 4.42781 -4.48768 -4 .4 6 8 2 6 4.44214 -4.14318 6.38M7 -4 .4 *6 3 4 6 .4 4 4 2 * 6 .6 IM 7 4 .4 86 *9 VAM444 4.36447 4.64748 6.44446 -4.47214 -4 .4 7 *4 4 4.64919 -4 .4 4 3 4 4 4 .4 *4 4 3 -4 .4 2 4 7 3 4.47183 4.46063 4.46327 VAM IB 4 . SB IBS 4.82643 6.42434 -4.42476 4.47384 4.38448 -4.41444 4.617*4 -4.4478* -4.47734 -4 .4 7 2 6 4 -4 .4 3 8 *4 V A M tl -4 .1 3 7 4 7 4 .1 3 4 *8 -4.43738 -4.23444 4. 18242 4.143*4 -4.49347 -4. 1496* 4 .4 8 4 8 * 4 .4 9 4 *4 -4.13471 -4 .1 4 4 3 8 VARBU 4.14314 4.82862 -4 .4 6 * 3 8 -4.44374 -4.44324 -4.43783 4.16874 4.33648 4.42693 -4.43439 -4 .4 2 4 9 3 -4 .1 2 8 *4 VAMIS *.24388 8.64444 4.44274 -4.18716 4.4MI7 6.87311 -4.43761 6.29396 - 4 .4 * 4 4 * 4.61678 -4 .4 1 1 3 8 -4 .4 2 3 *7 VAM14 -4.46442 4.21M2 -4.44822 -4.43616 4.44741 -4. IBBM 6 .M 3 2 4 4 .6 7 *7 8 -4 .4 4 1 3 4 -4 .4 1 4 3 7 -4.14444 -4.23286 VAMIB -4.13414 8.44416 -4 .4 8 6 2 4 -4 .4 *2 7 3 4.41444 -4 .4 4 1 4 3 6.4487* 4.44147 -4 .4 8 7 4 6 -4 .1 9 8 7 8 - * . * • * 6 7 -4 .4 4 4 3 8 VAMIB 4.24444 4.14114 -4.42714 4.44787 4.48266 6.18479 -4.44771 4.24692 -4.42462 -4.43124 4.43464 4.18848 V A M I7 4.34434 4.64244 6.4M72 6.42812 -4.12387 -4.44172 6.2186? 6 .7 4 7 *8 -4 .4 9 3 7 * -4 .4 4 8 M 4 .M M 7 -4 .4 *6 6 4 VAMIB 4.28433 6.88446 6.61443 6.47834 -4.44447 4.144M 6.48418 6.6464* -4.46842 -4 .167M -4 .4 7 *6 2 - * .* 4 6 * 3 VAM19 4 .3 4IB 4 8.47747 6.44444 •4.4M44 -4.14233 6.74M8 -4 .4 3 * 4 7 6 .4 M 9 3 -4.444*4 4.42177 4.42973 4.441*7 VABBM 4.13444 4.1444? -4.4 34 2 1 4.17714 -4 .4 4 4 8 4 -4 .4 7 4 8 4 6.1944? 4.4441* 4.46934 -4.47237 4.43844 4.46678 VA M 2I 4.44BB4 8.48763 4.61422 4.48*44 4.41884 -4 .4 1 2 6 3 6 .4 4 *2 1 4 .1 *4 8 4 4.46422 6.483*9 4.43444 6 .4 3 6 *6 VAM22 4.06487 8.44818 6.44684 4.12684 6.86737 6.48887 6.18179 4 .4 7 7 M 4 .4 2 *4 8 -4 .4 4 4 1 4 4.14214 8 .4 64 *1 VAM23 4.74122 4.11448 6.114*4 6.11218 4.4687* -4.18243 4.88*44 4.29762 4.41234 -4 .4 7 4 4 4 4.11441 ' "*4M VAM24 4.V IB 44 8.86821 4.4*428 6.14444 4.47474 6 .8 3 4 4 * 4.44822 6.44416 4.41884 -4.4*444 4.19243 ».. 844 VAM38 1.44444 4.48644 6.4M8I 6.12144 4.44448 4.14876 4.1*4*8 4.43*12 -4 .4 1 7 3 4 -4 .4 3 3 9 7 4.64613 4 .16488 VAMM 4.46844 1.44444 -4 .4 2 3 4 4 4.44444 4.48486 -4 .4 4 4 2 7 4.66*4* 4.3827* -4.43234 -4.49*72 -4 .4 1 7 2 9 6 .4 4 M 4 VAM27 4.46461 -4 .4 2 3 4 4 1.44444 4.14247 4.88474 -4 .4 *1 9 1 -4.4*68* 4.43376 -4 .4 6 *4 4 -4 .4 * 7 3 3 4.43IM 4.43478 VAM2B 4.12144 4.44444 6.14247 1.84446 4.14474 - 4 . 144M -4 .4 * 1 4 8 4 .4 6 3 4 * - 4 .4 S 4 M 4 .437 38 4.21447 4.23492 VAMM 4.4444B 4.46486 4.48474 4.14474 1.44444 -4 .4 6 7 8 ? 4.48444 -4.444*2 -4 .1 * 7 4 4 -4 .4 3 2 *4 4 .8 *3 7 9 4 .41272 VAMM 4 .14B7S -4.44427 -4.44141 -4 .1 4 4 6 4 -4 .4 8 7 8 7 1.44444 -4 .1 8 1 6 2 4 .6 M * 6 6.42MI 4.1282* -4 .4 3 4 1 6 - 4 . 4444V VAM31 4.14446 4.64444 -4.44884 -4.44148 - 4.88444 - 4 . IB 182 1.84*88 4.13483 4.11444 4.42481 8 .4 6 4 *7 4.44372 VAM32 4 .4 B 4 I2 8.38274 4.43378 4.46844 -4.44442 6.248*8 4 .1 3 4 *3 1.44441 - 4 .6 2 M 3 -4 .1 4 7 4 2 4.44164 4 .44828 VAM33 . -4.41734 -4.43234 -4.48444 -4.48688 -4 .1 4 7 4 4 4.42861 4.11449 -4.42M3 1.66666 4.4*473 4.41727 4 .4 3 8 *8 VAMM -4.43347 -4.44472 -4 .4 4 7 3 3 4.43736 -4.43244 6 . 128M 4.42*81 -4.14742 4.4*473 1.4*44* 4 .4 *3 8 * 6 .4 2 M 3 VAM3S B.24SI3 -8.61724 6.43IM 6.21447 8.34374 -4 .4 3 6 1 6 4 .86447 4 .4 4 1 M 4.41727 4 .4 *3 8 4 1.44464 4 .8 *1 6 3 VAMM 4.14468 4.44644 6 .4 M 7 8 6.23442 4.41272 -4.4444* 6 .4 43 7 2 4 .4 48 2 8 4.43848 4.42283 4.MIB3 1.4*4*4 VAM37 4.247BB 4.61748 4 .4 4 6 *4 6.16788 4.84844 -4.61446 6.I64M 4.48847 4.48 IM 4.I46M 4.61164 4.82443 VABBM 8.16427 -4.1 6 3 8 8 6.14163 4.86884 -4.84838 -4.44767 4.446*7 4.186*4 -4 .4 2 6 3 * 4 .868 66 8 .8 4 *8 2 4.14879 212 213

| iiiliiflililiiillililiiiiiiiiiiiiiiip APPENDIX B

THE STEPWISE ANALYSIS OF THE RELATIONSHIP BETWEEN MARKETING PERFORMANCE VARIABLES AND PROFITABILITY

STEP VARIABLES ENTERING A B C D E F

1 Net Sales Index .41994 .41994 .17635 .17635 67.02 3.84

2 Net Sales Index .47684 .05690 .22738 .05103 48.53 2.99 Gross Margin To Net Sales 20.61

3 Net Sales Index .51387 .03703 .26407 .03669 43.82 2.60 Gross Margin To Net Sales 21.18 Net Sales To Advertising Expenditures 15.50

4 Net Sales Index .53218 .01831 .28322 .01915 46.07 2.37 Gross Margin To Net Sales 21.80 Net Sales To Advertising Expenditures 15.24 Average Market Share 8.28

5 Net Sales Index .53571 .00353 .28699 .00377 46.90 2.21 Gross Margin To Net Sales 23.043 Net Sales To Advertising Expenditures 15.19 Average Market Share 7.45 Number o f Sales O utlets 1.63

6 Net Sales Index .53827 -28974 45.61 Gross Margin To Net Sales 22.93 Net Sales To Advertising Expenditures 16.35 Average Market Share 7.68 Number of Sales Outlets 1.90 Net Sales Per Square Foot 1.19 214 APPENDIX B (continued)

STEP VARIABLES ENTERING A B C D E F

7 Net Sales Index .54335 .29523 45.29 Gross Margin To Net Sales 22.94 Net Sales To Advertising Expenditures 17.20 Average Market Share 6.01 Number o f Sales Outlets 1.82 Net Sales Per Square Foot 2.66 Gross Margin Per Employee 2.40

8 Net Sales Index .54786 .30015 46.12 Gross Margin To Net Sales 21.35 Net Sales To A dvertising Expenditures 18.57 Average Market Share 5.61 Number o f Sales Outlets 1.91 Net Sales Per Square Foot 3.57 Gross Margin Per Employee 4.53 Net Sales Per Employee 2., 15

9 Net Sales Index .54803 .30034 48.96 Gross Margin To Net Sales 13.32 Net Sales To Advertising Expenditures 18.43 Average Market Share 5.34 Number of Sales Outlets 1.89 Net Sales Per Square Foot 3.64 Gross Margin Per Employee 4.52 Net Sales Per Employee 2.13 Gross Margin Return In Inventory 0.08

A = The Multiple Correlation Coefficient B = The Incremental Change in the Multiple Correlation Coefficient C = The Squared Multiple Correlation Coefficient (The Coefficient of Determination) D = The Incremental Change in the Squared Multiple Correlation Coefficient E = The F-Test Value for the significance of the incremental variance explained by each predictor F = The Significant T-Test Value at the 95 percent confidence level 216

APPENDIX C

THE STEPWISE ANALYSIS OF THE RELATIONSHIP BETWEEN FINANCIAL STRUCTURE VARIABLES1 AND PROFITABILITY

STEP VARIABLES ENTERING A B C D F F

1 Net Worth To Cash Flow .29875 .29875 .08925 .08925 30.67 3.84

Net Worth To Cash Flow ,40796 .10921 .16643 .07718 60.85 2.99 Total Liabilities To Cash Flow 28.89

Net Worth To Cash Flow .44941 .04145 .20197 .03554 68.79 2.60 Total Liabilities To Cash Flow 40.57 Days Payables Outstanding 13.85

Net Worth To Cash Flow .49494 .04553 .24496 .03299 28.48 2.37 Total Liabilities To Cash Flow 10.44 Days Payables Outstanding 27.91 Total Debt To Net Worth 17.65

Net Worth To Cash Flow .52831 .03357 .27911 .03415 25.93 2.21 Total Liabilities To Cash Flow 14.57 Days Payables Outstanding 43.67 Total Debt To Net Worth 20.72 Average Collection Period 14.64

Net Worth To Cash Flow .54000 .01169 .29160 .01249 24.44 2.09 Total Liabilities To Cash Flow 14.55 Days Payables Outstanding 24.94 Total Debt To Net Worth 25.49 Average Collection Period 15.93 Current Debt To Ending Inventory 5.43

Net Worth To Cash Flow .54538 .00538 .29744 .00584 25.93 2.01 Total Liabilities To Cash Flow 5.19 Days Payables Outstanding 23.59 Total Debt To Net Worth 26.71 Average Collection Period 17.56 Current Debt To Ending Inventory 6.34 Net Sales To Cash Flow 2.55 217

APPENDIX C (continued)

STEP VARIABLES ENTERING

8 Net Worth To Cash Flow ,55133 .00595 .30396 .00652 24.70 1.94 Total Liabilities To Cash Flow 3.92 Days Payables Outstanding 25.03 Total Debt To Net Worth 26.79 Average Collection Period 13.54 Current Debt. To Ending Inventory 4.35 Net Sales To Cash Flow 3.47 Net Sales To Accounts Receivable 2.87

9 Net Worth To Cash Flow 55231 .00098 .30505 .00109 24.44 1.885 Total Liabilities To Cash Flow 3.87 Days Payables Outstanding 25.45 Total Debt To Net Worth 25.93 Average Collection Period 13.67 Current Debt To Ending Inventory 3.73 Net Sales To Cash Flow 3.38 Net Sales To Accounts Recei vable 2.76 Gross Profits To Accounts Payable 0.48

10 Net Worth To Cash Flow 55331 .00100 .30615 .00110 23.34 1.83 Total Liabilities To Cash Flow 3.48 Days Payables Outstanding 25.84 Total Debt To Net Worth 25.17 Average Collection Period 13.77 Current Debt To Ending Inventory 3.47 Net Sales To Cash Flow 3.26 Net Sales To Accounts Receivable 2.84 Gross Profits To Accounts Payable 0.50 Nr>t. Sales To Cash + Marketing Securities 0.49 218 APPENDIX C (continued)

STEP VARIABLES ENTERING

11 Net Worth To Cash Flow .55362 .00031 .30649 .00034 22.81 1.79 Total Liabilities To Cash Flow 3.09 Days Payables Outstanding 25.20 Total Debt To Net Worth 25.09 Average Collection Period 12.59 Current Debt To Ending Inventory * 0.89 Net Sales To Cash Flow 3.39 Net Sales To Accounts Receivable 2.10 Gross P ro fits To Accounts Payable 0.37 Net Sales To Cash + Marketable Securities 0.54 Net Sales To Ending Inventory 0.15

NET SALES TO WORK CAPITAL HAD A F-VALUE OF 0000 and did NOT ENTER THE ANALYSIS

A = The Multiple Correlation Coefficient For The Variables In The Step B = The Incremental Change in the Multiple Correlation Coefficient of The Step C = The Squared M u ltip le C orrelation C o e ffic ie n t For The Step (The Coef­ ficient of Determination) D = The Incremental Changes in the Squared Multiple Correlation Coefficient For The Step E = The F-Test value for the significance of the incremental variance explained by each p re d ic to r F = The significant F-Test value at the 95 percent confidence level APPENDIX D

THE STEPWISE ANALYSIS OF THE RELATIONSHIP BETWEEN FINANCIAL STRUCTURE VARIABLES2 AND PROFITABILITY

VARIABLE(S) ENTERING A B C D E F

Net Worth To Cash Flow .29875 .29875 .08925 .08925 30.67 3.84

Net Worth To Cash Flow .40796 .10921 .16643 .07718 60.85 2.99 Total Liabilities to Cash Flow 28.89

Net Worth To Cash Flow .444941 .04145 .20197 .03554 68.79 2.60 Total Liabilities To Cash Flow 40.57 Days Payables Outstanding 13.85

Net Worth To Cash Flow .49494 .04553 .24496 .03299 28.48 2.37 Total Liabilities To Cash Flow 10.44 Days Payables Outstanding 27.91 Total Debt To Net Worth 17.65

Net Worth To Cash Flow .52831 .03337 .27911 .03415 25.93 2.21 Total Liabilities To Cash Flow 14.57 Days Payables Outstanding 43.67 Total Debt To Net Worth 20.72 Average C ollection Period 14.64

Net Worth To Cash Flow .54000 .01169 .29160 .01249 24.44 2.09 Total Liabilities To Cash Flow 14.55 Days Payables Outstanding 24.94 Total Debt To Net Worth 25.49 Average C ollection Period 15.93 Current Debt To Ending Inventory 5.43 APPENDIX D (continued)

STEP VARIABLE(S) ENTERING A B CD E F

7 Net Worth To Cash Flow .54135 .00135 .29306 .00146 24.24 2.01 Total Liabilities To Cash Flow 14.25 Days Payables Outstanding 25.51 Total Debt To Net Worth 24.60 Average C ollection Period 16.19 Current Debt To Ending Inventory 4.58 Gross P ro fits To Accounts Payable 0.64

A = The Multiple Correlation Coefficient For The Variables In The Step B = The Incremental Change in the Multiple Correlation Coefficient Of The Step C = The Squared Multiple Correlation Coefficient For The Step (The Coefficient of Determination) D = The Incremental Change in the Squared Multiple Correlation Coefficient For The Step E = The F-Test Value For The Significance of The Incremental Variance Explained By Each Predictor F = The Significant F-Test Value At The 95 Percent Confidence Level 220 APPENDIX E

STEPWISE ANALYSIS OF ALL 21 VARIABLES

221 VARIABLE NUMBER VARIABLE NAME

001 Return On Total Assets 002 Gross Margin To Net Sales 003 All Expenses to Net Sales 004 Operating Expenses To Net Sales 005 Grpss Margin Per Employee 006 Gross Margin Return On Inventory 007 Earn & Turn Index 008 Total Debt To Net Worth 009 Current Debt To Net Worth 010 Current Debt To Ending Inventory o n Average Collection Period 012 Current Assets To Current Debt 013 Days Payables Outstanding 014 Cash + Market Securities + Accounts Receivables To Current Debt 015 Gross Profits To Accounts Payable 016 Net Sales To Cash + Marketable Securities 017 Net Sales To Fixed Assets 018 Net Sales To Ending Inventory 019 Net Sales To Net Worth 020 Net Sales To Accounts Receivables 021 Net Sales To Working Capital 022 Net Sales To Cash Flow 023 Net Worth To Cash Flow 024 Total L iab ilities To Cash Flow 025 Current L iab ilities To Cash Flow 026 Net Sales Index 027 Net Sales Per Employee 028 Net Sales Per Sq. Ft. Of Selling Space 029 Number of Sales Outlets 030 Long Term Debt Net Worth 031 Times Interest Earned 032 Net Sales To Current Assets VARIABLE NUMBER VARIABLE NAME 223

033 Average Market Shares For Top 1 to 5 Markets 034 Market Share Variance For Top 1 to 5 Markets 035 Advertising Expenditures From 10-K Forms 036 Advertising Expenditures From Advertising Age 037 Net Sales 038 Advertising Expenditures From 10-K Forms To Net Sales FILE m n a u t (CIEAT10S U R • Il'OO/MI VARIABLE LIST I RECREBS1QR LIST 1 BEPERBEBT VARIABLE.. VAROOI

FAMULUS) a m a or ste p s o b e r i . . » u u

HILTIPLE * I . 4 I M 4 ARALTBIS OF VARIARCE SUM OF SQUARES ■EAR SQUARE F It OQUAHC 0 .1 7 A M RECRSBBIOR # 4*14.81882 4414. 81882 47.01742 ADJUSTED R SQUARE 0.17873 RESIDUAL I. 3 1 8 4 0 .147M 48.80383 STARDARD ERROR I . 1 M H VI A 40 1 A 81 MM VARIABLES I I TEE EQDATIOR VARIABLE B BETA BID ERROR B F VARIABLE BETA IR PARTIAL 70LERARCE F VAMM 8.244844 0.41*04 0.844*8 47.017 VAR002 0.38848 0.3484* 0.43234 M .4 0 4 (OMSTAim 8.388874 VAR003 -0 .0 0 7 8 4 -0.04424 *.*4404 2 .4 3 0 VAR004 0.13484 0.18830 0.43428 4.084 VAR007 0.08837 0.03488 0.09408 0.4*7 VAMIO -0 .0 4 7 8 4 -0 .0 7 *7 7 0.89*3* 1.871 VAMII -0 .1 8 8 4 7 -0.14848 *.*8124 4.748 VAMIS O .O II4 7 0.01273 0.43218 0.081 VAMIB 0 .0 *8 3 8 0 .0 *4 2 * 0.44187 0.024 VAMI4 -0.14744 -0 .1 4 1 3 8 0.48007 8 .8 4 2 VAMIS 0 .0 *4 1 8 0 .0 *4 8 1 0 .8 *0 4 7 O.OM VAMM 0.11344 0.13313 0.474*7 4.8*3 VA M 2I -0 .0 8 *8 7 -0.0888* 0.44329 0 .4 44 VAM22 0 .0 8 8 8 * 0 .0 3 4 *8 0.44132 0.474 VAM23 0 .38848 0 .37478 0.48774 34.444 VAMM -0 .0 0 4 8 4 -0 .0 4 8 8 8 0.44441 2.878 VAM27 -0 .0 * 0 1 8 -4.4042* 0.44447 0 .0 * * VAMM -0 .0 1 8 7 8 -0.030*4 0.447*8 0.133 VAM24 -0 .0 4 8 3 8 -0 .0 8 3 *0 0 .442S I 0 .879 VAM33 0.14384 0.18447 0.44898 7.882 VAU8S8 -0 .1 *8 8 7 -0 .3 1 8 4 8 *.*047* 14.*3* 224 F ILE n m i B (CRUTIOI MIS • ll/MW) »*•*****••■••••*•••••*• RULTIPLE BE VARIABLE LIST 1 RBCRESBIOB LIST I BEFEIBERT VARIABLE.. VABM1 VARIABLE!8) ERTEBEB OR B lip BOBBER S .. VAB222

MULTIPLE R 0.09074 ARALVBI8 OF VARIARCE r SUH OF SQUARES K A H SOUARE F R ROUARE 0.24082 RBCRESBIOB 1. 4943.43974 0101. 44988 49.40410 ADJUSTER R SQUIRE 0.23094 BBBIBUAL 1. I407B.B4B44 43.49444 T . f

VARIABLES IB T K B Q U A T IM------BVI IK OH VARIABLE B BETA BTD ERROR B t VARIABLE BETA IBPARTIAL rOLERAHCE F VANM2 0.1 BAM 0.10406 0.74841 0 . 187 WRROS - 0 . 14244 -0.14032 0.44172 0 .240 VARMA 0.40233 0.MB14 0.MI43 0 .4 4 7 VARR07 -0.4 01 2 1 -0 .0 4 2 7 4 0.04070 0 .0 4 0 VARS10 -4.47AM -0.00344 0.89832 2.179 VARRII - 4 . 44472 - 0 . I0BA3 4.40M3 8.717 VAMIB -4 .4 4 4 9 4 -0 .0 0 4 3 7 0.92784 0 .0 1 3 VAMIB 0.02290 0.02414 4 .4 0 6 4 9 0 .2 1 3 VAMM -4.14973 -0 .1 7 4 1 2 4.48442 9 .2 4 9 VAMIB -4.4 4 9 4 4 -0 .0 0 8 9 7 0.04484 0.477 VARR2R 0.0A24B 0.07009 0.93074 1.080 VAM2I -0.00194 -0 .4 8 9 3 4 0.44224 1.099 VAMM -0.02498 -0.20073 0.48444 21.741 VARB34 -0.383ST -0.31477 4.40447 24.484 VARR87 -0 .0 2 4 8 9 -0 .0 3 3 7 3 0.40442 0 .8 8 4 VARB28 -0.44474 -0.40328 0.40434 0 .0 00 VAMM -0.04294 -0.07187 4.90931 1.418 VARBS3 0 . IBBM O. I0BB4 4.44074 0.001 VABBM -0.14404 -0.2 23 3 3 0.40470 14.228 225 FILE I M tk (CBEATIM B in ■ I1/S4/M) VABIABLE LIST I ■BC3ESSIQB LIST 1 w a a u r t variable. . vabmi VABIA3LB(S> MHl MB M K IT BIHKA3 4 . . V A M M

■JLTIFLE B t > M H ANALYSIS OF VAEIABCE sr aw or madams km souabe I MUM *.337*3 RBCBEMldB 4. *S**.Sa*M 2342.288** ABJOSTED------* SSBABE * .3 4 * 4 3 REBIBCAL SIS. 140*7.4428* 64.21742

VARIAUS IB HE BBDATIM — VARIABLES BOT IB I B BOOATIOR ------a b e t a s t VABIABLE ■ETA IB PARTIAL TOLEBABCE r a.AMsir • ■ M W 4. SUSA SI.47S VAMM -S. I1SB4 -*.14482 *.*B444 4 .6 *2 S.1*1*3 44.445 VABBM - * .* 4 * 3 1 -*.**343 •.4MB3A *.6 0 2 n i i i i S.1*46* 47.144 VAM47 -S. I44S I -•.IASI* *.B4*2* 7 .4 24 S .S tS M I * . TAB VAMIS -S. **4*7 -*.**A3I * . * 4 * 3 * 2.S34 VAMI1 -S.SS23I -*.aa*7A s .a a s s * 17.a a * VAMIS -S. *4434 -4.M774 * .S 1 « * * I . *3 4 VAMIA —S.S M A O - * . * • 3 4 * * .* 7 3 4 * a . 72 * VAMI* -S.ISBS9 -*.taiAS * .M « 7 3 4 .4 3 2 VAMIS -S .S S B34 - * .* * 4 A 7 *.7 3 7 *8 2 .7 * 8 V A M 2* S.*3716 *.* 4 4 6 3 *.* 3 3 4 3 * .4 2 2 VAiwai - * . * 7 * * 9 - * . * • 7 * 1 * .* * * 4 A 2 .3 3 7 VABS22 - * . * * ! * • - * .M * B > * . M 1 M 1.149 VAM27 -*.MA*3 -*.*321* * . * * 3 7 7 * . 8 l« VABS28 - * . * 3 4 * 4 - * .* 3 * 7 4 * .* * 2 3 1 * .3 * 2 VARS39 - * .* 7 4 * 1 - * .* • 2 3 3 *.4 7 8 4 4 2 .4 3 7 VAMSS *.14431 * .IT * B 3 * . * * • 4 4 (*.3 2 4 VABBM -* .1 4 * 3 3 - * .1 7 * 7 * *.*MI3 *.274 226 n u t m m uuc (chatios bate ■ u/*4/m> VARIABLE LIST I BCBCMUMI LIST I o t r t m u r v variable. . vabmi VARIABLES) ERTEREB OB STET BOHB S.. VABBM

■BLTIPLE B I . H M 9 AH ALTO 18 OP VAR1ABCE SO I OP SQUARES MAR SOUABE 7 ft BOH AM 8 .8 I7 M ftBCBBBBIOR 82*8.474*4 3744.824*8 48.11379 ABJOBTOB ft SQUARE 8.81442 RESIDUAL 17078.40744 .37.48484 KBftOft T .H W 1

VARIABLES IB TOE BBDATIOB — VARIABLES BOT IB TOE BBUATIOB ----- VAfttABLK B BCTA VARIABLE ■ETA IB PARTIAL TOURARCE P 8.821484 A.MA4I A.H4M AB.M3 VARM2 8.33M3 8.24478 8.789*9 19.783 1.497173 0.44B4S 0.1*234 M.42I VARMS -8. IM74 -8.18*88 *.*4*98 8 .1 2 8 VAftVM -I. IBBM* I . IM 7 4 VARM4 8 .1 *4 7 * 8.II4M 8.81712 4 .2 8 8 (OB8B7ART) S .O M A M VARM7 -8.8*822 -0.88*23 8.78383 0 .8 4 * VAMIS 0.02104 8.82278 8.79919 8.141 VAMII -0 .0 * 8 8 3 -8 .1 1 4 7 8 8.98849 4 .2 8 4 VAMIS 0.89443 8.18448 8.83148 2 .8 4 9 VAMIB -8 .8 8 4 2 4 -8.8M10 8.97928 S .8 M VAMI4 -8.84832 -8.87743 0.08IM 1.879 VAMIB -8 .8 8 8 8 2 -8.8*448 8.82878 0 .8 13 VAMM 8 .8 4 7 *3 0.48844 8.93874 8 .9 4 3 VAB82I -8 .8 4 2 1 8 -8 .4 7 4 8 9 8 .9 9 4 *2 1.749 VAB822 -8 .1 2 4 7 8 -8 .4 8 1 7 8 8 .2 84 *9 2 .8 M VAB827 -8 .8 1 8 8 7 -8.82244 0.98498 8 .1 8 9 VAB828 -8 .8 3 1 *4 -8.82833 8.98231 8 .4 8 4 VAMM - 8 . M IS S -8 .8 4 1 9 9 8.98741 1 .1*4 VARB33 8.14134 8.I7M2 8.9988* 9 .3 2 * VABBM - 8 . 1S884 -8.17814 8.98277 1*.142 227 FILE n m n (CBAATIOB BATE ■ Il/M/M)

BBC8C3BI0B L IS T 1 BBPOBDT VABIABLE.. VABMI VABIABLE!B) EVRBEB OH STEP IU B E B VABBI1

MULTIPLE B 0 .4 M 4 8 ABALVBI8 O f VAB1ABCE 801 OP 88UABE8 MEAB SOUABE r B BOO AM 0.841B1 BECBE88I0I 1. 1 4 3 4 4 .3 M S 4 3 4 8 1 . 34811 3 4 .8 1 8 1 0 ABJUBTEB B BO ntBl 0 .80197 ■ 1. 14434. M 744 S I . 821ST BTABBABB EBBOB 7.17787

VABIABLE8 IB TIE BBUATIOB

VABIAU B BETA 1 I BETA IB PABTIAL TOLTBABrr r 1.734337 0. 43444 48.243 VAB44S —0.134 IB -0.14438 0.44428 8.744 1.134MB B.S34IB • 14134 IS.444 VABBM -O.1818* -0 .1 1 4 3 4 0.48442 4.474 -1.407340 -4.40717 4 14438 44.448 VAB447 - O . 14448 -0 .1 4 1 3 4 0 .8 8 4 8 4 11 .4 4 8 4. M12S14 4.BIBBS 4 44444 84.374 VABBIS -4.04474 -0.04714 0.41407 4 .4 8 4 -0.88433100 41 -4.B44SI 4 17.434 VABBIS 0.04444 0 .0 4 7 1 4 0.43044 0.484 -4.4S IT 44B VABB1B -0 .4 3 7 3 4 -0 .4 3 1 4 4 O.SOB78 4.308 VABB14 -0.10448 -0 .1 3 4 8 2 0.M473 4.874 VABB1B -O. I3BBI -O.13470 0.71834 4 .1 3 1 VABB3B -0 .0 3 0 8 1 -0.03842 0.82440 0.348 VABB21 -0 .4 7 8 1 4 -0 .0 4 8 8 4 0.48873 2.888 VABBM -O .IB 8 7 4 -0 .1 0 4 3 2 0 .3 7 3 4 3 3 .8 2 1 VABB27 -0.04148 -0.04042 0.48378 0.804 VABB28 -0.07338 -0.04074 0.4M43 2.840 VABB24 -0.04704 -0.0S8M 0.48300 1.071 VABB34 0 . I8BB2 0 .3 0 2 8 7 0.44307 13.174 VABBM -0.14384 -0.28388 0.43410 1 3 .1 0 3 • n a b a t c i s w i m f i l e so pbbabk ( o b e a t is s am ■ nx**/w> VABIABLE LIST I REGRESSIOR LIST 1 bepebdert v a r ia b l e . . v a b m i VARIABLE* 8 ) o m u t OB M T V BTBWH VABOBS

BDLTIPLE B 8 .4 4 *8 7 ARALYBIS OP VARIARCE 8 m OF SQUARES BAR 8011 ABE F B BOPABE *.4 1 * 7 7 REGRESSIOR . 1*4*0.47783 1818.84*31 B * .48171 ADJUSTED B 8011 ABE * .« * ■ « * RE8IBUAL 1. IBM7.8*477 44.B48M ------T.04B47

VABIAUJtB IB 7 K BODATIOB ------VARIABLES BOT IR T K EOUATIOR ------VABIABLE ■ mCTM VABIABLE BETA IB PARTIAL TOLEBABCE P VABBM l.rtTM B.BBTM B.SSBSI 42.4M VARQQ8 -*.IBB48 -8.13226 4 .4 13 *3 B.448 1.1IBB 44 8.88898 B 187*4 3*.*34 VAMM* -8 .1 *4 4 4 -8 .8 4 1 7 3 8.44487 2.488 -I.488BB* -*.411*3 B 18*77 $7.8*8 VARM7 -8 .1 8 8 8 9 -8 .1 8 *8 2 8.88838 I8.BB7 8.8984778 B.SBBAB B VARBIB -8.SM47 -8.83*22 8.41338 8.281 VABBII -8.M*3*8*8 *1 -8.81*83 *.**679 M. 113 VABBI3 B.B643B 8 .8 *4 8 8 8.43826 8 .417 VABBBa 0.14*4944 B.IBBBB 8.84889 IS. IT* VARBIB —*.* 8 8 3 3 -8 .8 8 4 7 3 8.74423 4 .9 M (OQBBTAVT) - I . N M 4 * VAMI* - 8 . 1 M 7 I -8 .1 3 2 2 3 8 .8 B 9 I7 4 .4 $ * VAR* 18 - 8 . I I S I * -8 .1 2 7 1 7 8.71118 $ .844 VABBM -8.8 4 7 7 3 -8 .8 8 4 8 8 8.8141* 8.48B VABB21 -*.8 8 *4 1 -8 .1 1 8 4 9 8.48487 3.829 VABBM -8.1724* -*.1178* 8.27211 4 .B M VARB27 —8.8148$ -8 .8 2 4 4 4 8.4 74 1 8 8 .1 8 7 VARB28 -8 .8 *4 7 1 -8 .*8 3 4 8 8 .4 3 4 2 * 8 . I M VABBM - 8 .8 M I3 -8 .8 3 8 *7 8.43842 8.391 VABBM -8 .1 *4 8 3 - 8 .M I7 4 8.48844 18.884 229 filx avniK (creatior bate ■ u / w t VARIABLE LIST 1 RECEE8SI0R LIST I KPERBERT VARIABLE.. VABMI VARIABLES) EVTEECB M WTO I M D 7 ..

MULTIPLE R A.tMTt W A L W I8 J T VARIARCE B f B9R OP MOAREB KA H M U ARE R M W A . M N t 7. I I8 M .B B M 7 IA 4 7 .M 7 2 I S4.IS444 A A j m U B B.4ST7E RIB IN A L SB7. MIS 47. 8TA 4.80444

VARIA1XE 111 TIE BBDATIOB------VARIABLE! N T H im BBUATIOR VAR1 B K T A R l ERROR B VARIABLE BETA IB PARTIAL TOLERARCE P VABBM B. MBA 33 B.B4IS3 B VABBM -B.B4AA7 -8.12MI 8.8I84A 8.720 VABB3S I.M747B B.BBBM • IMIS M .B I4 VABBBA -0 .8 0 4 2 6 -8.874BS 8 .4 4 7 M 1.443 VABBM -I.BBMSI -B.B7BSV B IMS 4 8 .77 3 VABM7 - 8 . IBAAfl -8 .IB S 4 7 8.86821 18.Ml VABBM S.M83SA2 B.BS1M • BASAB 8 7 .2 M VAMIB -8.83 7 8 1 -8 .8 3 4 B8 8.41224 8.474 VARBII -B.S3442I IB-B1 -B.B8S! I B BM7B M.BM VAMIS O.OSS4S 8 .83848 8 .4 IS B 3 8 .2 M VABBM B.IMIBS1 B.ISMB B IS.IBI VAMIS -8.84748 -8.BB487 B .7 4 A S 8 .820 VABBM -BA4.844T -B. IBBM BB IB.BM VAMIA -0 .0 4 4 4 4 -8 .1 1 7 2 2 B .M 7 4 B 4 . M S (OMITAIT) -B.AM 1AM VAMIB - B . 10174 -8 .1 1 4 4 3 B.T B 7M 4 . M B VAMM -8.8 3 6 8 4 -8.84234 B.BI4BI 8 .6 0 8 VARB2I -8.8 7 4 4 4 -8 .8 4 8 6 3 0.47441 8.8M VAMS - 8 . IB 461 -O .I8 7 S 4 8.27183 S .6 M VAM27 -8 .8 8 4 7 8 -B.S8A38 8.44740 8 .8 1 2 VABBM -8 .8 2 8 4 4 -8.B2S7A 8.84282 8.2M VABBM -0.BB4S4 - 8 . M IB B 8.48748 B.8M 230 M BATCI IB M F ILE StPBBAU (CBEATIOO M IC ■ 11 /0 4 /8 0 > LIS T 1 BECBEMI08 LIST 1 M r n o u r r v a b ia b l x . . v a b m i i a i u i l k o m w i o mr n n a.. vam ot

H L T i n x a a b a l w i s o r v a b ia b c e M O 07 88DABE8 KA O SOUABE r a aoPAai o.Aorrt b b c b e m io b l. 13034.04846 1004.33418 32.00038 ADJUSTED a BOUAM 0.44043 BBBtMUL 14141.88413 44. 31083 4 . T M I 0

n i i M U i i i rm m o a tio s VAB1AMX ■ KTA * n 18808 a 1 VABIABLX BETA 10 PAITIAL TOLEBAJICE r VAMM a.ooana 8.87018 8.84431 44, VABP88 -0.04441 -0.12304 0.41034 4 .4 0 0 VAB023 1.070770 0.81884 0.18843 84. VAM04 0.8334* 0.10413 0.118*9 7 .4 3 0 VAB084 -1.370840 -8.84771 8 . 18301 47. VAM10 0.87*04 0.07141 0.4374* 1.872 VAB003 0.0148044 8 .4 M I4 8.07374 44, VAMIS -0.01444 -0.01374 0.841*2 0 .0 0 0 0AB011 -0.80444788-81 -8 .8 0 1 4 * 8.00444 M, VAM10 0.03237 0.0374* 0.74877 0.441 VA8B8S 0.1887887 0.14740 0.04341 13, VAM14 -0.10234 -0.13848 0.M842 0.134 VAMM - M 0 . 8418 -8.18474 M. 14308 18, VAMI8 0.04348 0.033*4 0.38481 0.331

VAB007 -8.18447408-01 -8 .1 8 0 4 8 0.00480 10, nmiii VA8080 0.08031 0.03874 0.70808 0 .M 2 (o a a v n u m -8.8108880 VA803I -0 .0 7 1 0 * -0.0*370 0.97903 3.819 VAB033 - 0 . 10281 -0.07118 0.808*4 1.003 VAD037 -0.03700 -0.04842 * .* 3 2 0 0 0 .7 17 VA8038 0 .0 0 *8 4 0.01147 0 .8 0 *2 7 0 .0 4 2 VA883* •8 .8 1 *1 1 - 0 .0 M I4 0.08441 0.1*3

r o u> p i l e n m u i i c b e a t i o r b a t e ■ VARIABLE LIST I REGREBBIOB LIST I B C rtX O U T V A B IA B LE .. Vi

VARIABLEIS) D T Iia OB 810

HDLTIFLE B 0 .4 8 7 0 * AR AL IB IS OP VABIABCE SDH o r HOB ABM M A R SOUABE B BOD ABE i. ir m REGRESS108 I207*.01712 1 0 7 5 .4 7 *0 8 ADJUSTED B I BEBIBUAL 187*7.10548 STARBOBO O f

VAB1 IB iT It ------VARIABLES BUT IB T K BBUATIOR ------VARIABLEB KTA STS ERROR I r VABIABLE K T A IB PARTIAL IQLEBABCt P VABBBA 0.BSVB34 0.8*140 0.0484* 7 0 .0 0 7 VAMOO -4 .0 * 4 3 2 -0 .1 0 4 4 2 0 .4 0 8 8 2 B.BOO VABBBA I.IIB04* 0.88*14 0.180*7 07.7*0 VABOI* 0 .0 1 7 1 4 0 .0 1 4 4 * 0.S70B* 0.004 VABBBA -1 .B A 8 I1 * - 0 .8 0 * 0 8 0 .1 8 1 8 4 4 4 .7 0 0 VAR*13 -0 .0 0 8 0 2 -0.00740 0.8*140 0.017 VABBBB B.SMBIS* O.8B08B 0 .0 8 4 0 7 0 8 .2 3 8 VARBIB 0.01487 0.01707 0 .7 3 0 4 * 0 .0 * 4 VABBII -O.BIBOIOBB-OI -O.BBAIA 0.0007A SI .070 VAR0I0 -0.09484 -O.IBOOI 0.80200 4.488 VABBBB S . I T I I I A B 0 .1 0 0 7 1 0 .0 4 0 4 0 I B .004 VAMIS -0.00718 -0 .0 0 8 2 0 0 .3 3 0 3 8 1.030 VABBBB -BAA.BOSS - 0 . I 0 0 B * 07.8108* 10.8*0 VABBBB 0 .0 2 2 S * 0.03070 0.7*270 O.BOB VABBB7 -4.3787*7*0-01 - 0 . 4 4 IBB 0 .0 0 * 2 7 1 4 .0 *0 VAR02I -0.07044 -0.10406 *.*7700 3.324 VABBBA 8.SS7BI77S-SI 0 .8 8 8 0 * 0 .0 8 8 8 0 7 .0 8 0 VAB022 —0.13*24 - 0 . 0 * 0 0 * 0.80007 B.B2I CaOHBTABT) - 8 .8 0 1 * 0 IS VAB087 -0.00032 -0.10770 0.83008 3 .0 0 7 0 .0 1 0 1 7 0 .0 1 0 0 0 S X 0 .0 8 * VABBBB f 0 .8 0 8 0 0 VABBB* -0.02*08 0 .0 8 * 0 8 0 .4 7 0 232 m batc h r a m P ILE n m u (CREAT100 BATH • I1 /*4 ^ M > VARIABLE LIST 1 RECRESSIOR LIST I DEREJBCJTT VARIABLE.. VARIABLE**) ERTEREB

W L T ir iX B * .* 9 4 1 0 ANALYSIS OP VARIABCE OP SOB o r SQUARES WAR SQUARE P B MDU1 • . H I M BBCBCSSIOS 14. 12413.98*92 1241.390*9 30.27392 ABJURES B I •■44484 BBIBOAL ••4. 134*2.3*168 44.41349 BTA 4 . ATMS

II iTIOS — — VARIABLES SOT IB T IB BQUATIOR ------VARIAHBB SETA STB SHRUB B P VARIABLE BETA IE PARTIAL TOiXRARCE P VARS36 S . *4 * 7 7 4 * . s * a n *.*4412 77.101 VAR*I* *.*2140 *.*1031 *.37429 * .1 * 2 VAR023 1.178*9* •.S4074 * .1 0 3 * 3 4 1 .4 4 4 VAR*IS -*.*•443 -*.*•440 *.39130 * . * 1 * VABB34 -1 .2 2 4 4 * 1 - 4 . M 4 7 4 * . 1 0 * 4 0 4 4 .3 * 0 VAR* 14 * .* 1 2 8 3 *.*1429 *.73324 * .* 7 1 VABM2 ••SSB3S7* *•11*23 * . * 0 4 1 * 2 * . * 4 * VAR*16 -*.**947 -*.12747 *.03*94 3.MB V A R 3II -O.SISB448B *1 - * . 2 3 4 * 3 *.*•472 22.413 VAR4I0 -4 .1 1 4 9 2 - * .* 7 7 4 * *.231*1 1.034 VAR443 *.14047*1 *.14744 *.*4411 11.940 VARB2* * .* 1 4 0 0 *.*1729 *.*9947 *.M1 VABOSO -S B 7 . *443 - * .1 4 4 * 4 47.44*03 14.413 VAR42I -* .* 7 3 4 1 - * . 1**74 *.974*3 3.1*0 VARM7 -*.30421320-*1 -*.447*4 * . * • * 2 1 1 7 .4 *4 VAR* 22 -*.14*44 -*.1*424 *.23297 3.394 I'WII *.*4«64S4f-*l * . * 4 3 * 4 • . * • 0 1 0 0 .2 2 7 VAR427 - * . * 3 1 1* - * .* 3 9 4 2 *.4 4 4 0 3 *.S 4 4 VABBM - * . * * * S 2 * .* 0 4 S * S .B 4 * VAR* 28 * .* 4 6 4 7 * .* 7 4 * 4 *.4 7 * 2 3 1.743 (OOWTAVn *! t 47SSS* VAR429 -*.*2344 -*.*3148 * . * B 4 * I 0 .0 *1 233 PILE SUPRHARK (CREATIOR BATE > ll/BA/SS) VARIABLE LIST 1 RECREBSIM LIST 1 m n n r variable.. • varmi VARIABLE!8) BRIBED OR BTEP D M R I I . . VARBIA

MULTIPLE R 8.78823 ARALVBI8 OP VARIABCE BUM OP BSDARES K A B BOO ARE P R 8BDARE I . 4 M M RBCHEBBIOR 1. 12834.SBA88 IIA A . 788T1 BA.48714 ABJURED R BBDARE #.47188 RIB IBUAL 1. ISB43.IBS78 44..88843 STARBARB ERROR A.ASA77

I■■ I 4TW IB ■■SMI ■VI IR I B R M W ItU R ------VARIABLE B BRTA STB ERROR B P VARIABLE BETA IB PARTIALTBLBURCE P VARBBA S. ISSBBB 8 . >8881 B.SAS87 BI.AAB VARBI8 B.B2S2S 8 .8 I8 8 A 8.S7A34 8.138 VARBSS I.BBBABB B.BBBBS B. IBASB 8 8.887 VARBI3 8.88683 8 .8 8 7 AS 8.88718 8.818 VARBBA -1.114847 -S.SSSAS B.IBAAA SS.A8I VARBI8 B .B IB23 8 .8 I2 S 8 8.TS4B8 8 .8 4 4 V ABB 83 B.4IBBABA B.S44B8 B.BBATB BA.BSA VARBIB -8 .1 8 3 2 4 -B.BA883 8.33881 1.488 VARBII -8.BB8BBHB 81 -B.BBA78 B.BBAAB BS.BAA VARBSS B .B I44 7 8.81784 8.A88A7 8 .8 8 8 VARBSS 8 . IABIAAA 8 . IAS48 B.BASBA 11.418 VARB2I -8.S7BS4 -8 .8 8 7 8 8 8.87883 3.888 VARBSS -BBS.7884 -8.IS8S8 AT.87A2A IS.8B8 VARB22 - 8 . I44A2 -8 .1 8 1 AS 8.383AA 3 .188 VAB88? -B.S7BAI IAD-B1 - B . BABBS B .88818 I T . I l l VAR827 -8 .8 3 4 1 4 -8.8SSA4 8.4AAA3 S.S33 VABSBA B.BBSSBBAD-BI B.SS7B8 B.BBBIA 7.887 VARBSS 8.87SA4 8.88388 8.A748A 8 .I3 S VAB88S -B.B2I9A98D-B1 -B .IB S 48 B.8S43B S.77A VAR8S8 - 8 .8 3 IBS -8 .8 3 8 4 1 8.83878 8.3AI VARBIA -B.BI8B388D-B3 -8 .8 8 8 4 7 B.BBAII S . BBS (OBRBTART) B.BI4BA78 234 s r a s b a t c b e i e r a f il e N m u t ( creatiob H i t ■ i i /4 4 / s b i I 0 3 VARIABLE LIST 1 RECREW I OR LIST I b e p e b be b t v a r ia b l e . . v m m i t i u u u ( i > b i b o «R n v m n is.. v a r ie s

■H.TIFLE I ».nM7 ARALTBI8 Of VARIANCE BT ROB OF RQ04RRB K A N W O AXE r R ROD ART 4.44887 REGRESSION 13. 13473.34487 1481.48444 A.7347I ARJOniB IMOAB 4.47883 BE8IB0AL IS344. IRRM •CARBARN ERROR 4.413S4

VARIABLES BOT IX TBE BBOATIOR

i 8 IB TRi RROATIOB------VARIABLE B BETA I B ERROR A r VARIABLE ■ETA IB PARTIAL THJRABCE r VARBBA B .I4 B B M R.44441 4.444A3 48.IA7 VARBIA 4 .4 1 I I I 4.44488 4.87384 4 .4 3 4 VARB3B 1.B341S1 •.BAITS 9 « S 2 N i SB.317 VARAIS 4.44744 4.44447 4.S47IS 4.418 VARBBA -4 .8 7 1 4 1 4 * -4.SSSAS 4.33131 I4 .IA 2 VARAIB 4 .444 44 4.44844 4.78147 4 .4 14 VARA44 A.SBTBIA2 B.SBIB4 4.44441 34.434 VAR41B -4 .4 4 4 4 1 -4.4 4 4 7 4 4.33447 1.117 VARAII -4.8AITAHB Al -4.SSTS8 4.44471 3 8 .44 4 VARABA 4.43484 4.43483 4.44148 4.348 VARBSS 0 . lABARAT 4.14774 4.443TB IS . 147 VAR43I -4 .4 4 4 4 8 -4.44 8 4 1 4 .473 84 3.437 VARBBB -44 4.8 1 84 - 4 . lA U A 44.44441 IS.S44 VARA27 -4.43444 -4.48784 4.44844 4.438 VARM7 -4. ST448TTD-4 t -4.444*1 4 .4 4 4 IB 17.441 VARBSS 4.47484 4.4B144 4.4TS48 3.413 VARAII 0.B4IAIB4R-0I 4 . BASRA 4 .4 4 B I7 4 .444 VARASA -4.4S8B4 -4.44444 4.41434 4 .4 88 VARANS •4.4438S4AB-4I -4.IIII4 4.43431 4 .4 8 7 VARA16 -4.44427433-43 -4 .4 A 4 7 I 4.44414 4 . TBS VARAS3 -4.44RAB87B-AI -4.1444B 4.44433 S. IBB < OIRBTAETl 4 . I7TABBB

ro OJ t n NLE n P M U I (CREATIOS MIS ■ 11/M/M) VARIABLE LIST I BEGREH10R LIST 1 b e t e r b e r t v a r ia b l e . . v/

VARIABLE!!) 0TESED OB BTTP IS.. VAR8SI

BDLTIPLE R B .T B 7M ARALVBIB O f VARIABCE vr SOI Of SHADES BEAR SH AR E r R SH AR E 0.4m s is. 1004.40431 s.tors ARJOBTED R 0 .478 34 M l . 4.04404

VARIABLES SOT IB m BMATIOR VARIABLE B BETA STO ERROR B r VARIABLE BETA IR PARTIAL m E R A R C E r VABSB4 S.IBBA4I 0.4ISM 0 .0 4 0 0 3 B S .409 VARS10 0 . 0 M I 7 0 .0 0 0 1 0 0.S4R 41 0 .0 0 0 VARBSS I.S I4 S S S 0 .0 4 0 3 2 0.83342 BO.ISO VARSIS -0 .0 4 3 3 4 -0 .0 0 3 4 3 0 .3 8 2 7 7 0 .0 0 3 VARBBA -0 .4 0 3 1 4 1 0 -0 .0 4 1 0 3 0.3SIS4 10.301 VARBIB 0 .0 0 0 0 4 0 .0 0 4 7 S 0 .T S 183 0 .0 1 4 VARBSS 0.SBS4SB4 0.S333S O.OSSOS 3 0 .4 0 4 VAROIB -0.044S8 -0.04711 0 .3 3 7 1 0 1 .0 0 7 VARBI1 -O.SS0421BB-OI -O.SSTSB 0 .0 0 0 4 4 3 0 .1 4 8 VARBM 0.03144 0.03083 0.441S1 0 .3 0 0 VARBSS 0 . IB7SS44 0 .1 0 3 4 0 0.0 4S 7B I S .003 VAR027 -0 .0 4 0 4 4 • 0 .0 3 4 0 0 0 .4 4 0 4 7 0 .4 4 4 VARBSS -S S 7.B 1T 4 - 0 . I B I 3 S O 4.S7B07 13 .440 VAR02S 0.07333 O.OSB4S 0.47343 3.130 VAR007 -O.SSSSOBBB 01 -0.44040 0 .0 0 4 1 0 IT.BIB VARB34 -0.03770 -0.00700 0 .0 IS B 4 0 .4 S S VARBM 0.>4414100-01 O .S 4 M 7 O.OOSI4 4.104 VARBSS -0 .0 4 4 4 2 1 BO 01 —O.10040 0 .0 3 4 IS 4 .3 4 3 VARBIA -O.S7410410-02 -0 .0 4 4 0 3 0.00404 4.044 VARBSS -O .4 S 7 S 4 4 0 0 -0 I - 4 . 1STS0 0 .0 3 0 1 4 3 . BBS VARBSl -0.14744770-OS - 0 .0 4 4 4 0 0.00133 3.437 (CSSSTART) 0 .7 7 0 4 7 4 3 236 4

n u n r a i K

VARIABLE(S) ERTERED (Ml STEP 14.

HDLTIPLE K • .V M M ARALIBIS 6 t VARIABCE sr sum or sou ares eai square r R BOD ARE 0.S B 344 BREBSIO I. ■8178.74487 A41.SMSA S l.T S T M A U t m t R I ♦. I—M USUAL 1. 1SAB7. 7SBM 4S.SS87B VTA M

V A R IA U S IS I B RSBATIOB ------VARIABLES ROT IB T K ROOATIOR ------VARIABLE B a m STB BROS B r VARIABLE a m is PARTIAL TOLERABCE r VARBM S.ITS4I4 B.41BB7 B.S44S4 B 4 .B 3 4 VARBIA B .B I4 M B .B IS I8 B . M I M B.B44 VARBSS I.SIAASS B.MB4I B.S23BI SS.S3S VARBIS B.BISSI B.B1I4S B.S7ISS B.BSA VABSS4 - 4 . M I U U B.SSIBS 14. ASS VARBIS B.B1B4A B.B1SA7 B.7aaS4 B.B4B VARSSS B.M1IA1S B.SS4BB B.BBS4S SB.AAS VARBIS -S.B7BS4 -B.B4B47 B.SIMB B .7 M VARSII -B.SIASSBIS SI -B.SSSS4 B.BB4BI ai.SAB VARBM B.B1A7I B.B3328 B.AAMI B . IA2 VARBSS B.IS474ST B.144BA B.B4S7A IS.SB6 VARB37 -B .B 3A 7A -B.BSB88 B.487B7 B.S44 VARBSS -S M .78A B -B . 17I M 7B.BS473 I4.7S 3 VARBSA -B.BBS47 -B.B47B4 •■•BBSS B.AB7 VABSBT -B.IBS!IISR-SI -B.4A4BS B.BMIB IA.B4A VARBM B.28II7S4B-SI B.BA77A B.BB81S A.BM VARBM -B . IBTMA2 -B.ISSB4 B.BS727 B.MS VARBIA -A-ASHBBSS BS -B .M 7 A 2 B.B B4M 4 . AM VARBSS •S.41BI4I4S-SI -S .IS 4 T A B.B28IS S.748 VARB21 -B.SB14SB4B-BS -B.BAB83 B.SBI2S B .7 4 I VARBSS B.AISS437R-BS B.B7SS3 •■M ASS a. iss (OOBSTABT) S.TBB4SS1 FILE SUTRHARE (CREATION M IC ■ Il^ B L 'a N ) 8 8 I 0 II VARIABLE LIST 1 REGRESSION LIST I dependent variable. . vabbbi VARIABLE! ■> arrow OR s t c f IB. VABBIB

HDLTIPLE R 0.TIB 3T ANALYSIS 07 VARIABCE 8UH OF SQUARES S A B SQUARE F R SQUARE B.0B44SI REGRESSION 132B9.20TB9 SSB.41914 M.SB040 ADJUSTED R •.4 7 9 7 ? RESIDUAL 12947.19001 41.84801 STABBARB ERROR 4 . IM 40

VARIABLES IB THE EQUATION ------VARIABLES ROT 13 TRE EQUATION ------VARIABLE B BETA STD ERROR B r VARIABLE BETA IB PARTIAL TOLERANCE r VARBBA S.S84B19 4 .42440 B.SS7B4 82.694 VARBIB B.B0B84 B.BS862 B .28042 4 .440 VARB23 I.S I27 B S B.saass B.22227 B4.879 VARBIS B.BS4BS B.B2834 B .33422 4 .244 VARBM -B.NBBBB9B -4 .2 8 0 9 7 B.2S1IB 14.989 VAMIO B.BB49B B.BBOSB B .71344 B.B14 VABBB2 B.S4I8IAI B.89973 B.B92SS IB.949 VAM2B B.02044 B.B2981 B.479B2 B.24B VARBI 1 -B.01A9A7BB-B1 -B.S2S9B B.BB482 21.428 VARB27 -B.B 2478 - B .42347 B .4 0 2 M B. 147 VARBSS B. 1BB4447 B.ISBSS B.B4S79 I2.6B7 VARB29 -4.B4BB0 -B.BS90S B.840BB 1.B4B VARBM -SAI.8I7B -B .IA 4 S I 7 9.41 7*1 I9.49B VARBB7 -B.SSB43S2D-BI -B.40224 B.BB9SI 17.4B2 VARBBA B.S94S7MD-B1 4.4SB07 B.BB9A4 9 .324 VARBM -B .IB B 4 8 I2 -B.IS409 B.B3729 8 .4 42 VARBIA -B.8749802D-B2 -B.B9483 B.BB4IB 4.044 VARB22 -B.S992AIB0-BI -« .I287B B.BM2A 2.490 VARB2I -B.SBBBA74B-B2 -B.B7BS9 B.BBI22 2 .89? VARBM B.B2BB1B4B-Q2 B.BAI9S B.BB43S 1.471 VABBIB -B .IB 1 7 9 7 8 -B .B 7 S M B . 12131 4 .7 4 4 (OOBSTABT) l.IBBBSA 238 SPSS BATCH 1 1 8111 F IL E SUTRIIARE (CHEAT I OH M i l ■ U 'B B /’M ) VARIABLE LIST I RECRESSIOR LIST 1 DCrEHDOT VARIABLE.. VAHM1 VARIABLE!S> EHTERED OR TOP HVIBER 14. VARBM

MULTIPLE B • .7 1 1 6 * ARALVB1S OP VARIABCE DP SUB OP SQUARES HEAR SQUARE P B SQUARE B .B M 3 8 REGRESSIOH It. 1 MSB.3*484 R3R.4R2M 19.1B648 ADJUSTER R Q.47B8B RER1BUAL m b . larai.asTTt « i . m m i STARBARD BBBOB • .I

VARIABLES IB T IE EQDATIOH ------VARIABLES HOT IR T IE BQDATIOH ------VARIABLE ■ BETA STB ERROR B F VARIABLE BETA IR PARTIAL TDLERABCE P VARBM S .M 8 T 7 S B.BMBT B.8SB4B 83.737 VABBIB B.M744 B.BBMB B . 3 M M B .B 87 VARBM 1.3 247 43 B.SBMI B .2 M 8 7 M . M 2 VARBIS B.B3S94 B .B 3 IIB B .S 34B 8 B . 133 VARBM -4 .B 7 4 7 B 7 * -B.B B SB 7 B .33184 1 4 .S M VARBIS B .B M 7 B B.BBBM •.*1784 B.M8 VARBM 8.8H8S432 B.SBBIB B.MSM 14.I7S VARB2B B.B3SS5 B .BB447 B.474B2 B.M7 VARBM -4.SB37S99D-B1 -B .3 I3 B B B.BMVS I B . 483 VARBM -B.BBBM -B.BM14 B.4RBB4 B . IBB VARBM B. IRIBBBI , B.I44M B .B 49B * 1 1 .0 *4 VARBM -RBI. IBM - B . 16*18 T B .tlB B I I B .*7 9 VARBB7 -B .9B 192B 1D -B 1 -B .4 S B M B .B B932 I7 .4 4 B VARSBt B.S143374R-BI B.BBBM B.BBBB3 I B . 324 VARBM -B. IBMBB3 - B . 13*47 B.BS72B R .4 7 4 V A R B lt -B.Rt2BB7BB-B2 -B.MS32 B.BMII 4.41* VARBM -4.41784840-41 - B . 184*1 B .B 2833 2 .7 3 B VARB2I -O.M7BBBSB-M -4.B7BM B.MIM B. 844 VARBM B.B84I897D-B3 B .B *S 4 3 B.BBBM 1.632 VABBIB IM7BIB -B.BBMB B . 124IB I.B T 7 VARBM -4.9*32812B-M -B.BBBM B.BBBM I.BM ICORBTAIT) t.M M M SPSS BATCH SYBTEH f i l e s u r n u u ic k a t i o b bat e - u/04/a9> VABIABLE LIST 1 RECRESSIOS LIST I DEPERDEST VARIABLE.. VABM1 VARIABLUB) 0 1 1 ID OH STEP TO 1 7 .. MS

MULTIPLE K S.TISM ABALVSIB or VABIAMZ BT SUB OP M U R E S MAH SOU ABE r B SQUARE 0.80702 RECRESS109 17. 13273.*391* 7 M .7 M 1 B 17. ABJUSTES B S .4 7S M BIS I DUAL » 7 . 4.MIA!

VABIABIES IB TEE BOUTIOS VARIABLES SOT I I I K BBUTIOR VABIABLE B BETA STB E8B0B B r VARIABLE B R A IB PABTIAL VAB034 4.B449S3 9.48109 0.84181 81.492 VAROIS -0 .0 0 4 0 0 0.19994 0 .0 0 3 VAB039 1.994070 0.49148 0.88791 8 9.432 VABBIB 0.03389 0.09490 0.99743 0 .409 VAR084 -0.9930971 -9.87089 0.84941 19.994 VARB8B 0.90909 0.03403 0.44944 0 .9 M VABB03 0 .9 9 0 4 1 » 0 .8 9 0 9 0 0.09918 14.144 VABB37 -0 .0 8 8 8 9 - o .o o is a 0 . HOBO 0 .199 VAMII -0.839001li-91 -4 .8 8 8 8 8 0.99749 17.499 VAB099 0.1914474 0.14444 0.04490 11.849 VAB098 -B 9 9 .0 I9 4 •4.14379 71.14894 12.912 VAB007 -0.80079220-01 -4.49848 0.00994 17.822 VAM904 0.00449940-01 4.44047 0 .00992 9 .8 97 VAMOB -0.11 0 7 77 2 -4 .1 8 9 4 8 0 .08789 8 .7 2 7 VARBIA -4.09997400-02 -4 .0 9 2 9 9 0.00411 4 .374 VAooaa -0.99149000-01 -0.18424 0.08949 8.884 VARB2I -4. SB174490-02 -4.04884 0.00188 8.701 VARB2S 0.94107090-08 0.04498 0.00494 1.479 VABBIB -0.14 4 0 78 7 -0.18109 O.18893 1.491 VAR429 -4.09940040-09 -0 .0 4 9 0 9 0.0 00 9 9 0 .9 99 VABBIB 0.11897410-01 0.40744 4.01898 O.BB7 (OOSSTAIT) 1.094482 STBS BA7CR 8YBTKH FILE H 7 U U U (CREATIOR M I S ■ 11/94/891 VARIABLE LIST I REGRESSIOB LIST 1 KrERDERT VARIABLE.. VARMI VARIABLE! 8) ERTERED Ml STEP RUIBER IB. VARBIB

HDLTIPLE R B .71289 ARALTOIB or VARIABCE BF Bin or B00ARE8 K A N BBDARE r R BBDARE B.BBTTB REGRESSIOB IB . 19288. 48432 798.91888 14.89889 ADJUSTED R B.4TT7A RESIBUAL 994. 19884.79828 49. A. 84222

VARIABLES U H E BOBATIOB VARIABLES ROT III T K EOUATION — VARIABLE R BETA SIR ERROR R r VARIABLE BETA IB PARTIAL TOLERANCE VARBM 8 .M 9 4 IS 9.42247 9.94224 81.441 VARBIS -8.88298 -8.88148 8 . 18988 8.881 VAR829 1.942444 9.48919 9.92772 8 9.88 8 VARB29 B.BS3IB 8.B9B48 8.44849 8.434 VAR824 -8.9 2 9 99 7 2 -9 .M T 9 9 9.99814 19.782 VARB27 -8.81794 -8.81444 8. 8.8B8 VAR882 B.9922947 9.29184 8.899M 14.249 VAMII -8.9998I92B-8I -8 .8 9 9 9 9 8.88889 17.848 VAR939 B. I9 M 7 9 7 9.I47B4 9.84489 11.992 VAR89B -98 4 .2 4 4 8 -8.14987 71.M784 12.998 VAM87 -8.9922884D-BI -8.49471 9.88997 17.848 VAR884 9.2949488B 91 9.8947B 9.91884 8.741 VAR888 - 8 . I I I M 2 4 -8 .1 4 8 8 9 9.89784 8 .7 8 3 VARBIA -8.8994124D-82 -8.89294 9.88412 4 .983 VAR822 -8.98194919*81 -8 .1 2 9 1 2 9.82974 2 .1 9 8 VAR82I -8.98872149-82 -8.84791 8.88129 2.447 VARS2B 8.48178489 82 8.87189 8.88499 1.874 VARBIB -8.1484944 -8 .1 2 2 9 9 9.19849 1.478 VARB29 -8.44182899-89 -8.88488 9.88898 1.981 VARBIB 9.19948999-81 9.88494 9.91899 9 .8 M VARBIB B.9B7849BR-8I 9.89999 9 .84888 9 .4 8 8 (OBRHTART) 1.848897

r o 8PM BATCH ETBTU n i x BUF1 HARE ICREATIOH u n * ll'**/a*> VARIABLE LIST I REGRESSIOH LIST 1 b e p e h b e r t v a r ia b l e. . v a b m i VARIABIXIB) ERTERED OH STEP RUBBER IV..

W L T I P L E R •. 7 1 3*4 ARALVBIS OP VARIABCE DP SDR Of 8 QBARES SMI ARE P R SQUARE s.Bsava RECRES8 I0 B IV. I9 3 N.SMH 7BB.4H7M IS. ADJOSTEB R *. 4 7 *7 4 RES i DUAL BPS. i m s t .t v b b s 4 3 .SIVS 4 «.U4U

VARIABLES I I T B EBDATIOH VARIABLES HOT IH THE EBDATIOH — VARIABLEBBETA STB ERROR B V VARIABLE BETA IH PAHTIAL TOLEBAHCE VARS9 S B.2 SBB4 BB.4 2 IMB.SS2 V6 BB.H7 S VARSI3 I.SSS7 S -s.vsass S.IVSSV B.BB2 VAHS2 3 I.BSSHM B.4 BMI B.2 3 7 VS M.S 3 3 VABSS7 I. 41V44 - S . I I M l s.vviav S. ISO VARS3 4 -B.VS7 SVIB -S. 3 *3 * 3 B.3 S 1 *3 IS.VBI VARSS3 S.SSSV7 SV B.3 VB7 S B.BV3 3 7 1 4 . I M VARSII - B . B M T B S M SI -B.BBSSI B.BBBMIS. * 8 7 VARSM S. IAVS8 BIB. 1 4 4 * 3 B.B4 4 S7 II.S7 7 VARSM-MT. 4 BS4 -B.ISSBB 7 1 .3 3 * 7 4 IB.SM VARSS7 -S.SSI7 SS3 D-S 1 -B.4 S 7 7 S B.BBMBIT. * 4 3 VARSSSS.BVS7 7 IIR-SI B.4 3 7 4 S B.BIBBS B.B3 S VARSM -B . 1 IS1338 -s.tsass B.B3 7 S3 B.BSV VARSIS -B.BSVBBMB SB -B.BVIBI B.BB4 I3 4 .2 4 4 VARSM -B.S4 S4 BI4 B-BI -B.IRSR4 B.B2 SM a.siB VARS2 I -S. 1V7 7 1 SSB-S2 -B.BSSB*B.BB 12B a .BBS VARSM B. SOTS AMP S3 B.BSV3 4 B.BB4 4 B 1 .781 VARBIB-B.IBBSSV7 -B.ISSBB B . 1 4 1 1 * I.7 M VARSM -S.4 4 S7 8 3 4 D-B3 -B.BBB4 I B.BBBM I.S7 B VARSISB.IS3 IBS1D-BI B.BS4 SSB.BIBSS S.* 7 4 VAMIB S.3 7 4 0 7 ASS SI B.SMVI S . * 4*81 B.4 SB VARSM B. IB4M 4 3 B S I B . B M I f B.BB7 VI S.4 BS (OOEBTAHT) I.BIBSV3 SPSS BATCH S W IU P IL E SUPBRARK (CREATION B A IT ■ II/* 4 S B * > VARIABLE LIST I REGRESSI OR LIST I BEPEHDEHT VAB IA B LE .. V A R **I

VAR IABLE**) ENTERED OR STEP RUBBER : VABB27

MULTIPLE R *.71310 AHALVBI8 OP VARIABCE DP SUM OP SQUARES K A H SQUARE P R SOU ARE * .0 * 6 0 9 REGRE8SI0R 2 * . 13913.*0719 *40.4038* 10 .213 63 ADJUSTED R SQUARE * .4 7 B I« RESIDUAL 3*4. 196*3.43*41 4 0 .7 M 1 0 STARDARD ERROR

VARIABLES ROT IH TEE EQDATIOH ----- VARIABLE B BETA BTB ERROR B P VARIABLE BETA IH PARTIAL TOLERAHCE VAR*3* 8 .2 *8 4 8 * *.4 2 1 7 7 *.**•*• 8 8 .41 2 VAR*10 >1* * . 14 *48 *.*•• VAM23 1 .9*1 8*0 Q.4S2B1 *.23833 8 * .* 0 9 VAR834 -*.*•242*0 -*.2*111 *.2*287 12.73* VARM2 *.2021273 *.2*17* *.*9309 14.187 VAR*11 —*.82*7044D-*1 -4.2MS7 * . * * 0 2 * IS .*3 4 VAR839 *.1 0 1 *7 7 2 *.1 4 *3 1 • ■ *4 *4 * 11.442 VARB38 -2*0.9*93 -*.1*2** 71.42327 12.778 VARM7 -4.4174042D-*1 - * .4 8 * 2 * * . * 1 * 7 * 1 * .* 9 * VAR*** *.**9283*D-*1 * .4 * 2 8 * * . * l * « * 8 .8 *4 VARMS -*.*974838D-*1 - * .1 2 0 0 * * . * 4 * « * 8 .9 8 1 VARBI* -*.80234*38-*2 - * .* * 2 1 7 *.**4I» 4.2*2 VAM22 -*.8*97*41D-*1 - * . I2SS7 * .* 2 0 * 6 2 .3 72 VAM 2I -*.1*82*40D-*2 - * .* 4 7 * 8 * .* • 1 2 3 2 . * 8 * VARB28 *.*4*3*S2D-*a *.*4 7 2 1 * . — *40 l.* 3 9 VARBIB -*. I8*««7« -* .1 2 1 * 8 8.14228 1 .« 14 VARB24 -*.«*—0*00 *0 - 4 .M 4 0 8 * . * • • 3 8 I.S 2 8 VARBI* *. 144I03*D-*I * . * * 2 * 7 *.* 1 8 7 2 * .* 3 0 VAR* 10 * . 20 7 **4 *D -* 1 * .* 3 3 * 7 * . * 4 * * 9 • .* * * VARB2* *.188*821D-*1 *.*33*4 * .* 2 7 9 * 8 .4 0 8 VABB27 -4.2147444D-42 -*.*1*44 *.*•**• * . 18* (OOBSTABT) 1 .*3 2 1 *4

P-LEVEL OR TBLERAHCE-LEVEL IBSUPPICIIRTSOB PIBUBOJI OOMTATIOR STATIST1CB HHICB CARROT RE OMPDTEB ARB PR IS R B AS ALL HIRES. SPSS BATCH BITTEN FILE SUPRHARK (CREATION BATE ■ 11/64/86) 8110 VARIABLE LIST I REGRESSION LIST I OEPERDUT VARIABLE.. VARS6I SUMMARY T A B U

VARIABLE MULTIPLE R k SQUARE » CHARGE SIMPLE n BBETA VARS36 6 .4 1 6 4 4 6 .1 7 6 3 0 6 17630 6 .4 1 6 6 4 8.262*86 6.42177 V A M 23 S.4SS74 6.24682 6 M 4 4 7 6 .2 6 8 7 0 1.361060 6 .4 *2 8 1 VARSM S .869S 3 6 .3 1 7 6 6 6 67618 -6.66212 -6.9*342*0 -6 .2 6 1 1 1 VARSS2 S . 1*8 2 6 6 .3 0 7 * 2 664691 6.32741 6.8021273 6.2917* VAIWII S.620*1 6.06181 6 *3389 -6.67344 -*.82670498-61 -6.23687 VARS33 S.64007 6.41677 6 *2466 6.12881 6. 101*772 6.14631 VAR638 S.66S7S 6.44606 6 *2 3 7 4 -6 .2 4 4 2 6 -2 * 1 .6 6 6 3 -6 .1 6 2 9 * VARSS7 6 .6 7 8 6 6 6 .4 0 * 7 0 6 *1620 6.17*89 -6.41760*28-61 -6.48629 VARSM S.68744 6 .4 7 2 * 2 6 61317 6.226*4 6.66928088-61 6 .4 0 2 8 6 VARSSS 6 * 6 * 4 1 8 6 .4 8 1 8 8 6 - 6 .6 8 3 * 2 -6.66743388-61 -6 .1 2 8 0 9 VARS16 S .76622 6 .4 6 6 3 6 6 6 *8 4 2 -6 .6 8 0 7 * -6.80234338-62 -6.69217 VARS22 6 .7 6 3 * 7 6 .4 6 0 0 7 6 6 *3 2 7 6.67441 -4.36679618-61 -6 .1 2 8 8 7 VARB2I 6.7S7M 6.466*3 6 6*436 -6.61329 -6 . 19826688-62 - 6 . M 7 6 8 VARS28 6.78900 6.06346 6 6*302 *.***16 6.8663*828-62 6 . M 7 2 I VARS18 6 .7 1 6 3 7 6 .0 6 4 6 2 6*6117 6.14713 -6.18M976 -6.131*8 VARSM 6.71166 6.0M38 6 6 *1 7 6 -6 .6 1 1 0 7 -6.44660488-63 -6.60408 VARBIS 6.71260 6.06762 6 66*84 6.67443 6.14610368-61 6 .M 2 9 7 VARSIB 6 .7 1 2 0 3 6 .0 6 7 7 6 6 6 6 *8 7 6 .6 4 6 1 7 6.20766468-61 6.63367 VARSM 6.71364 6.06842 6*6*73 6.17133 6.18868218-61 *.*3346 VARS27 6.71310 6.06806 6 * 6 * 1 7 - 6 .6 6 * 8 4 -6.214744*8-62 -6.61944 (CONSTANT) 1.02 918 4

r o ■£» APPENDIX F

SIGNIFICANCE TESTS OF REGRESSION COEFFICIENTS

Test For Determining If Population Regression Coefficient Is Different From Zero

2 Y 1 -r

F = regression mean square Residual mean square

(1 -r t o 2 n-2

= r 2(n-2) 1 -r2

*For details of test see Jacob Cohen and Patricia Cohen, Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (New York C ity : John Wiley & Sons, 1975), pp. 49-5(1.

245 246

APPENDIX F (continued)

Source Sum o f Squares d f Mean Square

Regression r 2 Y.y2

Residual ( l- n 2)Zy2

Financial Management

Regression (.110) (26^177.6) = 2879.536 2 1439.768

Residual (.890) (26,177.6) = 28,298,064 312 74.673

L iq u id ity Management

Regression (.017) (2*177.6) = 445.019 3 148.340

Residual (.983) (26,177.6) = 25,732.581 311 82.741

Cash Flow Management

Regression (.025) (26,177.6) = 654.44 3 218.147

Residual (.975) (26377.6) = 25,523.160 311 82.068

Asset Management

Regression (.013) (26,177.6) = 340.309 4 85.077

Residual (.987) (26,177.6) = 25,837.291 310 82.346,

Marketing Management

Regression (.247) (26,177.6) = 6465.867 6 1077.645

Residual (.753) (26,177.6) - 19711.733 308 63.999

Margin Management

Regression (.111) (26,177.6) = 2905.714 3 968.571

Residual (.889) (26,177.6) = 23,271.885 311 74.829 APPENDIX G THE PAIRWISE HIERARCHICAL ANALYSIS OF THE SIX VARIABLE SUBCLASSIFICATIONS

Marketing Management w ith Cash Flow Management

F = R2yAB-R2y-4 „ n~ka~kb '1 1-R2y-AB A kb

.36372 - .23753 x 315-6-3-1 1- .36372

- .12619 X 305 .63628 3 .19832 101.66667

= 20.163

Marketing Management w ith Margin Management

r - -30034 - .23753 „ 315-6-3-1 F - X ------3

.06281 y -I n-i rry .6 3 5 ^ X 101,667 = 9.127

Marketing Management w ith Financial Management

e _ .32694 - .23753 „ 315-6-2-1 F r x — 2 — _ .08941 u Icq ' T u r n x 158 = .13284 X 158

- 20.989

Marketing Management w ith Asset Management

r _ .26752 - .23753 „ 315-6-4-1 F = — r ~ ^ 6 7 & r x — ?—

.02999 v 304 7rm s x ~ r 248

APPENDIX G (continued)

Marketing Management w ith L iq u id ity Management

r _ .24519 - .23753 „ 315-6-3-1 F r - x — 3—

.00766 v 305 77533T X T "

.010148 X 101.66667

F = 1.032 APPENDIX H THE COMPLETE HIERARCHICAL COMPARISON OF THE SIX VARIABLE SUBCLASSIFICATIONS

Marketing Management + Cash flow Management w ith Margin Management

, R2vW yA /-W l r ~ — o *---- * i?------1-R y*AB B

F = I '41923 X 100-667 .096 X 100.667

9.664

Marketing Management + Cash Flow Management + Margin Management w ith Financial Management

.49309 - .41923 v 315-12-2-1 ------Y X j

.07386 „ -ica T m m x 150

.14571 X 150

21.856

Marketing Management + Cash Flow Management + Margin Management + F inancial Management w ith Asset Management

,50310 - .49309 w 315-14-4-7 v -;-50-3T0 X jf

,01001 X 74

,02014 X 74

1.491

Marketing Management + Cash Flow Management + Margin Management + Asset Management w ith L iq u id ity Management

.50903 - ,50310 u 315-18-2-1 r T T 5 0 gg3 X j

.00593 14, , m § r 147

.01208 X 147

1,775

249 APPENDIX I

SAMPLE VARIATIONS BASED ON MANAGERIAL STRATEGY

IDENTIFICATION GEOGRAPHIC LINE COMPANY NUMBER DIVERSIFICATION TRAI 1. Bormans 99855 1 2 2. Winn D ixie 974230 3 2 3. D ill ion 254165 3 3 4. Stop & Shop 862097 2 3 5. Bayless 72797 i 2 6. Star 825052 1 2 7. Safeway 812744 3 2 8. Wei s 948849 2 2 9. Supermarkets Gen 868443 2 3 10. Thorofare 885392 2 2 11. Sunshine Jr 867830 2 1 12. Southland 844436 3 1 13. Muniford 626144 3 1 14. Pueblo 744897 1 2 15. Niagara 653471 1 3 16. Laneco 515506 1 2 17. Shepwel1 825101 1 2 18. Pneumo 730196 2 3 19. Ruddick 781258 2 3 20. Jewell 477196 3 3 21. Hannaford 410550 2 2 22. National Tea 638097 3 2 23. C irc le K 172576 3 1 24. Fi sher 337819 2 2 25. Foodarama 347708 1 2 26. Lucky 549577 3 3 27. Motts 620127 1 2 28. Kroger 501044 3 2 29. Giant 344478 2 3 30. A&P 390641 3 2 31 . Big V 89698 1 2 32. Albertsons 13104 3 2 33. Penn 707831 1 2 34. A11ied 19537 2 2 35. Food F a ir 344708 2 2

I. Geographic Diversification Strategies 1. Firm has retail outlets in two or fewer states 2. Firm has retail outlets in from three through ten states 3. Firm has retail outlets in eleven or more states

II. Line of Trade Strategies 1. Firm operates convenience stores and related support fa cilitie s 2. Firm operates supermarkets and related support fa cilitie s 3. Firm is diversified; ie., operates food and non food retail outlets 250 APPENDIX J

ANALYSIS OF THE RESIDUAL PLOTS

251 SPSS BATCH SVSTEFI K ILE SUPRHARK (CREATION DATE = 1l^OA/BO) * ************** it ******* HULTIPL RECRESS ION ***********************

DEPENDENT VARIABLE: VAROOl FROM VARIABLE L IS T I REGRESSION L IS T I

OBSERVED PREDICTED PLOT OF STANDARDIZED RESIDUAL NUB VARDOI VAROOI RESIDUAL - 2.0 -i.o o.o i.e 2 .e 1 2.780080 9.409199 -6 .6 2 9 1 9 9 2 2 .7 0 4 4 4 4 3.262529 -0.3323304 3 1.340000 6.406359 -4.866359 4 -1 .3 3 0 0 0 0 5.368323 -6.898322 3 3 .0 0 4 9 4 4 7.997571 -4.987572 6 3.7 994 44 4 .9 1 1 2 9 2 - 1 .111293 7 4.610000 6.984404 -2.374404 8 0 .6 4 0 0 0 0 0 9 .2 4 3 2 4 0 -8 .6 0 3 2 4 0 4 HISS INC** 2.830457 HISSING** ie 2 9 .0 6 0 0 0 12.10839 16.93140 11 2 4 .6 7 0 0 0 12.26622 17.40376 12 2 6 .3 7 4 9 9 12.45526 13.92473 13 25.28944 12.72259 12.56740 14 2 6 .0 7 9 9 4 14.76203 11.31713 13 2 4 .0 3 0 0 0 13.31011 8.719891 IS 2 2 .8 7 0 0 0 12.71411 10.13389 17 22 .189 49 13.12564 9.064342 IB 20 .0 7 9 9 9 18.69143 1.388531 14 14. 1 1000 3 .1 2 4 3 0 6 8 .9 8 3 4 9 3 20 14.98000 3 .3 4 2 2 9 9 9.6 377 01 21 13.57000 3.942431 9.627367 22 14.970 00 U.931096 6.038901 23 17.030 00 10.98540 6 .0 4 4 6 0 0 24 18.43999 14.34705 4 .1 1 2 9 4 4 23 2 0 .4 0 4 9 9 14.61891 5.791079 26 2 1 .2 3 9 9 9 16 .366 68 4 .8 9 3 3 0 0 27 18.32999 21.64996 -3.319982 28 4 .8 0 0 0 0 0 3.643344 -0.7833448 24 1.730000 0.4630301 1.264961 30 4.366000 0.3424900 4.017309 31 3 .6 3 0 0 0 0 1.048316 4.781484 32 6 .3 4 9 9 9 9 2 .3 3 7 1 1 0 4 .2 1 2 8 8 9 33 8.490000 4.644746 3.845253 34 3 .7 9 9 4 9 4 2.007497 3.792502 33 3.9100041 4.438206 1.471713 36 HISS INGA* -2.731855 HISSING** 37 18.84999 15.08419 3 .7 6 5 7 4 6 38 17.48999 13.71889 3 .7 7 1 1 0 2 34 16.89000 14.64563 2 .2 4 4 3 6 5 48 13.53000 15.60033 -0.1303479 41 13.140 00 15.04438 - 1.904385 42 10.040 00 I S . 18411 -3 .1 6 4 1 1 5 43 8 .5 9 9 9 9 9 13.24779 -4.647787 44 11 .130 00 0 .0 8 4 2 3 0 3 .0 6 3 7 4 8

TOcn r o SPSS BATCH SYSTEM

45 h is s m e * * 2.3 370 41 m s s i ! » c * * 4 * 11.00000 13.25370 - I .453703 47 9 .3 4 9 4 9 9 13.54576 -4 . 145765 4B 6 .2 9 0 0 0 0 13.61280 -7.3221104 44 9 .1 5 0 0 0 0 14.64572 -1 0 .5 4 5 7 4 50 8.7 9 9 4 9 9 14.13747 -5 .3 3 7 4 7 4 51 13.52000 15.47652 - 1.456535 52 11.03000 11.04342 -0 . I342436F.-01 53 7 .6 2 0 0 0 0 11.26706 -3 .6 4 7 0 5 * 54 10.13000 4 .4 0 4 3 3 4 0 .7 2 0 6 6 3 6 55 16.06999 12.45382 3 . I 16174 56 16.10999 13.06458 3 .0 4 5 4 0 Z 57 14.82000 12.47743 I . H42070 68 11.74000 12.08666 -0 .3 4 6 6 6 5 4 54 9.450000 12.25723 -2 .8 0 7 2 2 6 64 17.16999 14.55457 2 .9 1 0 4 1 4 61 I 1.19 000 12.284 48 - 1.09497(1 62 7 .1 9 0 0 0 0 12.81133 -5 .6 2 1 3 3 5 63 9 .7 3 9 9 9 9 13.64335 -3 .9 3 5 3 5 1 64 34.12000 18.35304 15.76690 65 3 3 .4 3 9 9 9 10.76385 14.69612 66 2 8 .5 0 9 9 9 18.584 14 9.9201137 67 27.37000 14. 10140 8 . 108088 60 2 9 .7 3 9 9 9 18.68886 II.0 5 1 1 2 69 29.62000 14.35641 10.26357 70 3 0 .0 0 0 0 0 18.34403 I 1.60996 71 3 0 .4 2 9 9 9 18.43464 1 1.49529 72 2 9 .7 3 0 0 0 2 2 .1 1 3 1 3 7 .6 1 6 8 4 7 73 7 .0 7 0 0 0 0 6 .2 1 1 4 8 6 0 .8 5 8 5 1 3 3 74 6 .8 3 0 0 0 0 3 .4 1 4 1 8 3 3 .4 1 0 5 1 6 75 2 .3 8 9 9 9 9 1.825470 0 .0 6 4 5 2 9 5 76 3 .2 9 0 0 0 0 2 .0 0 0 2 3 4 1 .289760 77 1.129999 2.0 740 01 -0.9490020 78 4 .3 4 9 9 9 9 3 .3 3 4 4 4 0 1.015059 79 6 .1 5 0 0 0 0 3 .1 3 8 8 0 4 3 .0 1 1 1 0 9 80 7.730000 6.466887 I . 263111 81 HISSING** -1.060182 HISSIHCOS 82 16.73000 16.32844 0 .4 009 891 83 16.85999 18.07135 -1 .2 1 1 3 6 9 64 6 . 139999 10.077 12 -3 .9 3 7 1 1 8 85 * 4 2 .0 9 0 0 0 5.0 302 71 -4 7 .1 2 0 2 5 86 II.18000 7.1276II 4 .0 5 2 3 8 7 87 *11.30000 7. 114407 -1 8 .4 1 4 4 0 88 1.639999 5.402364 -4 .2 6 2 3 6 8 89 *6.339999 8.075284 -1 4 .4 1 5 2 8 90 “A . 4IHIUAw w w w 0 .0 6 1 6 3 4 -3 .9 8 1 6 3 4 91 H IS S IA Q t* 0 .2 5 2 2 3 8 4 HISSINC*« 92 14.480 00 10.87134 4.1 086 61 93 13.84000 13.43832 -0.4832619E-01 94 15.50000 14.44446 I .00 0 5 3 8 95 2 1 .7 8 4 4 4 15.04746 6 .7 4 2 0 3 7 96 14.35444 16.41434 2 .4 4 0 5 9 6 97 17.46444 14.60753 -1 .6 3 7 5 5 5 253 SPSS BATCH SVSTCN

9A m s s jif c * * 6.25223B4 HISSIHC** 99 h is s in g ** 6 .2 5 2 2 0 0 4 HISSING** 100 10.06000 7.4 4 1 6 7 6 2 .6 1 6 3 2 2 101 10.60000 9.364534 1.235463 102 10.21000 1 1 .447 36 -1.237337 103 10.22000 12.64904 -1.629047 104 l 2 .o:)em» 13.65363 - 1.623649 105 12.01000 13.57373 -6.7637354 100 12.93000 13.67667 -6.7466066 107 1 1 .92000 14.20417 -2 .2 6 4 4 7 4 100 HISS IOC** 6 .5 1 6 3 1 3 6 HISSING** 109 0 .5 2 0 0 0 0 12.67(619 -3 .3 3 6 0 8 8 1 10 0.669999 13.26569 -4.395096 111 0.079999 13.19326 -4.313265 112 0 .9 4 0 0 0 0 13.66409 -4.664090 f 13 9.250000 14.516 08 -3 .2 6 6 0 7 7 1 14 7.419999 13.63056 -6.436536 1 15 4.270000 11.7H773 -7 .3 1 7 7 2 7 1 10 -2 .2 7 0 0 0 0 16.22191 - 12.49191 117 H IS S IN G ** 5.6611144 HISSING** 110 9 .6 2 9 9 9 9 7 . 516261 2 .1 1 3 7 1 7 1)9 9.629999 9.999666 —6.3690661 120 -1 .429 99** 16.15362 - I I . 56562 121 6 .5 4 9 9 9 9 9 .7 7 9 2 6 6 -3 .2 2 9 2 6 9 1 °2 3.930000 6.270643 -2.326043 123 -2.419999 3. 166262 -3.6662UI 124 - 14.34000 6.644364 -26.43437 125 7.060000 3.436326 2.429674 120 HISSING** 6.2522364 HISSING** * n< Aj|«A 127 v . OvlTIFvV 9.102799 -2.242666 120 12.33606 16.2469 1 2. 161009 129 16.46999 11.66466 6 .8 6 5 3 6 4 130 16.13999 16.71643 7 .4 495 61 131 2 2 .6 4 6 0 0 12.961 IB 9.93B620 132 22.93999 15.89311 7.646673 133 23.06999 14.66677 9 .5 4 1 2 1 4 134 26.67999 16.63267 4.647917 135 21.93666 10.54115 3.468632 136 H1SSINC** 6.2522364 HISSING** 137 h is s in g ** 6.3912250 HISSING** 130 9 .3 3 6 6 6 6 6 .9 7 6 7 6 7 3 4 .3 5 3 2 1 2 139 4.776666 -1.124668 5.894066 140 9.646666 -2.642966 7.682973 141 7.826666 -2.437327 16.27733 142 1 . -1.526962 8.846965 143 RISSINC** -3 .6 5 6 1 9 3 HISSING** 144 HISS 160** -4.183996 HISSING** 145 12.99666 0.114658 4 .8 7 5 9 4 0 6 •) 8A8A6 146 16. WVW 6.496749 4.563256 147 9.256666 7.3 1 3 4 7 1 -2 .6 6 3 4 7 1 140 • .4166666 6 .4 6 7 2 7 6 -6 .6 5 7 2 6 9 149 -4 .1 1 6 6 6 6 6 .6 3 6 6 6 1 -1 6 .7 4 8 6 6 150 -2.269999 7.243494 -9 .4 5 3 4 9 3

r\3 SPSS BATCH SYSTEM

151 1.8?;:ar 8:N=22_S:J2Jii j ; «=2=2 = 2 = *« r;« cj^ ^ ><;(: J a 2 air: 583:^Ni=i2222_»St:SJS22Jeiai« ci ‘ - - * j ^ ,*f;ci ? *2 ^JNCINCAMNfl CJMNCINMPICIN^tl C^MCICINNNCINMNCi Nei ei CIMMNnCICICj N jM C I C I C n N M M I iC C I C iM ie iC e iN ie C N M N I C N N N I C I C M ^ lC C I lC t ^ N I C I P M N I C N M J lC lf f N M A C N I C N J i^ s e ^ » • ■■ • •• - E • . .. c N N « o n - n * « - - ^ N O n n t n n n s - N N N f - N i f '1 • • ♦ •••••« • • ■ * • • * « • • ••— £ «NN» - - , - - B«eN%NA»K N■ •«< • «N ■— r ! i •

256 SPSS BATCH 6YSTEH

257 8 .3 7 9 9 9 9 12.79910 -4 .4 1 9 1 0 0 258 12.130 00 14.07019 -1 .9 4 0 1 8 7 259 10 .686 00 11.23370 -0 .5 3 3 7 0 0 5 260 11.01000 14.33018 -3 .3 2 0 1 7 8 261 HISSING** 3 .8 5 8 9 4 6 HISSING** 262 9 .3 6 0 6 0 0 2 1 .0 9 4 5 6 - I I . 73457 263 2.160660 12.53123 -1 0 .3 7 1 2 3 264 -1 6 .3 1 0 0 0 7 .0 6 6 2 6 6 -1 7 .3 7 6 2 5 265 1.879999 9 . 121)254 -7 .2 4 8 2 3 6 266 -1 6 .4 8 9 9 9 6 .2 2 2 4 5 8 -2 2 .7 1 2 4 5 267 —.AW • ^ 6 ■1 VWU1AAAA W 5 .2 3 8 4 9 3 -5 .6 4 8 9 9 2 268 2.320000 8 . 144154 -5 .8 2 4 1 5 4 269 6 .4 8 6 0 0 0 0 8 .2 1 8 4 1 0 -7 .7 3 8 4 0 9 270 HISSING** 1.769974 HISSING** 271 HISSING** 0 . 7863IIH 1 HISSING** 272 HISSING** 6 .4 6 3 0 3 0 HISSING** 273 HISSINC** 5.909230 HISSING** 274 HISS IN C ** 6 .8 5 7 9 6 0 HISSING** 275 HISSING** 7 .4 1 0 6 9 6 HISSING** 276 HISSINC** 9 .8 7 7 H I3 HISSING** 277 HISSINC** 8.4 558 61 HISSING** 278 HISSINC** -1 .0 6 8 3 9 3 HISSING** 279 HISSINC** -1 .4 1 5 9 4 2 HISSING** 280 13.64000 6 .7 6 8 7 6 4 6 .8 7 1 2 9 4 281 13.68000 3 .4 8 5 9 3 4 10.19406 282 13.55600 3 .2 3 5 4 3 0 10.31457 283 13.06000 2 .7 1 4 6 7 4 10.34532 284 13.32000 2. 131314 11.21869 285 13.88000 3 .6 9 2 5 8 1 10.187 42 286 14.11060 3 .5 6 2 6 0 2 I | o | iyin 10.548 00 287 11* 1.9 873 50 9 .8 5 2 6 4 9 288 HISSING** -1 .2 3 2 0 6 7 HISSINC** 289 16.42000 16.27811 0.1418060 290 14.470 00 13.83123 0 .6 387 711 291 12. 10000 14.93695 -2 .8 3 6 9 5 0 292 15.480 00 15.985 58 -0 .5 0 5 5 8 2 9 293 16.31000 15.05285 1.257147 294 2 1 .7 5 0 0 0 16.624 30 5 . 125501 295 8191 ■ OfMMkA**OTW 15.23649 6 .0 4 3 5 1 0 296 18.120 00 15.54269 2 .5 7 7 3 0 7 297 HISSINC** 3.309949 HISSING** 298 -2.599999 -2 .3 6 3 9 3 9 -0 .2 3 6 0 3 9 4 299 -3 .2 5 9 0 9 9 -2 .8 8 9 3 8 2 -0 .3 7 0 4 1 1 3 900 1.3 899 99 -0 .8 6 9 0 9 8 6 2 .2 5 9 0 9 8 301 2 .0 7 0 0 6 0 -0 .3 4 1 2 4 6 5 2 .4 1 1 2 4 6 302 8 .1 1 0 0 0 0 0 -0 . 1023816 0 .2 1 2 3 8 1 8 303 -2 .5 8 9 9 9 9 -4 .1 4 1 2 0 8 1.551213 304 -1 0 .5 3 0 0 0 -4 .5 2 9 9 3 5 -6 .0 0 0 0 6 1 305 -0.889999 -6 .1 8 2 0 3 4 -3 .2 0 7 9 6 2 306 -3 .2 0 9 9 9 9 - I3 .3 0 8 U I 10.09882 307 5.190000 4 .1 5 2 6 8 9 1.0 373 10 308 5 .0 2 0 0 0 0 3 .3 1 6 5 3 6 1.703469 309 2 .5 4 0 0 0 0 1 .1 5 4 9 5 8 1.385041 257 J

SPSS BATCH SYSTKH 3 ie 1. 43O M 0 3 .1 *7 3 4 0 - 1 . 137340 *1 3ii 2.0611000 2.636 143 •.2 2 3 0 0 4 0 * 312 - | . 740000 2.4 B 5 II55 -4.2251155 * I 313 2.254444 •.0434551 1.414544 I * 314 2.204541 - 1.664341 * I 315 HISSING** 1.326344 HISSING** 1

DURBIN-WATSON TEST OP RESIDUAL DIFFERENCES COMPARED BY CASE ORDER (SEOftUH). VARIABLE L IS T I . REGRESSION L IS T 1. DURBIN-WATSON TEST 0 .9 8 4 9 B APPENDIX K

THE FIRMS INCLUDED IN THE RESEARCH SAMPLE

1. Bormans 2. Winn D ixie 3. D illio n 4. Stop & Shop 5. Bayless 6. Star 7. Safeway 8. Weis 9. Supermarkets Gen 10. Thorofare 11. Sunshine Jr 12. Southland 13. Muni ford 14. Pueblo 15. Niagara 16. Laneco 17. Shepwell 18. Pneumo 19. Ruddick 20. Jewell 21. Hannaford 22. National Teas 23. C irc le K 24. Fisher 25. Foodarama 26. Lucky 27. Motts 28. Kroger 29. Giant 30. A&P 31. Big V 32. Albertsons 33. Penn 34. A llie d 35. Food F a ir

259 APPENDIX L

THE CLASSIFICATION TEST ANALYSIS OF THE PREDICTOR MODEL

CNJ X 4/1 o> T3 in t/t V) tn T3 40 to m to to (/> **■■) "O Oi o 4-> 0* o 01 o a> o 01 <_> u u 4-> 4-> 4-> 4-» n Q. o to Ol ID 0) 40 a> YO i / ) 2 a ; 3 GO GO Z OC 00 CO 4-> +J 4-> o r v 4-> N N I--. ** r-- o o o y COMPANY c r 0 ) O i Q cr> L. r- Z r — h * •— o £ Cl < CL « CL CL 1. Star 2.56 197.05 21.41 8.99 10.13 l 1 2 2 3 2 2. Albertsons 5.05 270.93 23.88 13.94 12.89 l 1 1 1 2 2 1 1 3. Pueblo .69 232.93 24.66 4.37 6.16 2 2 3 3 4 3 3 3 4. Southland 3.73 203.07 27.08 14.15 9.93 1 1 1 2 2 2 5. Mumford 2.23 314.14 31.43 8.45 0.34 2 2 2 3 3 4 1 3 6. Niagara 2.47 88.70 13.09 8.87 21.95 2 1 /. 1 3 1 7. Shopwel1 1.77 227.26 18.54 4.64 1.99 2 2 3 3 3 4 3 3 8. Pneumo 4.27 166.37 19.69 13.67 5.97 1 2 1 3 2 3 1 3 9. Ruddick 2.58 132.22 19.67 8.99 10.97 2 1 2 2 3 2 10. Supermarkets Gen. 3.32 347.14 22.33 6.32 6.35 2 2 3 3 3 3 3 3 11. Hannaford 2.67 255.39 21.47 7.35 8.72 2 2 2 2 3 3 12. Giant 2.48 192.25 27.16 11.35 10.81 ■ 1 1 2 2 2 2 13. Foodarama 2.69 95.07 20.17 12.19 6.64 1 2 2 2 2 3 14. Kroger 2.25 198.11 21.00 7.98 9.27 2 2 2 2 3 3 15. Penn Traffic 3.98 91.28 21.21 16.05 15.37 1 1 1 1 1 1 1 1 16. D illio n 7.45 104.58 5.56 17.93 18.41 1 1 1 1 1 1 1 1 17. Stop & Shop 2.45 271.78 22.60 6.72 6.35 2 2 3 3 3 3 3 3 18. Safeway 3.06 202.85 23.01 10.75 9.76 1 1 2 2 2 2 19. Weis 2.79 22.03 26.59 17.57 29.73 1 1 1 1 1 1 1 1 20. A&P 1.30 222.00 22.47 5.37 -4.86 2 2 3 3 3 4 3 3 21. Sunshine 4.12 43.91 23.80 19.07 14.93 1 1 1 1 1 1 1 1 22. Bormans 1.90 274.19 24.55 6.04 4.73 2 2 3 3 3 3 3 3 23. Bayless 3.04 120.31 23.78 13.76 9.72 1 1 1 2 2 2 24. F isher Foods 1.29 272.71 25.20 4.80 4.78 2 2 3 3 3 3 3 3 25. M o tt’ s 3.81 79.63 18.12 14.71 16.69 1 1 1 1 1 1 1 1 26. Cudek 6.18 172.99 25.11 20.61 14.15 1 1 1 1 1 2 1 1 27. National Tea .61 214.65 18.71 2.27 6.65 2 2 1 2 4 3 1 28. Big V 4.73 150.17 18.78 15.00 17.48 1 1 1 1 1 1 Correct Classifications 24 o f 28 22 o f 26 17 o f 28 l b O t I B Projected Class-| = The p rofita b ility class the predictive model assigns observation to where the sample is split in halves based on the actual return on total assets for the years 1969-1977.

Actual Class-j = The profitability class the observed sample return on total assets fa lls into where the sample is sp lit in halves based on the return on total assets for the years 1969-1977.

Projected Clas $2 = The profitab ility class the predictive model assigns observation to where the sample is sp lit in thirds based on the actual return on total assets for the years 1969-1977.

Actual Classy = The profitability class the observed sample return on total assets falls into where the sample is sp lit in thirds based on the return on total assets for the years 1969-1977.

Projected Class, = The pro fita b ility class the predictive model assigns observation to where the sample is split in fourths based on the actual return on total assets for the years 1969-1977.

Actual Class, = The profitability class the observed sample return on total assets falls into where the sample is split in fourths on the actual return on total assets for the years 1969-1977.

Projected Class- = The p ro fita b ility class the predictive model assigns observations to where the sample is split into thirds based upon the actual return on total assets for the years 1969-1977, and the middle third is omitted from the analysis. The sample median is then used to determine if the classification sample observation below go in the top or bottom third.

Actual Class^ = The profitability class the observed sample return on total assets falls in to where the sample is split into thirds based upon the actual return on total assets for the years 1969-1977, and the middle third is omitted from the analysis. The sample median is then used to determine i f the c la s s ific a tio n sample observation below go in the top or bottom th ird . APPENDIX M

DATA PROVIDED BY THE COMPUSTAT FILES

261 ANNUAL INDUSTRIAL, OVER-THE-COUNTER AND CANADIAN FILES DATA ITEMS 262

DAT UNIVERSAL ITEI CHARACTER NO DATA ITEM NAME UNITS PRECISION

1 Cash and Short Term Investments MM* *.8 2 Receivables MM* 8,8 3 Inventories MM* 8,8 4 Current Assets (Total) MM* 8,8 6 Current Liabilities (Total) MM* 8,8 6 Assets (Total)/Liabilities and Net Worth (Total) MM* 8,3 7 Plant — Grose MM* 8.8 8 Plant — Net MM* 8,3 9 Long-Term Debt (Total) MM* 8,3 10 Preferred Stock at Liquidating Value MM* 8,3 11 Common Equity (Tangible) MM* 8,3 12 Sales — Net MM* 8,8 13 Operating Income before Depreciation MM* 8,3 14 Depreciation and Amortization MM* 8,3 16 Interest Expense MM* 8.3 16 Income Taxes (Total) MM* 8,3 17 Special Items MM* 8.3 18 Income before Extraordinary Items and Discontinued Operations MM* 8,3 19 Preferred Dividends MM* 8.3 20 Available for Common after Adjustments for Common Stock Equivalents MM* 8.8 21 Common Dividends MM* 8,3 22 Price — High * * 8 t h » 8,3 23 Price — Low SA.&hl. 8.3 24 Price ~ Close * A 8ths 8.3 / 25 Common Shares Outstanding M 10,3 26 Dividends Per Share **« 8,3 27 Adjustment Factor (Cumulative) Ratio 10,6 28 Common Shares Traded M 10,3 29 Employees M 8,3 30 Capital Expenditures MM* 8.8 31 Investments in and Advances to Unconsolidated Subsidiaries MM* 8.3 32 Investments in and Advances to Others MM* 8.8 33 Intangibles MM* 8.8 34 Debt in Current Liabilities M M * 8.8 36 Deferred Taxes and Investment Tax Credit (Balance Sheet) MM* 8.8 36 Retained Eaminp MM* 8.8 (Common Shares Outstanding — Class A on Canadian File) 37 Invested Capital (Total) MM* 10,8 38 Minority Interest (Balance Sheet) MM* 8,3 39 Convertible Debt and Preferred Stock MM* 8,3 40 Common Shares Reserved for Conversion M 10,8 ANNUAL INDUSTRIAL, OVER-THE-COUNTER AND CANADIAN FILES 263 DATA ITEMS

DATA UNIVERSAL ITEM CHARACTER NO. DATA ITEM NAME UNITS PRECISION

41 Cost of Goods Sold MM$ 8.8 42 Labor and Related Expense MM* 8.3 43 Pension and Retirement Expense MM* 8.3 44 Debt Due in One Year MM* 8.8 45 Advertising Expense MM* 8.3 46 Research and Development Expense M M * 8.8 47 Rental Expense M M * 8.3 48 Extraordinary Items and Discontinued Operations MM* 8.3 49 Minority Interest (Income Account) M M * 8.3 50 Deferred Taxes (Income Account) M M * 8.3 51 Investment Tax Credit (Income Account) MM* 8.3 (Currency Conversion Rate on Canadian File) 52 Tax Loss Carry Forward MM* 8.3 63 Earnings Per Share (Primary) — Including Extraordinary Items and Discontinued Operations * * < 8.3 54 Common Shares Used To Calculate Primary Earnings Per Share MM 8,3 65 Unconsolidated Subsidiaries — Equity in Earnings MM* 8,8 66 Preferred 8tock at Redemption Value MM* 8.8 57 Earnings Per 8hare — Excluding Extraordinary Items and Discontinued Operations — F u lly Diluted **« 8.3 58 Earnings Per Share (Primary) — Excluding Extraordinary Items and Discontinued Operations « * < 8.3 59 Inventory Valuation Method Code 8.3 60 Common Equity (As Reported) MM* 8.3 /

EXPANOEO ANNUAL INDUSTRIAL ANO EXPANDED OVER-THE COUNTER DATA ITEMS

61 Non-operating Income/Expense MM* 8.3 62 Interest Income MM* 8.3 63 Income Taxes — Federal MM* 8,3 64 Income Taxes — Foreign MM* 8,3 66 Amortization of Intangibles MM* 8.3 66 Discontinued Operations MM* 8.3 67 Receivables (Estimated Doubtful) MM* 8.3 68 Current Assets (Other) MM* 8,3 69 Assets (Other) MM* 8.3 70 Accounts Payable MM* 8,3 71 Income Taxes Payable MM* 8,3 72 Current Liabilities (Other) MM* 8,3 73 Construction in Progress MM* 8.3 74 Deferred Taxes (Balance Sheet) MM* 8.3 76 Liabilities (Other) MM* 8.3 76 Raw Materials MM* 8,3 77 Work in Process MM* 8,3 78 Finished Goods MM* 8.3 EXPANDED ANNUAL INDUSTRIAL AND EXPANDED OVER-THE-COUNTER 264 DATA ITEMS

DATA UNIVERSAL ITEM CHARACTER NO. DATA ITEM NAME UNITS PRECISION

79 Debt (Convertible) MM6 8,3 80 Debt (Subordinated) MM$ 8.3 81 Debt (Notes) M M f 8,3 82 Debt (Debentures) M M f 8,3 83 Debt (Other Long-Term) MM$ 8,3 84 Debt (Capitalized Lease Obligations) M M f 8,3 85 Common Stock M M f 8.3 80 Treasury Stock (Total Dollar Amount) M M f 8,3 87 Treasury Stock (Number of Common Shares) M 10,3 88 Present Value of Non-Capitalized Leases M M f 8.3 89 Unfunded Pension Costs — Vested Benefits M M f 8.3 9U Unfunded Pension Costs — Past or Prior Service M M f 8,3 91 Debt Maturing in the Second Year M M f 8,3 92 Debt Maturing in the Third Year M M f 8.3 93 Debt Maturing in the Fourth Year M M f 8,3 94 Debt Maturing in the Fifth Year M M f 8,3 95 Minimum Rental Commitments in Five Years (Total) M M f 8,3 96 Minimum Rental Commitment in One Year M M f 8,3 97 Retained Gamings (Unrestricted) M M f 8.8 98 Order Backlog M M f 8,8 99 Retained Gamings (Restatement) M M f 8.8 100 Property, Plant and Equipment (Beginning Balance) M M f 8.8 101 Property, Plant and Equipment (Retirements and Sales) M M f 8.3 102 Accumulated Depreciation (Beginning Balance) M M f 8.3 / 103 Accumulated Depreciation (Additions) M M f 8.4 104 Accumulated Depreciation (Retirements, Renewals, Etc.) M M f 8.3 105 Accumulated Depreciation (Ending Balance) M M f 8,3 106 Unremitted Earnings o f Unconsolidated Subsidiaries (Statement of Changes in Financial Position) M M f 8.3 107 Sale o f Property, Plant and Equipment (Statement of Changes in Financial Position) M M f 8.3 108 8ale of Common and Preferred Stock (Statement of Changes in Financial Position) M M f 8.3 109 Sale o f Investments (Statement of Changes in Financial Position) M M f 8.3 110 Total Funds from Operations (Statement of ' Changes in Financial Position) M M f 8.3 111 Issuance of Long-Term Debt (Statement of Changes in Financial Position) M M f 8.3 112 Total Sources of Funds (Statement of Changes in Financial Position) MMf 8.3 113 Increase in Investments (Statement of Changes APPENDIX N

THE STEPWISE ANALYSIS OF THE SIGNIFICANT PREDICTOR PROFILE

265 VARIABLE NUMBER VARIABLE NAME

001 Return On Total Assets 002 Gross Margin To Net Sales 003 A ll Expenses to Net Sales 004 Operating Expenses To Net Sales 005 Gross Margin Per Employee 006 Gross Margin Return On Inventory 007 Earn & Turn Index 008 Total Debt To Net Worth 009 Current Debt To Net Worth 010 Current Debt To Ending Inventory Oil Average Collection Period 012 Current Assets To Current Debt 013 Days Payables Outstanding 014 Cash + Market Securities + Accounts Receivables To Current Debt 015 Gross P rofits To Accounts Payable 016 Net Sales To Cash + Marketable Securities 017 Net Sales To Fixed Assets 018 Net Sales To Ending Inventory 019 Net Sales To Net Worth 020 Net Sales To Accounts Receivables 021 Net Sales To Working Capital 022 Net Sales To Cash Flow 023 Net Worth To Cash Flow 024 Total Liabilities To Cash Flow 025 Current Liabilities To Cash Flow 026 Net Sales Index 027 Net Sales Per Employee 028 Net Sales Per Sq. Ft. Of Selling Space 029 Number of Sales Outlets 030 Long Term Debt Net Worth 031 Times In te re s t Earned 032 Net Sales To Current Assets VARIABLE NUMBER VARIABLE NAME

033 Average Market Shares For Top 1 to 5 Markets 034 Market Share Variance For Top 1 to 5 Markets 035 Advertising Expenditures From 10-K Forms 036 Advertising Expenditures From Advertising Age 037 Net Sales 038 Advertising Expenditures From 10-K Forms To Net Sales

/ / SPSS BATCH STBIEH M IX BVPMAHI ICREATIOR BATE * 1 1 /04 /8 0 ) VARIABLE LIST REGRESSIOP LIST depehdert v a r ia b l e . . v a a m i VARIABLE(B) OTTERED OS STCP BUBER 1.. VAR334

MULTIPLE R 0.41994 All ALTS IS OP VARIARCE DP S im OP SQUARES HEAR SQUARE F R SQUARE 0 . 17439 RECRE88IOII I. 4414.31032 4414.31032 47.91742 ADJUSTED R SQUARE 0 .17372 RESIDUAL 313. 21840.I472B 48.88232 8TAMDARD ERROR 0.39994

VARIABLES IB THE EBDATIOH VARIABLES HOT IR THE EOUATIOR — VARIABLE BETA STD ERROR B P VARIABLE BETA IR PARTIAL TDLERARCE VAR924 3.249844 0.41994 0 . 09498 4 7 .01 7 VAR042 0.23399 0.24890 0.93224 (OORSTART) 8.283379 -0.2 84 8 8 -0.31 4 4 7 0.99999 3 4 .42 8 V A M I3 0.01197 0.01273 0.93218 0.081 VARQ2Q 0.11294 0.12313 0.97907 4 .803 VARQ23 0.28948 0.2 79 7 8 0.98779 24.494 VARQ24 -0 .0 9 8 8 9 0.99441 2.879 VAR033 0.14284 0.18497 0.99898 7 .8 82 VAR338 - O .19037 -0 .2 1 3 4 3 0.98479 14.920

CTtr o CX> SPSS BATCH SYSTEM PILE 8DPRHARE (CREATION MIT ■ Il/AA/DA) VARIABLE LIST 1 REGRESSION LIST 1 DEPENDENT VARIABLE.. VARNAI VARIABLE(S) ENTERED OR STEP RUBER 2 .. VARSM

MULTIPLE R t . M B U AR ALTO IB OP VARIANCE SUH OF SQUARES HEAR SQUARE P R SOUARE S .29864 RECRESSIQR 6778.10488 3388.89229 84.42299 ADJUSTED R SOU ARE 0.283 88 RESIDUAL 19484.29882 42.19947 STANDARD ERROR T .88688

------VARIABLES I I THE EQUATION ------VARIABLES ROT IN IDE EQUATION — VARIABLE B BETA STD ERROR B P VARIABLE BETA IN PARTIAL TOLERANCE VARS2A 8.288838 8.42184 8.87724 74.484 VAR882 8.34738 8.37323 8.88899 88.338 VAR888 -8.244I489D-81 -8.28488 8.88423 84.428 VAR8I3 8.22868 8.21898 8.67944 18 .6 8 2 (CONSTANT) 4.818887 VAM29 8.89897 8 . 1 1818 8.97388 3.816 VAH823 8.22178 8.28388 8.97118 21.412 VAR824 8.84137 8 .8 4 3 S 1 8.82818 8 .898 VAR833 8.14819 8.16272 8.99889 8 .4 8 9 VAR838 -8 .2 1 4 2 4 8.98371 14.964

VARIABLES) ENTERED ON STEP SUM E R S. VAR882

MULTIPLE R 8.48184 ANALYSIS OP VARIANCE t SUM OP SQUARES K A N SQUARE P R SQUARE 8.84191 REGRESSION 3. 9473.83988 3187.84683 88.79744 ADJUSTED R SQUARE 8.38874 RESIDUAL I. 16782.94382 83.78721 STANDARD ERROR 7.82882

VARIABLES IN THE EQUATION------ROT IR THE EQUATION ------VARIABLE B BETA SID ERROR B VARIABLE BETA IN PARTIAL TOLERANCE P VAR824 2.841837 8.83188 8 . 4 4 .8 1 2 VARA13 8.81879 8.88916 9.44844 9.926 VARANS -8:33248970-81 -8 .3 8 2 7 8 8.88411 4 8.849 VARA28 8.88339 8.86842 9.98811 1.332 VAR892 8.4193848 8.34738 8.88418 VAR823 8.89648 8 . 18673 9.78139 8.872 (CONSTANT) 8.8864148 VARA24 -8.86948 -8.87482 9.74919 1.748 VAR833 9 . 14899 8.17629 9.99888 9 .943 VAR838 -8.17934 -8.22243 9.98328 16. 166 SPSS BATCH SYSTEM PILE SUPRHARK ICREATIOB BATE * 11/04/89) VARIABLE LIST I REGRESSIOR LIST 1 DETERDCRT VARIABLE.. VAHMI VARIABLE(S) UTTERED OR STEP SOMBER 4 . VARB38

MULTIPLE R S .42733 ASALVBIS OP VARIARCE DP SUM OF SQUARES ICAR SQUARE R SQUARE 0.89394 RECRES8I0R 4 . 14341.41101 2078.34288 80.29013 ADJUSTEDR SQUARE Q.S8B7I RESIDUAL 314. ID87S.07109 81.20991 8TAHDARD ERROR T .IB 4 II

VARIABLES IR THE N D A T IO H------VARIABLES HOT IR TTE EQUATIOR - VARIABLE B BETA STD ERROR B VARIABLE BETA IR PARTI V . TOLERARCE VARS24 3 .8 9 B I9 2 0._. 0.38779 44.923 VAR413 -0 .0 2 4 4 2 ■ 4 .5 2 1 2 4 0.48224 0 .1 4 0 VAROOB -0.3244108D-01 -0 .3 7 8 7 8 0.00401 46.181 VAR420 0.07114 0.08901 0.94949 2 .448 VAR002 0.4144780 0.34338 0.08772 81.889 VAR323 0.10014 0.11348 0.78114 4 .044 VAR038 -281 .8 34 9 -0 .1 7 9 8 4 74.09884 14.144 VAR024 -0.03421 -0 .0 3 9 7 0 0.72880 0 .4 88 (OORSTART) 2.308971 VAR433 O .13428 0 17484 0.99818 9.743

VARIABLE!S) ERTERED OR STEP RUBER 8 . VARSS3

MULTIPLE R 0.44193 ARALVBIS OP VARIARCE BP SUM Of SQUARES ICAR SQUARE P R SQUARE 0 .41208 REGRESSIOR 8 . 10786.47988 2187.33892 43.31849 ADJUSTED R 0.40286 RESIDUAL 809. 18389.80302 49.80819 8TARDARD ERROR T .08728

VARIABLES IB THE ESBATIOR------VARIABLES ROT IR T IC EQUATIOR — VARIABLE B BETA STB ERROR B VARIABLE BETA IB PARTIAL TOLERARCE VAR024 8.434923 0 .2 14 4 4 O. 4 7 .8 4 0 VARS13 -0 .0 2 8 1 7 -0 .0 2 2 0 7 0.48224 O.ISO VAROOB -0.82843908-01 -0 .8 7 4 8 9 O. 4 7.723 VARA 24 0.04191 0.07880 0.94820 1.910 VAR802 0.4149494 O.S4S7S 0.8 04 9 3 S3.139 VAR923 0.09734 0 . 11218 0.78087 8 .924 VAR038 -27 6 .1 1 4 8 - O . 17870 49.18172 18.943 VAR924 -0 .0 4 8 4 0 0.72814 0 .0 4 2 VAR033 0.1407243 0 .1 8 4 2 8 0 .0 48 0 8 9 .7 4 3 fCORSTART) 0.

r o O SPSS BATCH SYSTEM PILE BUFRMARK (CREATION M T E ■ II/04/88I BIO VARIABLE LIBT I REGRESSION LIST I DEPENDENT VARIABLE.. VAROOl VARIABLE!S) ENTERED OR STEP NUIBER S .. VARS23

MULTIPLE R 0.44747 ANALTBIS OP VARIANCE DP SDR OP SQUARES H AN SOD ARE P R SOUARE 0 .4 1 *4 7 REGRESSION 6. 10980.36899 1830.0610* 37.09229 ADJUSTED R SQUARE 0.40017 RESIDUAL 008. IB 196.11661 49.33804 STANDARD ERROR 7.03410

VARIABLES IN TOE EQUATION------VARIABLES ROT IN THE EQUATION — VARIABLE B BETA STD ERROR B VARIABLE BETA IN PARTIAL TOLERANCE VAM26 3.441404 0.31048 0 .0 8 1 4 * 4 8.270 VAR0I3 -0 .0 *9 7 4 —O.OOB83 *.4 4 0 0 4 0 .0 2 2 VAROOB -0.00349340-01 -0 .8 4 9 4 2 0 .0 *4 0 9 00.1*2 VAR02* 0.04900 0.06242 *.9 2 2 9 6 1.2*1 VAROOB 0.0890*41 0 .29788 0.06318 32.097 VAR024 -0 .1 8 4 6 7 -0 .1 8 7 *9 *.4 2 0 0 9 7 .7 6 8 VAR03S -37 8.4 4 42 - O .17733 4 8.83868 16.087 VAR033 0.139*880 0.13470 0.04488 9 .4 *0 VAR023 0.329*374 0.09734 0.14407 3.934 (CONSTANT) 0 .7 1 *4 0 *0

VARIABLE! 8) ENTERED OR STEP NOSER 7 . . VAR334

MULTIPLE R *.4 0 8 4 4 ANALYSIS OP VARIANCE DP 8DM OP SQUARES KAN SQUARE R S8DARE 0.43380 REGRESSION 7. 11300.38171 1422.19739 3 3 .4*1 7 3 ADJUSTED R SQUARE 0 .4 2 *8 9 RESIDUAL 0 *7 . 14821.1*089 4 8.27 72 * STANDARD ERROR 4 .94818

VARIABLES IN T IE EQUATION ------VARIABLES SOT IN THE EQUATION — VARIABLE B BETA STD ERROR B VARIABLE BETA IN PARTIAL TOLERANCE VAR026 2.448727 0.01443 0.04741 49.428 VAR013 -* .0 * 3 1 4 -0 .0 *2 7 8 *.4 4 4 9 3 *.**2 VAROM -*.2 2 3 7 9 20D-O1 -0 .2 8 7 4 4 0.00496 2C 9*3 VAM 2* *.0 4 1 4 4 *.*0 2 7 9 *.9 1 8 9 2 0 .800 VAR002 *.0880689 *.2 9 7 *3 0.04249 82.*.. VAR*38 -23 9 .8 1 1 8 -* .1 8 2 4 * 0 9 .0 *4 8 2 1 1 .9*4 VAR033 * .1 4 1 *6 4 * *.1 0 7 4 8 *.*4 4 4 1 10.221 VAM 23 *.7211107 0.21334 0.2 16 2 8 11.117 VAR024 1.6382480 ~ * . 10467 0 .2 2 9 *0 7 .7 4 8 (CONSTANT) *.3431326

PO 8T88 BATCH SVBTEH f i l e s u p r h a r k (c h e a t i n ' m i x ■ ii/vo/asi VARIABLE LIST I RECRES8I0R LIST I a m m a n variable. . varmi VARIABLE'S) UTTERED OR STEP RUBBER 8 . . VARS24

HDLTIPtX R 4.68983 ARALTOI8 Of VARIARCE vr sdh or sod ares ikar square i R SOPARE S.43838 RECRE88I0R 8. 11396.69244 1424.88686 29 49487 ABJUBTED R SODABE 4.43462 RESIDUAL 346. 14779.79416 48.29997 STARBARD ERROR 4 .4 4 9 8 3

VABI IR IRE MOAT I OR ------VARIABLES ROT IR THE E0UATI0II — VARIABLE B BETA 81V DOOR B r VARIABLE BETA IR PARTIAL TOLERARCE r VARS24 8 .4 4 9 2 4 4 4 .3 1 1 3 2 4 .3 8 4 3 4 4 7 .3 4 2 VARS18 4.41386 4.41144 9.41648 4 .4 3 7 VARS48 -4.23846888-41 -4 .2 8 7 2 6 4 .4 4 4 9 6 2 4 .2 9 2 VAR442 4 .3 8 8 3 4 8 4 4 .2 9 4 3 3 4 .4 6 2 6 1 3 2 .2 4 6 VAR938 -2 4 6 .6 5 6 1 -4 .1 8 6 9 6 6 9 .9 1 3 9 9 12 .446 VAR43S 4 .1 3 9 1 7 6 4 4 . 13478 9 .4 4 4 8 2 9 .7 7 4 VAR823 4 .6 8 8 8 4 7 7 4 .2 4 3 7 9 4 .2 1 9 1 2 9.883 VAR924 •4.6242423 -4 .1 8 4 6 1 4 .2 2 9 8 6 7 .3 9 4 VAR424 4.23427978-41 4 .4 4 1 4 4 4 .9 2 4 9 4 4 .8 8 8 IOORSTART) 4 .S 4 3 4 1 14

r\5 TO 8P88 BATCH SYBTEH riL£ 8UPRHARK (CREATION BATE ■ ll/OO/'SR) MULTIPLE REGRESSION «••«•*•*••• VARIABLE LIST I RECRE88I0R LIST 1 DEPEHBEBT V AR IABLE.. VAR881 VARIABLES) ENTERED OB STEP RUHER V. VAM 1S

HDLTIPLE R 0. ANALYSIS OP VARIARCE DP BUH O f 800ARES WAR 800ARE R 8 0 0 ARC 8 .4 8 8 4 0 REGRESSIOR 9 . 11398.49306 1366.49933 3 6 .1 3 9 8 3 ADJUSTED R 8 .4 1 8 7 V RESIDUAL 880. 14777.98874 4 8 .4 0 3 4 3 8 .8 8 8 7 8

VARIABLX8 IR T K BBBATIOB ------VARIABLES ROT IR T IE EOOATIOR — VARIABLE B BETA S IR ERROR B VARIABLE BETA IR PARTIAL TOLERARCE VARB36 3 .3 9 8 0 8 3 8 .3 8 9 0 8 8 .8 0 7 9 7 4 4 .7 8 4 VARBBB - 8 .33788343-81 -4 .3 6 1 4 1 3 .8 8 0 3 1 18 .3 8 4 VAR883 8 .8 4 8 8 3 4 8 8 .3 8 7 1 4 8 .8 7 7 3 8 3 8 .1 6 3 -3 4 0 .1 1 4 0 -8 .1 0 8 9 8 7 8 .4 7 9 9 9 12.898 VAR833 3 .1 3 8 9 8 8 9 8 .1 3 4 8 8 8 .8 4 4 6 8 9 .7 1 3 VAR833 3 .8 9 3 1 3 0 7 3 .3 8 4 7 6 8 .3 3 8 1 3 9 .8 8 6 VAB834 -8.8301188 -8 .1 8 8 8 7 8 .3 2 9 9 7 7 .3 0 9 3.34394383*81 3 .8 4 3 7 3 8 .8 2 8 7 9 8 .0 8 7 VAR8I3 3.14148833-81 8 .8 1 3 8 6 3 .8 7 3 3 3 3 .8 3 7 < 8 .

STATISTICS WHICH CARROT BE ODIR UWLR 8RE P R !R ID 6 8 A LL RIHEB.

ro SPSS BATCH SVBTEH

PILE 80PRHAHK ( CHEAT I Oft B A IT ■ M /S A /S S ) VARIABLE LIST I RECRESSIOR LIST 1 PePEPPM T V A R IA B L E .. V A H M 1 SONIART TABLE

VABIABLE ■DLTIPIX R R SHARE » CHARGE 811TLE R B BETA VAMM •.41*94 I.17438 •.17438 •.41*94 2.8*8842 •.39988 VABMD ».NHi *.28844 • . • 8 2 2 8 -• .2 8 8 2 8 -*.227M340-«1 - • .2 4 1 4 1 VARSSa •.*•189 *.941*1 •.1*327 •.32741 •.3 4 4 4 2 4 4 • .2 8 7 1 4 •.42733 *.3*384 •.•3143 —•.2442* -248.1148 -•.18898 VABB3S • .4 4 1 * 3 * . 4 ia M •.•1884 •. 12881 • .1 3 8 * 8 4 * • .1 3 4 4 * •.44747 *.41*47 • .M 7 4 * • .29878 •.4*21387 • . 2 * 4 7 4 VARB24 • .4SSA4 «.433M •.•1433 -•.•4212 -•.428114* -*. 18*87 VAM 3S •.439S3 *.43338 • . • • 1 8 8 •.17133 • .242*43*B-«1 • ••4372 V A M I3 •.48*88 *.43848 «.MM7 •.12*88 • . I4I4W2D-91 • .•1284 (CORSTAim • .8 8 2 2 3 8 * APPENDIX 0

THE STEPWISE ANALYSIS OF THE SIGNIFICANT PREDICTOR PROFILE WITH CURRENT DEBT TO CASH FLOW SUBSTITUTED FOR TOTAL DEBT TO CASH FLOW

275 VARIABLE NUMBER VARIABLE NAME

001 Return On Total Assets 002 Gross Margin To Net Sales 003 A ll Expenses to Net Sales 004 Operating Expenses To Net Sales 005 Grpss Margin Per Employee 006 Gross Margin Return On Inventory 007 Earn & Turn Index 008 Total Debt To Net Worth 009 Current Debt To Net Worth 010 Current Debt To Ending Inventory Oil Average Collection Period 012 Current Assets To Current Debt 013 Days Payables Outstanding 014 Cash + Market Securities + Accounts Receivables To Current Debt 015 Gross P ro fits To Accounts Payable 016 Net Sales To Cash + Marketable Securities 017 Net Sales To Fixed Assets 018 Net Sales To Ending Inventory 019 Net Sales To Net Worth 020 Net Sales To Accounts Receivables 021 Net Sales To Working Capital 022 Net Sales To Cash Flow 023 Net Worth To Cash Flow 024 Total L ia b ilitie s To Cash Flow 025 Current Liabilities To Cash Flow 026 Net Sales Index 027 Net Sales Per Employee 028 Net Sales Per Sq. Ft. Of Selling Space 029 Number of Sales Outlets 030 Long Term Debt Net Worth 031 Times Interest Earned 032 Net Sales To Current Assets VARIABLE NUMBER VARIABLE NAME

033 Average Market Shares For Top 1 to 5 Markets 034 Market Share Variance For Top 1 to 5 Markets 035 Advertising Expenditures From 10-K Forms 036 Advertising Expenditures From Advertising Age 037 Net Sales 038 Advertising Expenditures From 10-K Forms To Net Sales 8P88 (LATCH STBTEH

n i x o v p r h a r k ( c r e a t io n d a te ■ u / m / o o i »*«***#*»**«»**»*****•»MULTIPLE ItC lE II I 01 »•***»** VARIABLE LIST REGRESSION LIST DEPENDENT VAR IABLE.. VAR441 VARIABLE(0> ENTERED OH STEP NUMWR I . . VAR336

MULTIPLE R 0 . 4 I M 4 AH ALT SIS OP VARIANCE BP SUM OP SQUARES ■EAR SQUARE R SQUARE S . 17*35 RECRESSION I . 4616.31332 4616.31332 6 7 .4 1 7 4 3 ADJUSTED R SQUARE 0 .1 7 3 7 2 RESIDUAL SIS. 31064.I672S 6 6 .6 0 2 3 2 STANDARD ERROR 0 .2 4 4 8 4

VARIABLES IR THE EQUATION VARIABLES HOT IN T IE EQUATION — VARIABLE B BETA STD 'ERROR B P VARIABLE BETA IR PARTIAL TOLERANCE VAR026 3■ 344666 4 .4 1 4 4 4 S .S 444S 4 7 .4 1 ? VAR402 4 .3 3 3 4 3 4 . 4 .4 3 2 2 6 2 4 .6 4 6 ICONSTANT) 3 .2 8 8 3 7 4 VAR344 - 4 .2 8 4 1 2 -4 .2 8 8 1 4 4 .4 4 7 7 3 2 7 .6 2 3 VAR413 4 .4 1 1 4 7 4 .4 1 2 7 3 4 .4 3 2 1 8 4 .4 3 1 VAR424 4 .1 1 2 4 4 4 .1 2 3 1 3 4 .4 7 4 4 7 4 .0 4 3 V A M 23 4 .2 3 3 4 8 4 .2 7 4 7 0 4 .4 8 7 7 4 2 6 .4 4 6 VAR424 —4.4 86B 6 -4 .4 4 8 8 8 4 .4 4 6 6 1 2 .0 7 3 VAR433 4 .1 4 2 3 4 4 .1 3 6 4 7 4 .4 4 0 4 3 7 .0 8 2 VAR438 -4 .1 4 3 8 7 -4 .2 1 3 6 3 4 .4 8 4 7 4 14 .4 2 4

-vlno 00 SPSS BATCH STBTEM riLc n rn u t c c h e a t i oh b a t e « ti/*4/ao> ••****«»**•»***«*»**»•* HDLTIPLE R E C R E 8 8 IO VARIABLE LIST 1 BECRE88I0B LIST 1 depehdert t a m a b l e . . vahooi VARIABLEIS) EMUES OB STEP RUBBER S..

MULTIPLE R 0.49830 ARALTBIS Or VARIARCE DT SUM OF MUAREB u a h square R SQUARE 0.34334 RECRESSI0B 2 . 4340.87449 3184.93738 SO .17011 ADJUSTED R 0.23849 RESIDUAL S I2 . 19804.40791 43.48272 STABOARD ERROR 7.04740

------VARIABLES IB THE EQUATIOR------VARIABLES ROT IB T IE EQUATIOR ------VARIABLE B BETA STD ERROR B r VARIABLE BETA IB PARTIAL TOLERARCE r VARS24 8.348447 0.43230 0.88184 7 4.884 VAR002 0.31787 0.84199 0.87780 41.192 VAB009 -#.88724810-01 -0 .2 8 9 1 2 0.00787 27.423 VAR0I3 0.17170 0.14919 0.73474 9.148 (C0B8TART) 4.019037 VAR020 0.10894 0.12442 0.97834 4.874 VAR023 0.23384 0.24873 0.97949 23.429 VAR024 0.00168 4.00200 0.88049 0.041 VAR033 0.13409 0.18432 0.99832 7 .790 VAM38 -4.18 7 4 1 -0 .2 1 3 9 3 0.98387 14.914

VAR I ABLE!S) m u r e d or stop HUMES 8 . VAROOB

HDLTIPLE R 0.87404 MALTSIS OP VARIARCE or sum or SQUARES U A H SQUARE r R SQUARE O .S 3I84 REGRESS 1OB 3 . 8484.43239 2898.47744 81.48404 ADJUSTED R SQUARE 0.82840 RESIDUAL 811. 17490.08021 84.23810 STARDARD ERROR 7.49021

— — — VARIABLES VARIABLE B ■ETA S IS ERROR B r VARIABLE BETA IB PARTIAL TOLERARCE r VAR024 8.788248 0.88819 0.87184 84.118 VAR*13 -0 .0 4 4 1 9 -0 .0 3 9 3 4 0.48832 0.481 VARS09 -0 .4984S49B-OI -0 .8 8 3 8 1 0.00718 4 8.422 VAR024 0.07048 0.08444 0.94397 2 .2 8 8 VAR002 0.8838424 S .81787 0.00978 4 1.192 VAR023 0.13048 0.14334 0.80728 4 .8 0 8 (COHSTAim 0.4741814 VAR424 -0 .1 1 1 7 8 -0.12 1 4 4 0.78930 4 .4 4 2 VAR933 0.13889 0.14873 0.99832 8 .788 VAM838 -0 .1 8 2 1 4 -0.22101 0.98384 18.920

•'jr o <0 8roe BATCH SISTER FILE SUPRHARE ICREATION DATE ■ I l/M/M) CUE VARIABLE LIST 1 REGRESS IOR LIST 1 DEFENDCST VARIABLE.. VARS#I VARIABLEIS! ERTERED OR STEP NUJBER 4 . . VAM38

MULTIPLE R # .*# 3 7 2 ARALTSIS o r VARIARCE W SUN Or SQUARES K A N SQUARE R SQUARE #.34446 REGRESSION 4. 464#.73864 3388.18391 ADJUSTED R SQUARE #.36426 RESIDUAL 81#. I443S.74494 83.4437# STARDARD ERROR 7.32830

VARIABLES IR THE EQUATION ------VARIABLES ROT IR THE EQUATION — VARIABLE B BETA STD ERROR B VARIABLE BETA IR PARTIAL TOLERARCE VAR834 S.B441B4 #.88134 #.34842 49.239 VARBI3 -#.#4983 #.47788 1.891 VAR6#9 -# .3 2 7 1 8 # .# •4 9 9 48.98# VAR829 #. # . 1# 8I6 •.9 8 8 7 8 3 .4 88 VAR##2 #.S79#837 # .3 1 4 *3 #.#6834 42.194 VAR823 #.13343 #.18937 #.erm 7. 148 VAR838 -386.2494 -# .1 8 2 1 4 T I . 74849 18.43# VARB24 -# .# 8 # S I -#.#8851 #.71.798 2 .4 4# lOORSTAim 2.19 VAR833 # .1 3 *9 9 #.14412 #.94764 8 .683

VARIABLEIS) MTIIRED OR STEP HUBER S .. VAR833

MULTIPLE R #.41778 ARALTS 18 OP VARIARCE BT 8UJI OF 8Q0AREB MEAN SQUARE r R SQUARE #.38189 REGRESS1OR 8 . 9988.79937 1497.78987 3 8 .1 3 4 4 1 ADJUSTED R #.37169 RESIDUAL 14187.48333 82.38732 7.23741

VARIABLES IR T B EQUATIOR VARIABLES ROT IR THE EQUATION — VARIABLE B BETA S T VARIABLE 1H PARTIAL TOLERARCE 2.399374 8. 3.84188 31.778 VARB13 • # .# 8 3 1 4 -# .# 7 2 2 # 3.47763 1 .4 1 4 VI —#.48413473-91 -# .8 2 3 9 3 # . # • 4 4 1 49.168 VAR62# # .#7968 #.#9871 # .9 81 4 8 3 .# 3 1 VAR##2 #.2788789 #.81884 # .• 0 7 6 4 43.177 VAR623 # .1 3 1 3 1 # . 14999 #.8#493 7 .3 8 9 VAR638 -# .1 7 8 7 4 7 3 .4 1 1 1 7 18.49# VAR824 H -#.#9838 #.74712 2 .8 2 4 VAM833 #.1382884 #. # .3 4 4 2 8 8 .8 83 IOORSTART) #.7*43387 PILE BUFRHARE (OtCATIOK M i l • 11/04/86) VARIABLE LIST RECRESSIOH LIST deterdekt v a r ia b l e . . v u m i VARIABIX(S) ERTEHED OK * 1 0 RUBER 6 .. VAR623

MULTIPLE R Q. AR ALTS IS OP VARIARCE IIP SUM OP SQUARES HEAR SQUARE r R SQUARE 0.34881 REGRESSIOR 6 . I*392.4783I 1738.44687 S3.88634 ADJUSTED R SQUARE •■88373 RESIDUAL 806. 18838.88434 81.37881 STABOARD ERROR T . 1*764

VARIABLES IR TRE EQUATIOR ------VARIABLES ROT IR T O BBOATIOR — VARIABLE B BETA STD ERROR B VARIABLE BETA IR PARTIAL TOLERARCE VAR836 3.848741 8.83883 8.38778 82.823 VAR8I3 -8.8868! -8 .8 4 4 1 3 8 .4 6 8 8 3 8 .7 4 3 VAR884 -8.44S8882D-8I -8.34631 8.88644 48.788 VAR828 8.86121 8.87873 8 .4 2 8 3 2 1.771 VAR882 8.3806216 8.28866 8.86248 24.872 VAR624 -8.3 48 8 8 -8 .2 2 7 4 8 8.88686 16.783 VAR838 -283.6424 -8.18883 78.23863 16.317 VAR88S 8.1334871 8.12434 8.84881 8 .484 8.4438342 8.18131 8.16678 7 .889 (OOMSTAim 8.8678348

VARIABLEIS) D R W 08 STEP RUBER 7.. VAR824

MULTIPLE B 8.68824 AR ALTO IB OP VARIARCE DP SUM OP SQUARES WAR SQUARE P R SQUARE 8.42674 RECRESSIOR 7 . I I 171.78736 1848.46462 82.68346 ADJUSTED R 8.41872 RESIDUAL 1 *7 . 18884.64824 48.87823 STARBARD ERROR 6.44188

VARIABLES 1H I K EQUATIOR VARIABLES ROT IR TDK EQUATIOR — VARIABLE B K T A STB ERROR B VARIABLE BETA IR PARTIAL TOLERARCE VARD26 2.847762 8 .884 32 0.84411 8 3 .88 8 VARA13 -0.82377 -0.02 1 2 4 6.48767 6 .1 3 8 VAR844 -6.8076263D-6I 8.80761 16.886 VAR338 0.04844 0.06833 6.41878 1.041 VAR362 6.8086146 6.87388 0.06184 2 8 .SIB VAR83S -228 .42 7 8 -8 .1 4 8 6 8 64.74882 16.788 VARS33 8.1340871 6.13467 0.04470 4 .6 7 8 VAR823 6.4211487 6.27281 8. 2 1 .14 3 VAR824 6 . 16.783 ICORSTART) 6.8160232 SPSS HATCH s y s t e m PILE 8UPRHAKK (CREATIOS BATE ■ 11/04/80) • *«*••«*«•*••*•••»••*** HD VARIABLE LIST 1 RECRE88I0S LIST 1 DEPEHDEST VARIABLE.. VAHMI VARIABLE! 8) EHTERED OH STEP HDHBER S .. VAR030

HDLTIPLE R 9 .4 *4 7 8 AHALTBIH OP VARIARCE DP SUH OP SOD ARES K A R SOUARE P R M U ARE 0.43873 REGRESSIOR 8. 11222.48289 1402.83187 38.70412 ARIOSTO) R SSDARE 0 .4 1 3 8 * RESIDUAL •4. 14983.83001 48.84873 STAIDARD ERROR 4.00043

VARIABLES IS THE HDATIOR VARIABLES SOT IS THE BOOATIOH VAR! B BETA F VARIABLE BETA IS PARTIAL TOLERASCE P S .83884 0.88174 •0 .4 7 3 VAR0I3 -0.00738 -0.00438 0.43498 0.013 -0.30471 00741 14.404 0.37130 0.04147 3 8 . ISA T O .10777 11.343 0.1880343 0.18144 0 .307 0.8830884 0.34138 0.30383 18.824 AR034 -0.8404702 -0 .3 4 3 3 4 0.21087 18.939 'ARS30 0.38888438*81 0.84000 1.041 (OORSTAST) 0.1848013

r o co r o SPSS BATCH STOTT!! TILE SUPRMUIK ICBEATIOS BAIT ■ 11/44/84) HULTIPLE IE C 1E I I I O I ••••••• VARIABLE LIST 1 BEGBES81QR LIST t DEPERDERT VARIABLE.. VAR44I VARIABLE!8) EHTERED OB STEP HUIBER 4 . . VAM13

MULTIPLE R S .M 47V AR ALTO IS O r VARIARCE BP stm o r SQUARES K A R SQUARE R SOP ARE A. 48878 RECRE88I0R 9 . 11223.84IR1 1247.42949 88.48888 ADJUSTED R SQUARE 4.4119V RESIDUAL 948. 14983.88479 49.42698 STARBARB ERROR 7 .4 4 1 *8

VARIABLES IR THE ESDATIOR ------VARIABLES ROT IR THE EQDATIOR ------VARIABLE B BETA STD ERROR B VARIABLE BETA IR PARTIAL TOLERARCE P VAR484 4.88444 4.38784 49.232 VARB49 -4.B441433B-41 -4.84481 4.44843 14.818 VAR842 4.BB84443 4.87888 4.47764 18.388 VAR438 -84 7 .4 4 7 4 -4 .1 8 1 8 4 74.88869 11.248 VAR433 4 .14*47 2 7 4.13177 4.44489 9 .1 89 VARB88 4.8793414 4.84418 4.84484 18.127 -4.8881884 -4 .8 4 8 8 4 4.81214 18.649 4.84844B8B-4I 4.44444 4.42898 4 .9 13 VAR413 -4.8117488B-43 4.47882 4 .4 1 2 (OOMSTAHT) 4.1874411

MAXIMUM STATISTICS VHICH CARROT BE OWTUTED ARE PRIRTTD AS ALL HIRES. 283 FILE 8UFRIURK (CREATIOR M U ■ ll/*4/B ») •«•••*•*•*•••••••*••••« K lTirit VARIABLE LIST I REGRESSION LIST I K n m a r r v a r ia b l e . . v u n i i m U T TABLE

VARIABLE W LTIPLE B R 8Q0ARE BBS CRABCE S IIT L E R B BETA V A M 34 •.4 1 4 4 4 •.1 7 4 3 3 •.1 7 4 3 8 •.4 1 4 4 4 8.SIMMS •.33444 V A M M •.4 4 3 3 * •.B 4 3 3 4 • .••4 4 4 -•.3 3 8 3 1 -•.3MIS33D-81 -4.2*481 IAMM • .8 7 4 M •.3 3 1 8 4 •.M B 3 4 •.3 274 1 •.3 3 2 4 3 *3 •.2 7 88 3 •.••372 •.84448 •■•3344 -8.3443* -2 3 7 .4 *7 4 -4 .1 3 1 3 8 VABS3S •.•1773 •.38134 • >•1712 •.1 388 1 •.184*737 4.13177 VAIW23 •.•a s s * •.8 4 38 1 •.•1341 •.34873 •.8743*14 •.34*18 VAM B4 • . u a s •.4 3 4 7 4 • .• 3 1 3 8 - • . • • 2 1 3 -•.8381384 —«.3428» VARSBS •.•3 4 7 8 •.4 3 8 7 3 • .• • 1 4 4 •.1 7 1 3 3 • .248M3S8-81 • .• 4 4 * 4 V A M I9 •■ •34 74 •.4 3 8 7 8 • . H M 3 • . 13*38 -•.8117622D-U —•.8 8 7 3 8 (GONSTART) •.1 8 7 *3 1 1 APPENDIX P

THE MEAN VALUES FOR THE TWENTY-ONE RESEARCH VARIABLES

285 VARIABLE NUMBER VARIABLE NAME

001 Return On Total Assets 002 Gross Margin To Net Sales 003 A ll Expenses to Net Sales 004 Operating Expenses To Net Sales 005 Grpss Margin Per Employee 006 Gross Margin Return On Inventory 007 Earn & Turn Index 008 Total Debt To Net Worth 009 Current Debt To Net Worth 010 Current Debt To Ending Inventory Oil Average Collection Period 012 Current Assets To Current Debt 013 Days Payables Outstanding 014 Cash + Market Securities + Accounts Receivables To Current Debt 015 Gross P rofits To Accounts Payable 016 Net Sales To Cash + Marketable Securities 017 Net Sales To Fixed Assets 018 Net Sales To Ending Inventory 019 Net Sales To Net Worth 020 Net Sales To Accounts Receivables 021 Net Sales To Working Capital 022 Net Sales To Cash Flow 023 Net Worth To Cash Flow 024 Total Liabilities To Cash Flow 025 Current Liabilities To Cash Flow 026 Net Sales Index 027 Net Sales Per Employee 028 Net Sales Per Sq. Ft. Of Selling Space 029 Number of Sales Outlets 030 Long Term Debt Net Worth 031 Times Interest Earned 032 Net Sales To Current Assets VARIABLE NUMBER VARIABLE NAME 287

033 Average Market Shares For Top 1 to 5 Markets 034 Market Share Variance For Top 1 to 5 Markets 035 Advertising Expenditures From 10-K Forms 036 Advertising Expenditures From Advertising Age 037 Net Sales 038 Advertising Expenditures From 10-K Forms To Net Sales 288

gf-B B BATCH 8VBTEH flLE BU7RHARK I CHEATIO* OATH • 1I/B4/6*)

CASES HEAR BrAJDARD DCV VARIABLE B -2 2 B B B .1 3 3 3 VARBBI IB . B 2 3 2 7 . BBS* VAR992 IA .6 B S 2 T. 83 78 VARB83 I*.4884 B.BBBS VARBB4 1 2 . 2 IB B It .3881 VARBBS lSa.BBBB 2 6 4 . 3 2 * 7 1 * 6 .3 * 9 8 VAR*** 2 * 2 . 4 8 * 7 V*R**7 IS B .B 2 B B IBS.11ST VARBBS t b . b b b i BI.BBU VAHBB9 8 4 . 2 7 9 * SB.8 3 3 B v a r b i b • 1 .B 3 B B * 3. 31 IB V A R B I1 B .7 3 3 8 VARB 12 1 .B IB B 1 7 . B IB B B.3B33 VARB 13 B .B I B 2 3 .3 1 3 8 VARBIB 1 1 .9 2 3 4 T .B 7 B B VARBIB 7 7 . 7 * 4 3 9 6 .7 3 8 2 VARBIB 7.2202 V ARB 17 1 2 . IB B 4 1 3 . I3 B 7 4 .6 2 3 1 VARBIB 1 1 . 1BBB 8.3133 VARBIB 1 I.B B 7 B 1 4 .4 3 1 3 VARB29 B .B B I 8 *86.632* VARB2I 3 B .2 B B 2 2 9 .3 3 1 3 VARB22 2 .B B B B 2 .7 * 1 2 VARB23 3 .B B B 3 2 .3 3 1 6 VAHB24 1 .3 * 3 3 VARB2B 1 .T B 3 B 1 . 8 2B B 1 .1 7 9 8 VARB2B 6 2 . * 7 * 1 VARB27 7 8 . BBBB 1 * 7 .7 8 * 9 B S .B B B B VARB2B 3 6 8 . 8 0 8 7 1 1 8 2 .8 *3 8 VARB2B * 1 . 3 7 3 3 * 4 . 8 9 * 4 VARB3B B .B B 7 B 8 8 .3 * 8 7 V A R B 3 I B . 8 I 4 8 6 .6 4 6 * VARB32 B .B B B B 6 .6 3 2 * VARB33 1 7 .8 2 3 3 2 6 .3 * 8 9 VARB3B 1*88*763.3661 VARB3B BTBBBB3.BB67 4476873.*27* 21794383.8*1* VARB3B 16374881**.118* VARB37 12497*733*.**82 VARB38 APPENDIX Q

A GLOSSARY OF TERMS USED IN THE RESEARCH

289 GLOSSARY

ADVERTISING EXPENDITURES FROM ADVERTISING AGE. An independent v a ri­ able; simply represents the total dollars spent on a ll promotions as gathered from Advertising Age.

ADVERTISING EXPENDITURES FOR 10-K FORMS. An independent variable; simply represents the total dollars spent on a ll forms of promotion as gathered from the SEC 10-K forms.

ADVERTISING EXPENDITURES FROM 10-K FORMS TO NET SALES. An independent variable; calculated by dividing the total dollars spent on all promotions as reported by a firm on its SEC 10-K Report by total net sales.

ALL EXPENSES TO NET SALES. An independent variable; calculated as operating expenses plus fixed expenses divided by net sales.

ASSET MANAGEMENT VARIABLES. Are measures which denote how the funds of a particular company are invested.

AVERAGE COLLECTION PERIOD. An independent variable; the measure is the mean number of days for which a firm 's accounts receivables have been outstanding.

AVERAGE MARKET SHARE FOR TOP 1 TO 5 MARKETS. An independent variable; calculated as the average market share fo r the fiv e largest population areas served by a firm . For some firms less than five markets have information available and therefore the average is calculated using the available information.

AUTOCORRELATION. The condition in a linear analysis where successive observations are highly related to each other.

CASH FLOW MANAGEMENT VARIABLES. Are measures which reveal the e f f i­ ciency with which a firm meets the need fo r cash.

CLASSIFICATIONS. Refers to Financial Structure and Marketing Performance.

CURRENT ASSETS TO CURRENT DEBT. An independent variable; calculated by dividing short-term assets by short-term liabilities.

CURRENT DEBT TO ENDING INVENTORY. An independent variable; calculated by dividing short-term lia b ilit ie s by the average ending inventory.

CURRENT DEBT TO NET WORTH. An independent variable; calculated by dividing short-term debt by net worth.

CURRENT LIABILITIES TO CASH FLOW. An independent variable; calculated by dividing a company's short-term lia b ilitie s by its average cash flow. 291 CASH + MARKETABLE SECURITIES + ACCOUNTS RECEIVABLES TO CURRENT DEBT. An independent variable; calculated by dividing a ll cash or near-cash assets by short-term liabilities.

DAYS PAYABLES OUTSTANDING. An independent variable; calculated as the mean number of days fo r which a company's accounts payables have been outstanding.

EARN % TURN INDEX. An independent variable; calculated as Gross Margin x Cost of Goods Sold Net Sales Average Ending Inventory

FINANCIAL MANAGEMENT VARIABLES. Measures which re fle c t a firm 's debt structure.

FINANCIAL STRUCTURE VARIABLES. Are measures which indicate how the funds or assets of a company are employed.

GROSS MARGIN PER EMPLOYEE. An independent variable; calculated by dividing the difference between net sales and the cost of goods sold by the total number of employees.

GROSS MARGIN RETURN ON INVENTORY. An independentvariable; calculated as Gross Margin x ______Net Sales ______Net Sales Average Ending Inventory

GROSS MARGIN TO NET SALES. An independent variable;calculated as Net Sales minus cost of goods sold, divided by Net Sales.

GROSS PROFITS TO ACCOUNTS PAYABLES. An independent variable; calculated by dividing the profits realized before any expenses are met by a firm 's accounts payables.

LINE OF TRADE. Refers to the type of re ta il operations undertaken by firm s; i.e . convenience stores, supermarkets, and diversified operations.

LIQUIDITY MANAGEMENT VARIABLES. Are measures which show how adequately a firm 's indebtedness is covered by its assets.

LOCALLY OPERATING COMPANIES. Firms having operations in two or fewer states.

LONG TERM DEBT TO NET WORTH. An independent variable; calculated by dividing the total long term lia b ilitie s of a firm by its net worth.

MARKET SHARE VARIANCE FOR TOP 1 TO 5 MARKETS. An independent variable; calculated as the variance for the average market share of a firm 's top five markets (some firms have information available for less than five markets). 292

MARKETING MANAGEMENT VARIABLES. Are measures which reveal how the marketing group employs the assets at the department's disposal.

MARKETING PERFORMANCE VARIABLES. Are measures which indicate how the effo rts of the marketing group of a firm are directed.

MARGIN MANAGEMENT VARIABLES. Are measures which indicate the price mark-up over cost for a firm 's product mix.

NATIONALLY OPERATING COMPANIES. Firms having re ta il operations in eleven or more states.

NET SALES. The net dollar sales for a firm for a given year.

NET SALES INDEX. An independent variable; calculated in the present research by using a company's 1969 sales cash base and then deciding each succeeding year's sales by the 1969 figure.

NET SALES PER EMPLOYEE. An independent variable; calculated by dividing a company's net sales by its average number of employees.

NET SALES PER SQUARE FOOT OF SELLING SPACE. An independent variable; calculated by dividing a firm's net sales by its average square footage of selling space.

NET SALES TO ACCOUNTS RECEIVABLES. An independent variable; calculated by dividing a company's net sales by its average accounts receivables.

NET SALES TO CASH + MARKETABLE SECURITIES. An independent variable; calculated by dividing net sales by cash and near cash assets.

NET SALES TO CASH FLOW. An independent variable; calculated by dividing a firm 's net sales by its average cash flow.

NET SALES TO CURRENT ASSETS. An independent variable; calculated by dividing a firm's net sales by its short-term assets.

NET SALES TO ENDING INVENTORY. An independent variable; calculated by dividing net sales by average ending inventory.

NET SALES TO FIXED ASSETS. An independent variable; calculated by dividing net sales by a company's long-term assets.

NET SALES TO NET WORTH. An independent variable; calculated by dividing net sales by a company's net worth.

NET SALES TO WORKING CAPITAL. An independent variable; calculated by dividing a company's net sales by its average working capital.

NUMBER OF SALES OUTLETS. An independent variable; is equal to the num­ ber of sales outlets possessed by a firm at the end of its fiscal year. 293

OPERATING EXPENSES TO NET SALES. An independent variable; calculated by dividing all expenses which vary with output volume by net sales.

PROFIT PERFORMANCE. Measured in the study as the pretax rate of return on total assets; used interchangeably with profitability.

PROFITABILITY. Measured in the study as the pretax rate of return on total assets; is used interchangeably with p ro fit performance.

REGIONALLY OPERATING COMPANIES. Firms having re ta il operations in from three to ten states.

RELIABILITY. Concerns the extent to which an experiment, te st, or any measuring procedure yields the same results in repeated tr ia ls .

RETURN ON TOTAL ASSETS. The dependent measure of the research; measured by the pretax profit divided by the total assets.

STATISTICAL SIGNIFICANCE. In the present study is defined as a prob­ ability level of 95 percent.

SUBCLASSIFICATIONS. Refers to Financial Management, Liq uidity Manage­ ment, Cash Flow Management, Asset Management, Margin Management, and Marketing Management.

MULTICOLL INEARITY. A situation where the independent variables in a linear analysis are highly correlated with each other.

SURROGATE MEASURES. The individual variables used to represent the effects of the six subclassifications.

TIMES INTEREST EARNED. An independent variable; calculated by dividing a company's net p ro fits by its average interest payments.

TOTAL DEBT TO NET WORTH. An independent variable; calculated as total debt divided by net worth.

TOTAL LIABILITIES TO CASH FLOW. An independent variable; calculated by dividing the total debt employed by a firm by its average cash flow.

VALIDITY. Concerns the a b ility of any measuring device to measure what i t purports to measure. BIBLIOGRAPHY

Altman, Edward I., "Financial Ratios, Discriminant Analysis and the Pre­ diction of Corporate Bankruptcy." The Journal of Finance, September 1968, pp. 589-609.

______, "Examining Moyer's Reexamination of Forecasting Financial Failure." Financial Management, Winter 1978, pp. 76-79.

______, Robert G. Holderman and P. Narayanan, "Zeta Analysis: A New Model to Identify Bankruptcy Risk of Corporations." Journal of Banking and Finance, Volume 29, Issue 54, 1977, pp. 29-54.

______, and Bettina Loris, "A Financial Early Warning System For Over-The-Counter-Broker-Dealers." The Journal of Finance, September 1976, pp. 1201-1217.

Bain, Joe S. Industrial Organization. Second Edition; New York City: John Wiley & Sons, Inc., 1968.

Bates, Albert D., Ronald L. Ernst and Cyrus C. Wilson. "Profitability and Productivity Trends In R etailing," A Special Report of Management Horizons, Incorporated, 1975.

Bazley, John D., "An Examination of The A b ility of Alternative Accounting Measurement Models To Product Failure." Review of Business and Economic Research, Fall 1976, pp. 32-46. /

Beaver, William H., "Financial Ratios As Predictors of Failure." Empirical Research in Accounting: Selected Studies 1966, Journal of Accounting Research, Supplement V 1967, pp. 71-111.

Beekman, E. "Problems of Calculating The Return On Investment." Journal of R eta ilin g, Summer 1968, pp. 3-16.

Beik, Leland L. and Stephen L. Buzby. "P ro fita b ility Analysis by Market Segment," Journal of Marketing, July 1973, pp. 48-53.

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