THE RESTAURANT INDUSTRY: BUSINESS CYCLES, STRATEGIC FINANCIAL

To view this page ensure that Adobe Flash Player version 9.0.124 or greater is installed.

Please login or register to make a comment!

...Description...... more. less.

and over. To answer for this demand, continued from the previous studies (Choi, 1996; Choi et al., 1997a; Choi et al., 1997b; Choi et al., 1999), this study developed the restaurant industry business cycle models and examined financial practices of the high and low performing firms over the industry cycles. The U.S. restaurant industry demonstrated three cycles (peak to peak or trough to trough) for the period of 1970 through 1998. The restaurant industry peaked in 1973, 1979, and 1989. The industry troughed in 1970, 1974, 1980, and 1991. The mean duration of the restaurant industry cycles is 8 years (SD: 2) calculated by peak to peak and 6.5 years (SD: 2.08) calculated by trough to trough. Expansion takes an average of 6 years in the restaurant industry but declines sharply after it reaches the peak taking average 1.33 years. ii The restaurant industry experienced high growth (boom) every five years on average. The troughs of the growth cycles, contrasted to the peaks of the growth cycles, coincided with those of the restaurant industry business cycles in each case except one (1985). During that year a low growth phase interrupted industry business expansion but did not terminate it. Restaurant industry growth cycles, then, tend to be relatively symmetrical: since 1970 the average duration was about 2.25 years for both expansion (L- H) and contraction (H-L). In contrast, the restaurant industry business cycles in the same period show a strong asymmetry: the expansions lasted on the average 6 years; the contractions, 1.33 years. The expansions have varied in duration much more than the high growth phases have (the respective standard deviations are 2.58 and 0.95 years). This study supports the view that the cyclical fluctuations of the growth of the restaurant industry can be projected by measuring and analyzing series of economic indicators and each economic indicator has specific characteristics in terms of time lags, and thus can be classified into leading, coincident, and lagging indicators. This study formed a set of composite indices with twelve indicators classified in the leading category, six as coincident, and twenty as lagging. The high performing firms' financial practices regarding investment decisions measured by capital spending, and price earning ratio, and part of financing and dividend decisions measured by market value of common share outstanding are independent of the cyclical fluctuations of the industry cycles. But, their practices regarding dividend decisions measured by the earning per share, investment decision measured by cash flow per share, and financing decisions measured by asset value per share and long term debt level are dependent on the events (Expansion/Contractions) in the Restaurant Industry Cycles. Conclusively, high performers exercise their capital investment (reflected by capital spending) and equity management (reflected by common share outstanding and P/E ratio) independently while being less influenced by the industry swings. They exercise, however, their working capital management (reflected by cash flow per share), earning iii management (reflected by EPS), asset management, and long term debt management quite dependently while being more influenced by the industry swings. The financial practices exercised by the low performing firms are independent from the events in the industry cycle. Although some financial practices are related to the events in the industry cycle, the directions are opposite to the events in the industry cycle. Specifically, for all of the selected financial strategies except common share outstanding and long-term debt, the low performers practice them independently from the cyclical fluctuations of the industry cycles. Even for common share outstanding and long-term debt strategies, they practiced their strategies in opposite directions to the events (Expansion/Contractions) in the Restaurant Industry Cycles. It is expected that the above results can be used for improving investment performance through understanding the cyclical behavior of the economy and the restaurant industry. With that model, investors should be able to take part in the upswings while avoiding the cyclical downturns, and to structure a portfolio that keeps risk to a minimum. This should then presumably result in competitive investment decisions of firms, thereby improving the effectiveness of resource allocation. v DEDICATED To my wife (Kyeong-Ran Yang), daughter (Allis, SuhJung Choi), and son (Alvin JinSung Choi) for their constant love, understanding, support, and their willingness to share their lives with me. vi Acknowledgements I would like to express my deepest and sincere gratitude to my committee members who provided invaluable guidance, suggestions, and support for my study, and particularly made it possible for me to achieve this dissertation. First of all, I would like to acknowledge the influence of my many years of working with Dr. Michael, D. Olsen and Dr. Francis A Kwansa. They have trained me in their professional frameworks and they have never failed to do that. I am being honored by studying under their guidances and being challenged by their professional standards not only in academia but also in common life. I won't forget any moments of my life with them. I also owe a deep debt to my committee members, Dr. Tse, Thompson, and Uysal, who have given me the benefit of their comments and suggestions. I have been fortunate in receiving helpful comments and suggestions from them. Likewise, I sincerely thank to all other faculty members at Virginia Tech under whom I studied. I would also like to take this opportunity to thank all my colleagues and the staff members at Hospitality and Tourism Management Department who have helped me with various ways over years. This dissertation is dedicated to my parents, brothers, and sisters. Their constant love, support, and understanding have made me possible to do all my scholarly efforts. I hope this dissertation makes them proud. E specially, I would like to acknowledge my sister, Jeong-Ja Choi, who has been with me as my tender but strong advisor and Kyeong-Ran Yang who has been always with me as my most intimate friend, constructive advisor, and lovely wife. Thank you. Jeong-Gil Choi vii TABLE OF CONTENTS ACKNOWLEDGEMENTS............................................................................................................... ...VI TABLE OF CONTENTS.............................................................................................................. ..... VII CHAPTER 1: INTRODUCTION ....................................................................................................... ...1 P ROBLEM S TATEMENT ..........................................................................................................................2 PART I......................................................................................................................... ...................2 Need for Developing a Restaurant Industry Cycle Model and Its Economic Indicator System .........2 Forecasting and Hospitality Business........................................................................................... ...............2 Industry Business Cycle ....................................................................................................... ......................3 Economic Indicator System...................................................................................................... ...................5 Hospitality and Tourism Industry Forecasting................................................................................... ..........7 Restaurant Industry Forecasting................................................................................................ ................10 A Tool for Restaurant Industry Forecasting..................................................................................... ..........11 Part II........................................................................................................................ ................... 12 Need for Examining Financial Practices (or Strategies) of the High and Low Performing Firms for the period of Peaks and Troughs of the Restaurant Industry Cycle................................................ 12 P URPOSE AND O BJECTIVES .................................................................................................................. 17 Part I ........................................................................................................................ .................... 17 Part II........................................................................................................................ ................... 18 O VERVIEW OF THE R ESEARCH D ESIGN ................................................................................................. 18 Part I: Developing the Restaurant Industry Cycle Model and Its Economic Indicator System........ 18 Part II: Examining Financial Strategies of the High and Low Performing Firms in each Stage of the Restaurant Industry Cycle...................................................................................................... ....... 20 O UTLINE OF D ISSERTATION ................................................................................................................. 23 CHAPTER 2: REVIEW OF THE LITERATURE ............................................................................. 24 C HAPTER P REVIEW ............................................................................................................................. 25 PART ONE: H OSPITALITY AND T OURISM B USINESS F ORECASTING ....................................................... 26 Introduction................................................................................................................... ............... 26 Forecasting Studies............................................................................................................ ........... 27 Hotel Industry................................................................................................................. .........................27 Tourism Industry............................................................................................................... .......................31 Restaurant Industry............................................................................................................ ......................39 Forecasting Methods............................................................................................................ ......... 41 Qualitative Techniques......................................................................................................... ....................42 Quantitative Techniques........................................................................................................ ...................45 Assessing Forecasting Methods and Section Summary .................................................................. 52 Business Cycle Studies......................................................................................................... ......... 59 Business Cycle................................................................................................................. ........................59 Growth Cycles ................................................................................................................. ........................60 Kondratieff Cycle.............................................................................................................. .......................61 Business Cycle Forecasting with Economic Indicator System....................................................................64 Rationales for leading Indicators ............................................................................................. .................64 The Hotel Industry Cycle Model (Choi, 1996) ................................................................................... .......69 Economic Indicator System (EIS) for the Hotel Industry (Choi, 1996) .............................................75 Section Summary................................................................................................................ ........... 83 viii PART TWO: E XAMINING F INANCIAL P RACTICES OF H IGH AND L OW P ERFORMING F IRMS FOR THE P ERIOD OF P EAKS AND T ROUGHS OF THE I NDUSTRY C YCLE ............................................................................... 87 Introduction................................................................................................................... ............... 87 The Concept of Strategic Management.......................................................................................... 8 8 Strategy....................................................................................................................... .............................88 Co-Alignment Principle......................................................................................................... ...................89 Business Environment........................................................................................................... ...................90 Environmental Scanning......................................................................................................... ..................91 Strategic Management and Company Performance................................................................................... .96 The organizational leaders and strategy........................................................................................ ............99 The organizational leaders and the life cycle.................................................................................. .........100 CHOICE OF STRATEGY............................................................................................................ 100 CHOICE OF FINANCIAL STRATEGIES .................................................................................... 103 Introduction................................................................................................................... .........................103 Overall Procedure of Financial Practices and Value Creation in the Restaurant Industry.........................104 Value Creation................................................................................................................. ......................105 Managing Financial Resources (Capital Budgeting)............................................................................... .107 General Procedure of Financial Practices....................................................................................... .........109 Financial Practices Decision Rules............................................................................................. ............110 Risk and Uncertainty........................................................................................................... ...................112 Choice of Financial Strategies Based Upon Theories.................................................................. 113 Finance Theory in the Hospitality Literature................................................................................... ........113 Finance Theory in General Business Literature.................................................................................. .....114 Summary of Theoretical Results................................................................................................. ............119 Summary........................................................................................................................ ............. 120 CHAPTER 3: METHODOLOGY..................................................................................................... 124 I NTRODUCTION ............................................................................................................................... .. 125 Research Propositions and Objectives......................................................................................... 1 25 M ETHODOLOGIES ............................................................................................................................. 126 PART ONE: Developing the restaurant industry cycle model and its economic indicator system, 126 Formulating the Restaurant Industry Cycles (RIC)............................................................................... ...127 Formulating the Restaurant industry Growth Cycle (RGC) .....................................................................130 Selecting Potential Economic Indicators for the Restaurant industry........................................................130 Selecting Cyclical Indicators that will be included in the model..............................................................1 37 Determining Leading, coincident and lagging indicators......................................................................... 138 Formulating Composite Indices.................................................................................................. ............140 Evaluating the Performances of the EIS......................................................................................... .........141 Part II: Examining Financial Strategies of the High and Low Performing Firms in each Stage of the Restaurant Industry Cycle...................................................................................................... ..... 143 Context and Classification..................................................................................................... .................143 Examining financial practices.................................................................................................. ...............145 Variables ..................................................................................................................... ..........................150 Testing........................................................................................................................ ...........................151 CHAPTER 4: RESEARCH RESULTS ............................................................................................. 154 I NTRODUCTION ............................................................................................................................... .. 155 R ESULTS ............................................................................................................................... ............ 155 P ART I.............................................................................................................................. ................ 155 I. The U.S. restaurant industry cycle......................................................................................... . 156 II. The U.S. Restaurant Industry Growth Cycle............................................................................ 162 III. Economic Indicator System for the Restaurant Industry......................................................... 163 III-1. Leading, Coincident, and Lagging Indicators............................................................................ .....163 ix IV. The Composite Indices...................................................................................................... ..... 165 P ART II............................................................................................................................. ................ 174 V-1. High Performers/Low Performers....................................................................................... 174 V-2. Practicing Financial Strategies over the Industry Growth Cycles and Their Patterns........... 192 Summary........................................................................................................................ ............. 212 CHAPTER 5: DISCUSSION AND CONCLUSIONS........................................................................ 213 I NTRODUCTION ............................................................................................................................... .. 214 C ONTRIBUTION OF T HIS S TUDY ......................................................................................................... 220 Part I ........................................................................................................................ .................. 220 Part II........................................................................................................................ ................. 221 D ISCUSSIONS AND C ONCLUDING R EMARKS ........................................................................................ 222 L IMITATION ............................................................................................................................... ....... 224 A GENDA FOR F UTURE S TUDY ............................................................................................................ 224 REFERENCES..................................................................................................................... .............. 226 VITA........................................................................................................................... ........................ 248 x TABLE OF FIGURES F IGURE 1. R ELATIONSHIPS BETWEEN D IFFERENT F ORECASTING M ODELS ................................................... 44 F IGURE 2: T HE K ONDRATIEFF W AVE ( B ASED ON ANNUAL AVERAGES WITH A RATIO SCALE OF 1967 = 100.)62 F IGURE 3. T HE H OTEL I NDUSTRY C YCLE : L ONG -T ERM C YCLICAL F LUCTUATION ...................................... 72 F IGURE 4. T HE H OTEL I NDUSTRY G ROWTH C YCLE (S YMMETRIC PERCENTAGE CHANGE , YEAR - TO - YEAR )... 74 F IGURE 5. P ERFORMANCE OF C OMPOSITE I NDICES ..................................................................................... 80 F IGURE 6. R ESTAURANT I NDUSTRY B USINESS C YCLES (1970-1998)......................................................... 157 F IGURE 7. R ESTAURANT I NDUSTRY R EVENUE T RENDS (1970-1998)......................................................... 159 F IGURE 8. R ESTAURANT I NDUSTRY G ROWTH C YCLES (1970-1998)........................................................ 161 F IGURE 9. P ERFORMANCE OF L EADING C OMPOSITE I NDEX (% C HANGES )................................................. 167 F IGURE 10. P ERFORMANCE OF L EADING C OMPOSITE I NDEX ..................................................................... 168 F IGURE 11. P ERFORMANCE OF C OINCIDENT C OMPOSITE I NDEX (% C HANGES ) ........................................ 169 F IGURE 12. P ERFORMANCE OF C OINCIDENT C OMPOSITE I NDEX .............................................................. 170 F IGURE 13. P ERFORMANCE OF L AGGING C OMPOSITE I NDEX (% C HANGES )............................................. 171 F IGURE 14. P ERFORMANCE OF L AGGING C OMPOSITE I NDEX ................................................................... 172 F IGURE 15. F INANCIAL P ERFORMANCE (C ASH F LOW P ER S HARE ) OF B OB E VANS F ARMS (NDQ-BOBE) OVER THE R ESTAURANT I NDUSTRY G ROWTH C YCLE ............................................................................... 175 F IGURE 16. F INANCIAL P ERFORMANCE (C ASH F LOW P ER S HARE ) OF C RACKER B ARREL (NDQ-CBRL) OVER THE R ESTAURANT I NDUSTRY G ROWTH C YCLE ............................................................................... 176 F IGURE 17. F INANCIAL P ERFORMANCE (C ASH F LOW P ER S HARE ) OF L UBY ' S C AFETERIAS (NYSE-LUB) OVER THE R ESTAURANT I NDUSTRY G ROWTH C YCLE ............................................................................... 177 F IGURE 18. F INANCIAL P ERFORMANCE (C ASH F LOW P ER S HARE ) OF P ICCADILLY (NYSE-PIC) OVER THE R ESTAURANT I NDUSTRY G ROWTH C YCLE . .................................................................................... 178 F IGURE 19. F INANCIAL P ERFORMANCE (C ASH F LOW P ER S HARE ) OF R YAN ' S F AMILY S TEAK (NDQ-RYAN) OVER THE R ESTAURANT I NDUSTRY G ROWTH C YCLE ...................................................................... 179 F IGURE 20. F INANCIAL P ERFORMANCE (C ASH F LOW P ER S HARE ) OF S HONEY ' S I NC . (NYSE-SHN) OVER THE R ESTAURANT I NDUSTRY G ROWTH C YCLE . .................................................................................... 180 F IGURE 21. F INANCIAL P ERFORMANCE (C ASH F LOW P ER S HARE ) OF V ICORP R EST (NDQ-VRES) OVER THE R ESTAURANT I NDUSTRY G ROWTH C YCLE . .................................................................................... 181 F IGURE 22. C LASSIFICATION OF THE R ESTAURANT F IRMS : H IGH P ERFORMERS , N EUTRAL , AND L OW P ERFORMERS ............................................................................................................................... . 191 F IGURE 23. T HE E VENTS (E XPANSION (+)/C ONTRACTION (-)) IN THE R ESTAURANT I NDUSTRY G ROWTH C YCLES . ............................................................................................................................. ......... 193 F IGURE 24. F INANCIAL P RACTICES (C APITAL S PENDING , % C HANGES ) OF THE H IGH AND L OW P ERFORMERS OVER THE R ESTAURANT I NDUSTRY G ROWTH C YCLE ...................................................................... 195 F IGURE 25. F INANCIAL P RACTICES (C OMMON S HARE O UTSTANDING ) OF THE H IGH AND L OW P ERFORMERS OVER THE R ESTAURANT I NDUSTRY G ROWTH C YCLE ...................................................................... 197 F IGURE 26. F INANCIAL P RACTICES (E ARNING P ER S HARE ) OF THE H IGH AND L OW P ERFORMERS OVER THE R ESTAURANT I NDUSTRY G ROWTH C YCLE . .................................................................................... 200 F IGURE 27. F INANCIAL P RACTICES (C ASH F LOW PER S HARE ) OF THE H IGH AND L OW P ERFORMERS OVER THE R ESTAURANT I NDUSTRY G ROWTH C YCLE . .................................................................................... 203 F IGURE 28. F INANCIAL P RACTICES (B OOK V ALUE - A SSET ) OF THE H IGH AND L OW P ERFORMERS OVER THE R ESTAURANT I NDUSTRY G ROWTH C YCLE . .................................................................................... 205 F IGURE 29. F INANCIAL P RACTICES (P/E RATIO ) OF THE H IGH AND L OW P ERFORMERS OVER THE R ESTAURANT I NDUSTRY G ROWTH C YCLE . ......................................................................................................... 208 F IGURE 30. F INANCIAL P RACTICES (L ONG -T ERM D EBT ) OF THE H IGH AND L OW P ERFORMERS OVER THE R ESTAURANT I NDUSTRY G ROWTH C YCLE . .................................................................................... 210 xi TABLE OF TABLES T ABLE 1. S UMMARY OF F ORECASTING M ETHODS ..................................................................................... 53 T ABLE 2. T URNING P OINTS OF THE H OTEL I NDUSTRY C YCLE .................................................................... 72 T ABLE 3. T URNING P OINTS OF THE H OTEL I NDUSTRY G ROWTH C YCLE ..................................................... 74 T ABLE 4. T HE F INAL INDICATORS FOR F ORMING C OMPOSITE I NDICES AND C ROSS C ORRELATION OF THE C OMPONENT S ERIES OF THE H OTEL I NDICATORS , 1966-93............................................................... 77 T ABLE 5. T HE C OMPOSITE I NDICES OF THE L EADING , C OINCIDENT , AND L AGGING I NDICATORS FOR THE H OTEL I NDUSTRY AND THEIR S YMMETRIC P ERCENTAGE C HANGE ..................................................... 78 T ABLE 6. P ERFORMANCE E VALUATIONS OF THE H OTEL I NDUSTRY I NDICATORS : LEADS (-) AND LAGS (+) IN YEARS OF TURNS IN COMPOSITE INDICES AT GROWTH CYCLE TURNS (1966-1993)............................... 81 T ABLE 6-A. S UMMARY OF P ROPOSITIONS AND S UPPORTING L ITERATURE 122 T ABLE 7. A S ET OF V ARIABLES (F ROM C HOI , 1996).............................................................................. 132 T ABLE 8. E CONOMIC I NDICATORS FOR D EVELOPING THE R ESTAURANT I NDUSTRY C YCLE M ODEL AND E CONOMIC I NDICATOR S YSTEM . ................................................................................................... 135 T ABLE 8-A. S UMMARY OF F INANCIAL D ECISION -M AKING C ONSTRUCTS AND VARIABLES 151 T ABLE 9. C ONTINGENCY T ABLE F ORM FOR F INANCIAL P RACTICES (T HE I NVESTMENT D ECISION FOR EXAMPLE ) OF H IGH AND L OW P ERFORMERS OVER THE I NDUSTRY C YCLES ( EXAMPLE ) ..................... 152 T ABLE 10. T URNING P OINTS OF R ESTAURANT I NDUSTRY C YCLE ............................................................. 158 T ABLE 11. S YMMETRIC P ERCENTAGE C HANGE OF THE I NDUSTRY T OTAL S ALES ...................................... 160 T ABLE 12. T URNING P OINTS OF R ESTAURANT I NDUSTRY G ROWTH C YCLE ............................................... 162 T ABLE 13. T HE F INAL I NDICATORS FOR THE R ESTAURANT I NDUSTRY TO USE FOR F ORMING C OMPOSITE I NDICES ............................................................................................................................... ........ 164 T ABLE 14. T HE C OMPOSITE I NDICES OF THE L EADING , C OINCIDENT , AND L AGGING I NDICATORS FOR THE R ESTAURANT I NDUSTRY AND THEIR S YMMETRIC P ERCENTAGE C HANGE ......................................... 166 T ABLE 15. P ERFORMANCE E VALUATIONS OF THE R ESTAURANT I NDUSTRY I NDICATORS : L EADS (-) AND L AGS (+) IN Y EARS OF T URNS IN C OMPOSITE I NDICES AT G ROWTH C YCLE T URNS (1970-1998)................ 173 T ABLE 16. S CORING THE F INANCIAL P ERFORMANCE (C ASH F LOW P ER S HARE ) OF B OB E VANS F ARMS (NDQ- BOBE) OVER THE R ESTAURANT I NDUSTRY G ROWTH C YCLE .......................................................... 182 T ABLE 17. S CORING THE F INANCIAL P ERFORMANCE (C ASH F LOW P ER S HARE ) OF C RACKER B ARREL (NDQ- CBRL) OVER THE R ESTAURANT I NDUSTRY G ROWTH C YCLE .......................................................... 183 T ABLE 18. S CORING THE F INANCIAL P ERFORMANCE (C ASH F LOW P ER S HARE ) OF L UBY ' S C AFETERIAS (NYSE-LUB) OVER THE R ESTAURANT I NDUSTRY G ROWTH C YCLE ................................................ 184 T ABLE 19. S CORING THE F INANCIAL P ERFORMANCE (C ASH F LOW P ER S HARE ) OF P ICCADILLY (NYSE-PIC) OVER THE R ESTAURANT I NDUSTRY G ROWTH C YCLE ...................................................................... 185 T ABLE 20. F INANCIAL P ERFORMANCE (C ASH F LOW P ER S HARE ) OF R YAN ' S F AMILY S TEAK (NDQ-RYAN) OVER THE R ESTAURANT I NDUSTRY G ROWTH C YCLE ...................................................................... 186 T ABLE 21. S CORING THE F INANCIAL P ERFORMANCE (C ASH F LOW P ER S HARE ) OF S HONEY ' S I NC . (NYSE- SHN) OVER THE R ESTAURANT I NDUSTRY G ROWTH C YCLE ............................................................ 187 T ABLE 22. S CORING THE F INANCIAL P ERFORMANCE (C ASH F LOW P ER S HARE ) OF V ICORP R EST (NDQ- VRES) OVER THE R ESTAURANT I NDUSTRY G ROWTH C YCLE .......................................................... 188 T ABLE 23. C LASSIFICATION OF THE R ESTAURANT F IRMS : H IGH P ERFORMERS , N EUTRAL , AND L OW P ERFORMERS ............................................................................................................................... . 191 T ABLE 24. T HE K APPA M EASURE OF A GREEMENT : H IGH P ERFORMERS C APITAL S PENDING ...................... 196 T ABLE 25. T HE K APPA M EASURE OF A GREEMENT : L OW P ERFORMERS C APITAL S PENDING ....................... 196 T ABLE 26. T HE K APPA M EASURE OF A GREEMENT : H IGH P ERFORMERS ' C OMMON S HARE O UTSTANDING .. 198 T ABLE 27. T HE K APPA M EASURE OF A GREEMENT : L OW P ERFORMERS ' C OMMON S HARE O UTSTANDING ... 198 T ABLE 28. T HE K APPA M EASURE OF A GREEMENT : H IGH P ERFORMERS ' EPS............................................ 201 T ABLE 29. T HE K APPA M EASURE OF A GREEMENT : L OW P ERFORMERS ' EPS ............................................ 201 xii T ABLE 30. T HE K APPA M EASURE OF A GREEMENT : H IGH P ERFORMERS ' CFPS ......................................... 204 T ABLE 31. T HE K APPA M EASURE OF A GREEMENT : L OW P ERFORMERS ' CFPS.......................................... 204 T ABLE 32. T HE K APPA M EASURE OF A GREEMENT : H IGH P ERFORMERS ' BV-A SSET .................................. 206 T ABLE 33. T HE K APPA M EASURE OF A GREEMENT : L OW P ERFORMERS ' BV-A SSET ................................... 206 T ABLE 34. T HE K APPA M EASURE OF A GREEMENT : H IGH P ERFORMERS ' P/E R ATIO .................................. 209 T ABLE 35. T HE K APPA M EASURE OF A GREEMENT : L OW P ERFORMERS ' P/E R ATIO ................................... 209 T ABLE 36. T HE K APPA M EASURE OF A GREEMENT : H IGH P ERFORMERS ' L-T D EBT ................................... 211 T ABLE 37. T HE K APPA M EASURE OF A GREEMENT : L OW P ERFORMERS ' L-T D EBT .................................... 211 T ABLE 38. S UMMARY OF THE K APPA M EASURE OF A GREEMENT FOR THE E VENTS (E XPANSION /C ONTRACTIONS ) IN THE R ESTAURANT I NDUSTRY C YCLES AND THOSE OF H IGH P ERFORMERS ' F INANCIAL P RACTICES ............................................................................................ 216 T ABLE 39. S UMMARY OF THE K APPA M EASURE OF A GREEMENT FOR THE E VENTS (E XPANSION /C ONTRACTIONS ) IN THE R ESTAURANT I NDUSTRY C YCLES AND THOSE OF L OW P ERFORMERS ' F INANCIAL P RACTICES ............................................................................................ 217 1 CHAPTER 1: INTRODUCTION 2 Problem Statement PART I Need for Developing a Restaurant Industry Cycle Model and Its Economic Indicator System Forecasting and Hospitality Business A major function of management is planning, and a subset of the planning function is forecasting. Forecasting is generally used to predict what will happen in a given set of circumstances. The forecast gives an idea of expected results if management makes no changes in the way things are done. In planning, forecasts are used to help make decisions about which circumstances will be most desirable for the hospitality operation. Thus, if a forecast shows room demand will decrease next month, management should prepare an action plan to prevent a sales decline. After the action plan is completed, a new forecast must be made to reflect the impact of the action plan. Forecasting is pervasive in hospitality operations. Every hospitality manager 9s job includes forecasting, which is the calculation and prediction of future events such as sales for the following day, week, or month. Forecasting is necessary in order to plan the most effective and efficient ways to meet an expected sales volume. For example, if the food and beverage manager of a hotel forecasts 500 dinner guests, then food, beverage, and other supplies must be obtained, and the appropriate personnel must be scheduled to prepare and serve the food and beverages to the guests. Generally, the accuracy of sales forecasts is a major determinant of the cost effectiveness of the hospitality operation. For instance, if forty meals are forecast and fifty guests show up, the food and beverage provisions and the number of employees scheduled to work may not be adequate. This may result in poor service and overtime wages. On the other hand, if sixty meals had been forecast and fifty guests showed up, service would probably have been outstanding; however, due to possibly excessive labor costs, efficiency would have been reduced. 3 The need for accurate forecasts of hospitality and tourism demand and supply is well recognized. As an example, Archer (1987) points out that cIn the tourism industry, in common with most other service sectors, the need to forecast accurately is especially acute because of the perishable nature of the product. Unfilled airline seats and unused hotel rooms cannot be stockpiled d (p.77). However, although the benefits of accurate forecasts to the hospitality and tourism industry are clear, no forecast can guarantee complete accuracy. The aim of demand forecasting, therefore, is to predict the most probable level of demand and supply that is likely to occur in the light of known circumstances or, when alternative policies are proposed, to show the different levels of demand that may be achieved. Forecasting is also an essential element in the process of management. No manager can avoid the need for some form of forecasting. A manager must plan for the future in order to minimize the risk of failure or, more optimistically, a manager must use forecasts. Forecasts will always be made, whether by guesswork, teamwork, or the use of complex models, and the accuracy of the forecasts will affect the quality of the management decision. Industry Business Cycle Industries react in different ways to the business cycle fluctuations of the U.S. economy (Berman and Pfleeger, 1997). Some industries are very vulnerable to economic swings, while others are relatively immune to them. For those industries that are characterized as cyclical, the degree and timing of these fluctuations vary widely. The industries that experience only modest gains during expansionary periods may also suffer only mildly during contractions, and those that recover fastest from recessions may also feel the impact of a downturn earlier and more strongly than other industries. Churchill and Lewis (1984) examined how over 1000 small firms adapted to a recession. Fay and Medoff (1985), through a small survey of 168 plant managers, examined the labor adjustment of firms in downturns and noted that firms retained more workers than were immediately needed. Mascarenhas and Aaker (1989) concluded that 4 firms do indeed adjust their strategies systematically over cycle stages, and managers should try to maintain flexibility with respect to the strategy choices exhibiting changing relationships with profitability over the cycle, and exploit their contemporaneous, leading, or lagging effects. According to a previous study (Choi, 1996), in the hotel industry, there were many chances to gain competitive advantages over the cycles, but many companies missed the opportunities because there were fears to take business actions at different stages of the industry cycle. Such fears stopped many U.S. hotel investors from buying at the bottom of the cyclical troughs in 1969,1974,1982, and 1991. Many of them also missed opportunities to add value by selling their assets at the cyclical peaks in 1967, 1973, 1980, and 1989. Muller and Woods (1994) emphasize that accuracy in forecasting, when business is highly predictable and cyclical, can offer significant competitive advantages. Relying on accurate forecasting also means that margins can be kept slim, giving a company an edge in the competitive bidding process for new business. Early signals of recession or of recovery are of great interest to business people, policy makers, job seekers, and investors. Because such decision makers consider turning points in the aggregate level of economic activity to be of special importance, considerable effort has been spent to forecast when these turns will occur. Moncarz and Kron (1993) also remind us that early warning signals are vital in assessing a company 9s health. Recognizing that a business is in financial distress and identifying its developing problems provides the best chance to take the necessary corrective action to turn the firm around. A reasonable way to forecast these turning points is to search for sectors of the economy that tend to lead the overall economy; observed turning points in these sectors would suggest that the overall economy will soon turn. The business cycle analysis techniques have been used mainly for identifying general business activities as a whole. Between the 1920 9s and 1940 9s, one such technique developed by the National Bureau of Economic Research (NBER), business cycle dating and analysis techniques, was criticized as a measurement without theory (Niemira and Klein, 1994). The pros and cons of this 5 criticism are well documented in the literature. Nonetheless, the techniques survived, thrived, and are now well founded in economic theory. That these techniques stood the test of time reflects the usefulness of this approach for business and policy makers (Niemira and Klein, 1994). One of the most striking aspects of the business cycle is that it is a phenomenon which, sooner or later, is reflected in similar patterns in almost every macro-economic variable, thus illustrating their interdependence (Berk and Bikker, 1995). Such interdependence is not restricted to national macro-economic variables either; it is also an industry phenomenon. It is important to understand the industry business environment if we are to forecast the impact of the cycle on our firm and to fix strategy on the basis of that forecast. In a sense, measuring, monitoring and forecasting business cycles is a relatively new class of methods in investigating the industry 9s overall phenomena. The systematic analysis of cycles in the hotel and restaurant business provides clues to help us forecast future direction and improve our ability to manage. It can be applied to almost any type of business function. Aside from such obvious applications as the advance purchase of inventory and borrowing, an understanding of long-wave business cycles would also provide specific information as to when to be aggressive in expanding business operations, when to sell businesses, and even when to enter certain types of new business. Monitoring and forecasting hotel and restaurant industry cycles clearly gives the manager insight into industry turning points. Moreover, a company that quickly recognizes a change in the phase of the industry cycle could use either a recession or a recovery strategy to optimize profit. To take any benefit from this type of analysis, it is necessary to understand the functions of the industry cycle models and economic indicator system. Economic Indicator System Economic indicators, as a general category, are descriptive anticipatory data used as tools for business condition analysis and forecasting (Zarnowitz and Moore, 1977). There are potentially as many subsets of indicators in this sense as there are 6 different targets at which they can be directed. As an example, some indicators may relate to employment, others to inflation. This may lead to the uses of such time series as lagged explanatory variables in econometric models and regression equations. But there is a different, established meaning to what is often called the cindicator approach. d This is a system of data and procedures designed to monitor, signal, and confirm cyclical changes, especially turning points, in the economy at large. The series that serve this purpose are selected for being comprehensively and systematically related to business cycles and are known as cyclical indicators (Zarnowitz, 1992). What matters particularly in the present context is the characteristic variation of cyclical indicators with respect to their relative timing (Zarnowitz 1992). Thus many economic time series, called leading indicators, tend to reach their turning points before the corresponding business cycle turns. Turns in the series of coincident economic indicators occur roughly at the same time as those of the business cycle. They go down at the peak and up at the time of the trough. There are also many series that tend to reach their turning points after the peaks and troughs in the business cycle, and they are the lagging indicators. Geoffrey Moore (1983) explains some of the particular reasons why series normally turn at different times: More especially, series that represent early stages of production and investment processes (new orders for durable goods, housing starts, or permits) lead series that represent late stages (finished output, investment expenditures). Under uncertainty, less binding decisions are taken first. For example, hours of work are lengthened (shortened) before the workforce is altered by new hiring (layoffs) (Moore 1983, p. 27) Leading series anticipate impending changes in production and employment and, therefore, changes in aggregate economic activity. Some of the lagging indicators lag because they represent activities that are influenced by the cycle. Thus interest rates, for example, usually lag behind the cyclical downturn because the downturn causes 7 emergency credit needs, which are accommodated in part but are charged at higher interest rates (Sherman, 1991). Each type of indicator series serves to qualify or to support the information or evidence supplied by the other two categories. The function of leading economic indicators is to warn of impending changes in economic activity. The coincident indicators are useful for helping to track the course of the economy, but do not provide much help in predicting future turning points. The lagging indicators have no use in predicting the beginning or end of recession, but it can be useful in helping verify that a recession has actually started or ended. Thus, each type of indicator can be a good tool to track the changes in aggregate activity of a certain economy or industry. There has been no effort to make a study for developing the restaurant industry cycle model and it 9s economic indicator system. Further, there has been no research that has empirically examined the intersection between the hospitality industry (including hotel and restaurant industry) cycle, strategy, and strategic outcomes. Hospitality and Tourism Industry Forecasting What would be the significance of such turning points to the hospitality and tourism industry? It would lead to the elimination or reduction of the industry cycle risk. This should then presumably result in an improvement in the investment decisions of firms, thereby improving their effective allocation of resources. In the hospitality and tourism industry literature, there are no studies using economic indicators to determine and analyze industry cycles. As reviewed and summarized in chapter two, most of the studies deal with different methods or techniques, and focus on different subjects. Most of the forecasting studies focus on the tourism industry (Armstrong, 1972; Shaw, 1979; Smith, 1979; Uysal and Crompton, 1985;Moutinho and Witt, 1985; Calantone, Benedetto, and Bojanic, 1987; Martin and Witt, 1989;Yang, Keng, and Leng, 1989; Witt and Witt, 1990; Morley, 1991; Enders, Sandler, and Parise, 1992; Athiyaman and Robertson, 1992; Witt, Newbould, and Watkkins, 1992; Sheldon, 1993; Baum and Mudambi, 1994; Bloom and Leibold, 1994; Tonini, 1994; Moutinho and Witt, 1995) 8 focused on air traffic forecasting, tourism demand forecasting, terrorism impact, and the tourism environment. For the hotel industry, there are some studies focused on capacity (Lambert, et al, 1989; Yesawich, 1993; Scott et al, 1995), lodging performance (Wood, 1994), customer expectations (Schuster, 1996), economic and market condition forecasting (Yesawich, 1984; Olsen, 1989; Littlejohn and Watson, 1990; Chon and Singh, 1993; Olsen, Murthy, and Teare, 1993), forecasting with time series (Pheifer and Bodily, 1990; Bonham, Carl, et al, 1992; Bonham, Carl, and Gangnes, 1996; Smith and Lesure, 1996; Wheaton and Rossoff, 1998), and the hotel business cycle and economic indicator system (Choi, 1996; Choi et al., 1997a; Choi et al., 1997b; Choi et al., 1999). Most of the studies are qualitative in nature. Providing industry experts 9 opinion or discussion is the major character of the studies. Some of the studies analyzed the industry future more systematically. Rushmore (1992) estimated occupancy for the proposed Sheraton Hotel in his book. His method for occupancy estimation was simple. He averaged the occupancy levels recorded during a 20-year period (he called it the cOccupancy Cycle d) and stabilized it based on the market demand and supply. The growth and turning points of the hotel and restaurant industry, however, can not be projected by a few operational indicators such as occupancy rate. This is because the industry phenomena interact strongly with the rest of the economy. To project the industry growth and turning points, developing economic indicator systems based on a wide-range of economic variables for the particular industry is required. Besides, occupancy rate does not reflect the industry 9s total output. It just shows how many rooms available in the industry are occupied in a given period. In short, the rate could be 100 percent if the rooms are free of charge. Another study (Smith and Lesure, 1996) examined lodging industry trends by using the twelve months moving average technique. They examined the average trends in supply, demand, and room sales for different geographic hotel markets, location types, price segments and Census regions since 1991. Time series approaches always assume 9 that a pattern recurs over time which may be used to forecast values for any subsequent time period. This study, for instance, used past occupancy percentage changes to project future occupancy percentage changes. That is, this technique was concerned solely with the statistical analysis of past data for the same single variable to be forecast. Again, this study also relied on single variables to forecast the change of the same variables in the future. Considering single variables to forecast future industry business direction has a built-in disadvantage in terms of accuracy because of the dynamic and complex nature of the business environment. It is true that the hotel and restaurant industry is highly dependent on the rest of the economy; if the economy goes into recession the performance of the industry will fall. The occupancy percentages and asset prices of the hotel and restaurant business do not move in a vacuum, somehow independent of what is happening within the rest of the economy. All markets are interrelated and many business environmental variables need to be considered together. Understanding the industry requires an understanding of how it interacts with the rest of the economy. Therefore, in analyzing the cyclical nature of the industry and forecasting the industry cycle, it is essential to recognize that the various markets are highly interrelated, and thus it is necessary to analyze as many economic indicators as possible. Failure to recognize the likely reactions to current events and policies results in a great deal of confusion, bad policy and poor investment decisions. Increasing awareness of the linkages that exist is the greatest single way that investors can improve their results. Wheaton and Rossoff (1998) examined whether the hotel market moved closely with the overall economy. They concluded that the demand for hotel night stays moves very closely with U.S. GDP. However, new hotel investment moves in a long range pattern that bears little connection to macroeconomic fluctuation. Further, the average hotel rental rate displays this same long run pattern and moves almost independently of short term demand shocks. They concluded that the industry appears reluctant to rapidly adjust rental rates in response to the kind of short-run changes in occupancy that are caused by the economy. In other words, hotel investments as well as pricing are not based 10 upon the informative messenger-economic indicators- even though they move closely with the hotel market. Now it is clear that a clue for understanding the industry comes from the highly correlated complex relationships between the industry and the rest of the economy. That is, the high interdependence of the industry with the rest of the economy is not only bad news but also good news for projecting the industry future if the relationships between the industry and economic variables are identified. Choi (1996) investigated this. He identified the cyclical characteristics that exist between the hotel industry and various economic indicators. He developed the US hotel industry cycle model and analyzed the patterns of the changes (see Chapter Two for more detail). The model provides information including the cyclical nature of the industry cycle, projected cyclical turning points and growth rates. The results of the study provide useful guideposts for taking every possible advantage of the cycle study to the practitioners and researchers in the hotel industry. Choi (1996) also develops the Economic Indicator System as a forecasting technique for the hotel industry. He identified and selected seventy economic indicators for the hotel industry by reviewing literature and testing the characteristics of each time series that are available in public. By classifying the indicators into leading, coincident, and lagging indicators, this study formed composite indices for the groups of indicators and defined the relationships in terms of time lags between the hotel industry growth cycle and the series of composite indices. The performances of the composite indices for the leading, coincident, and lagging indicators were measured based on their timing differences of turning points compared with those of the industry cycles. The usefulness and effectiveness of the indicator system composed of composite indices of leading, coincident, and lagging indicators were empirically supported in the study. Restaurant Industry Forecasting It is not difficult to find literature discussing the impact of forecasting on food management. In fact, the forecasting function has an effect on many components 11 contributing to the overall success of the foodservice (Messersmith and Miller, 1992). However, the literature on forecasting in the restaurant industry is very limited in terms at least of the number of studies. Some of the studies introduce a menu item forecasting system (Messersmith, Moore, & Hoover, 1978), discuss traditional planning problems of the restaurant industry (Wacker, 1985), explain forecasting menu item demand in food service operations (Miller and Shanklin, 1988), forecast restaurant sales (Forst, 1992), introduce general forecasting techniques for restaurant operation (Messersmith and Miller, 1992), present a case study for demand forecasting (Yavas, 1996), and discuss market trends (Silverstone, 1993; Troyer, 1996). Most of the studies are discussions and thus hard to apply to dynamic and complex economic trends and therefore industry's overall trends. There is no systematic forecasting study for the restaurant industry as a whole and no restaurant industry business cycle study and its economic indicator system. A Tool for Restaurant Industry Forecasting As stated above, however, it is possible to improve our forecasts by finding the relationship between changes in the industry 9s as well as specific company 9s business cycles and changes in the overall economy. As several studies (Choi, 1996; Choi et al., 1997a; Choi et al., 1997b; Choi et al., 1999; Wheaton and Rossoff, 1998) already discovered, these relationships exist, and once they are uncovered, accurate forecasting is then a simple matter of monitoring changes in those economic indicators and determining their effect on the future trends of the industry and sales of individual companies. Therefore, there is merit to developing a systematic industry cycle model as a forecasting tool and providing a guidepost for the restaurant business managers and investors. 12 Part II Need for Examining Financial Practices (or Strategies) of the High and Low Performing Firms for the period of Peaks and Troughs of the Restaurant Industry Cycle It is imperative that top managers have a good understanding of the strategic nature of their industry so that they can effectively select businesses and allocate resources. Industry analysis is the starting point for almost any strategic plan. It is the process through which managers can evaluate the factors within the environment critical for business success (Bernhardt, 1993). To have an effective strategy, competitive intelligence should focus on information related to competitor analyses, environmental trends, and market dynamics (Sammon, Kurland, and Spitalnic, 1984; Cartwritht, Boughton, and Miller, 1995). Competitive intelligence has to incorporate probable future developments and changes in the structure of the industry and the market if it is to be of any practical value for managers (Bernhardt, 1993). There are some studies for turnaround strategies: Schendel, Patten, and Riggs (1975), Hofer (1980) and Bibeault (1982). Schendel et al. (1975) studied 54 firms that, based on Compustat data, had suffered four consecutive years of earnings decline and then four consecutive years of earnings improvement. Using business periodicals, the authors subjectively rated the causes of the declines and the actions accounting for the upturns and classified each as either "strategic" or "operating" in nature. The authors generally found support for their theory: that declines caused by operating problems (e.g., production bottlenecks, labor strife) tend to be followed by operating cures (e.g., new cost controls, plant modernization) and that declines caused by strategic factors (e.g., obsolete products, intense price competition) tend to be followed by strategic cures (e.g., new products, redefining the business). 13 By analyzing written cases on 12 poorly performing firms, Hofer (1980) found support for his theory that the appropriateness of a strategic or operating turnaround depends on whether the firm's "illness" stems from poor strategy or poor operations. He also laid out a framework for choosing among different operating turnarounds according to the firm's closeness to breakeven, and here again he found some support. In particular, he found that firms operating close to breakeven tended to turn around successfully if they pursued cost-cutting strategies and that firms operating far below breakeven required more ambitious revenue-increasing or asset reduction strategies. Bibeault (1982) conducted a survey of 81 chief executives who had faced turnaround situations. He coupled the data with anecdotes to discuss why failures occurred, characteristics of successful and unsuccessful turnarounds, and leadership aspects of turnarounds. He concluded that most turnarounds involve five stages. First, is the management change stage (Hofer, 1980) agrees that a change in top management almost always is required). Second is the evaluation stage (generally a matter of several weeks). Third is the emergency stage ("stop the bleeding" or "unloading"). Fourth is the stabilization stage (with emphasis on organizational rebuilding). Fifth is the return-to- normal growth stage (new products and other entrepreneurial activity). It implies that cost cutting and/or asset reduction is done before any entrepreneurial activity is undertaken. Hofer (1980) indirectly expressed some agreement saying that, in general, efficiency-oriented moves tend to produce the quickest, most dramatic results. The study of the impact of the restaurant industry cycle on restaurant firms (both high and low performers) and their financial practices over the cycle warrant our attention. This is because the complications posed by the restaurant industry cycles may call for different strategies. Because of the nature of heterogeneity, a strategy may not be equally effective over the restaurant industry cycle, and compromise strategies that are less than optimal for either an up or down market or dynamic strategies with built-in cycle adjustments may be needed. 14 Research on strategy in a cyclical environment has been provocative, though limited in scope and focus. Several articles have suggested the need for strategy adjustments over the business cycle. The potential use of counter-cyclical strategies has been discussed by Dhalla (1980) for advertising, Greer (1984) for employment hiring, and Nolan (1982) for data processing investments. Few studies have empirically examined firm strategies over the business cycle. Churchill and Lewis (1984) examined how over 1000 small firms adapted to a recession. Fay and Medoff (1985), through a small survey of 168 plant managers, examined the labor adjustment of firms in downturns and noted that firms retained more workers than were immediately needed. Hultgren (1965) examined the indices of prices, costs, volume and profits of the aggregate manufacturing sector over several economic cycles and observed that unit costs move inversely with sales but with a lag. Mascarenhas and Aaker (1989) analyzed strategy over the business cycle and concluded that firms adjusted their strategies significantly and asymmetrically over business cycle stages and there was no consistency in performance between up markets and down markets. Ruggeri (1991) explained the usefulness of the business cycle for forecasting future directions of a business. Some other studies focused on the determinants of the cyclical behavior of real industrial output and price. Mankiw (1990) provides a theoretical explanation of industrial business cycle. The cyclical behavior of real industrial output and price is dependent on flexibility of the nominal wage in the face of aggregate demand shocks (Kendil, 1997). Identifying and understanding the various strategies of different firms can provide managers with the capability to foresee the impact on industry structure and evolution. Analyzing the variety of competitors within the industry can be immensely helpful in predicting future industry conditions (Kight, 1996). On the other hand, the impact of the business cycle on firm strategy has been neglected in strategy research (Bishop, Graham and Jones, 1984). This claim can be well applied to the restaurant industry. Part one of this study provides useful information for the industry cyclical nature and trends. A firm that fails to take into account the changes occurring in the industry and the broader macro-environment will miss out on 15 opportunities, and will be vulnerable to external threats. Inability to identify and respond to how external changes reflect on the industry cycle would subject the firm to serious competitive attacks. Risk is often defined as the variation in returns (probable outcomes) over the life of an investment project. Uncertainty refers to a state of knowledge about the variable inputs to an economic analysis. If restaurant management is unsure of the value of the information, there is uncertainty. The uncertainty of the market and other factors in the restaurant creates risks to the business. The general financial decision rules 1 such as the NPV (Net Present Value) method, therefore, should be adjusted to the cyclical nature of the business, which creates dynamic risk and uncertainty in practicing financial strategies in the restaurant industry. Within an industry, it is true that some firms perform well during a certain phase of a cycle

less