case on mis

[gagandsaluja]

MEASUREMENT OF RESTAURANT MANAGER PERCEPTIONS OF
RESTAURANT MANAGEMENT INFORMATION SYSTEMS
DISSERTATION
Presented in Partial Fulfillment of the Requirements for
The Degree Doctor of Philosophy in the Graduate
School of The Ohio State University
By
Marsha M. Huber, M.B.A.
*****
The Ohio State University
2003
Dissertation Committee:
Professor R. Thomas George, Adviser
Professor P. C. Chu Approved by
Professor W. Johnson ________________________________
Adviser
Professor H. G. Parsa College of Human Ecology:
Hospitality Management Program
Copyright by
Marsha Huber
2003
ii
ABSTRACT
The strategic use of management information systems (MIS) can convey
competitive value. For this reason, it is important to understand which antecedents are
associated with system success. By utilizing theoretical assumptions from several fields:
strategic management, MIS, and hospitality, this study develops a model of Restaurant
Management Information System (RMIS) success for the foodservice industry.
The purpose of this study was to identify current information technology (IT)
trends in the foodservice industry, to identify the types and quality of IT training and
support offered to managers, and to develop and test the RMIS model. This study uses
survey research. A survey was administered to restaurant managers (n = 243) to gather
data about their system features and effectiveness.
This study demonstrated several important findings. First, many foodservice
establishments are utilizing systems more than indicated in earlier foodservice literature.
Food and labor cost analyses, sales forecasts, server performance evaluations, menu
analysis, and e-mail are commonly utilized by today’s restaurant manager.
Second, this study provides support for contingency theory, that is, firms do not
use systems equally. Full and quick service restaurants, chains and independents, and
successful and unsuccessful restaurants all utilized systems differently.
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Third, this study provided partial support for systems implementation theory.
This study found that training related to system success, but not support. The availability
(hours) of support provided by the “help desk” did not relate to system success.
Lastly, this study used regression analysis to test the RMIS research model. The
first regression model of RMIS success, with decision-making support satisfaction as a
dependent variable, exhibited a fit of .450. Four antecedents – system use, system quality,
report quality, and training quality –were significant.
Sensitivity analysis was conducted on the regression analysis, and the concept of
FIT emerged as a potentially important dependent variable. This yielded an adjusted r2 of
.608. Five antecedents – system quality, report quality, user competency, competitive
rating, and ownership type –were significant. The adjusted r2 of .608 implies that FIT
might be a better measure of system success than decision-making satisfaction for certain
industries or levels of management.
iv
Dedicated to my family
v
ACKNOWLEDGMENTS
I want to thank my advisor, Dr. R. Thomas George, for his intellectual support,
guidance, and encouragement which made this dissertation possible. He truly exemplifies
what it means to be a “mentor.” I wish to convey my deep gratitude to him for his
unwavering support of my many endeavors over the last five years.
Next, I want to thank the complete dissertation committee: Dr. H.G. Parsa, Dr. P.
C. Chu, and Dr. W. Johnson. Dr. Chu introduced me to the topic of MIS in a graduate
class, and his succinct use of models helped me lay the theoretical foundation for this
study. Dr. Parsa pushed me to stretch myself personally and intellectually. He encouraged
me to write papers and attend conferences which helped me to develop my scholarly
abilities. Over the last two years, each individual has provided me with insights that
guided my thinking and writing, substantially improving this study.
I am also grateful for the support I received from my colleagues at Otterbein
College. My chairperson, Chuck Smith, reviewed several of my chapters and relieved me
of certain departmental duties so that I could complete my dissertation. Others – Shirine
Mafi, Lori Callihan, Kyriacous Aristotelous, and David Dennis – helped me either by
reviewing my materials or encouraging me to press forward. A special thanks to
Kyriacous for his help with the regression analysis portion of my study.
vi
I wish to acknowledge the survey respondents, notably, the Central Ohio
Restaurant Association for supporting my research and providing me with a mailing list.
In addition, I want to thank the Cameron Mitchell restaurants, Steak Escape, Frish’s Big
Boy, and my restaurant clients for allowing me to survey and interview their managers.
I want to thank my friends also for helping me in many ways – stuffing envelopes,
proofreading, and enduring with me. Those friends are: Sandra Naipaul, David Lyon,
Chris Selch, Carol Zimmerman, Lynn Loofbourrow, Lois Palau, and Steven King. The
value of their emotional support is priceless. A special thanks to Chris for helping me
conceptualize my early ideas; Carol for helping me during “crunch” times; Lynn for the
late night dinners; and David for running errands for me and taking care of me when I
became especially busy.
I want to thank my Bible Study group for their two years of prayer for my
successful completion of my dissertation. In addition, I wish to thank my friends at
Pinecrest Bible Training Institute who also encouraged me and supported me in prayer.
In addition, I want to thank departmental secretary, Ethel Hurley; statistics
consultant, Dr. Mohammed Rahman; and mailroom personnel, Mari and Priscilla, for
helping me with this project. I also want to acknowledge the funding I received to support
my endeavors from both Ohio State and Otterbein College.
Most importantly, I want to thank my family for providing me with an excellent
upbringing. No matter how great of an accomplishment this dissertation is, my mother’s
and maternal grandparents faith in Christ, is the gift I cherish above all others.
vii
VITA
November 10, 1960 ……………… Born – Chicago, Illinois, USA
1981………………………………. Bachelor of Arts, Political Science, Ohio University
1983 ……………………………… M.B.A., Accountancy, Miami University (Ohio)
1984 – 1986 ……………………… Certified Public Accountant
Wilson, Shannon, and Snow, CPAs
Newark, Ohio
1986 – present …………………… Associate Professor of Accounting
Otterbein College
Dept. of Business, Accounting, and Economics
Westerville, Ohio
1989 – present …………………… Certified Public Accountant
Huber Consulting, Inc.
Westerville, OH
PUBLICATIONS
Research Publication
1. Huber, M. and Pilmanis, P. (2001). "Strategic Information Systems in the Quick
Service Restaurant Industry: A Case Study Approach." Journal of Restaurant &
Food Service Marketing, Volume 4, Number 4 (2001).
Reprinted in: Parsa, H.G. and Kwansa, F. A. (Eds.). (2001). Quick Service
Restaurants, Franchising, & Multi-Unit Chain Management, The Haworth
Hospitality Press, 15 pages.
Books
2. Huber, M. A CPA's Guide to Restaurant Management Strategies: Accounting,
Cost Controls & Analysis (2000). American Institute of Certified Public
Accountants.
viii
3. Huber, M. Restaurant Management Strategies (1996). American Institute of
Certified Public Accountants.
Chapters in Books
4. Huber, M. "Tax Evasion," American Justice, Salem Press, (1995).
5. Huber, M. "Eileen Ford," Great Lives from History: American Women, Salem
Press, (1995).
6. Huber, M. "Margaret Rudkin," Great Lives from History: American Women,
Salem Press, (1995).
7. Huber, M. "Martha Stewart," Great Lives from History: American Women,
Salem Press, (1995).
8. Huber, M. "U.S. Tax Laws Allow Accelerated Depreciation," Great Events from
History II: Business and Commerce, Salem Press, (1994).
9. Huber, M. "1931 New York Case of Ultramares," Great Events from History II:
Business and Commerce, Salem Press, (1994).
10. Huber, M. "Restaurants," Encyclopedia of Accounting Systems, Prentice Hall,
(1994).
11. Huber, M. "Automobile Repair Shops," Encyclopedia of Accounting Systems,
Prentice Hall, (1994).
Articles
12. Huber, M. "Accounting in Great Britain," New Accountant Magazine, February,
1992.
13. Huber, M. "Home Office Strategies," New Accountant Magazine, April, 1991.
FIELDS OF STUDY
Major Field: Human Ecology:
Hospitality Management
Minor Fields: Information Systems and Research Methods
ix
TABLE OF CONTENTS
Page
Abstrac ii
Dedication .......................................................................................................................... iv
Acknowledgements............................................................................................................. v
Vita.... vii
List of Tables .................................................................................................................. xiii
List of Figures ................................................................................................................. xvi
Chapters:
1. I 1
The Foodservice Industry ...................................................................................... 2
Competitive Pressures............................................................................................. 3
Accounting and Management Information Systems............................................... 5
Statement of Problem.............................................................................................. 6
Research Approach ................................................................................................ 7
Significance of the Study ....................................................................................... 8
Limitations of the Study.......................................................................................... 9
Outline of Dissertation ......................................................................................... 10
Explanation of Terms ........................................................................................... 11
2. Literature Review.......................................................................................................... 13
Historical Development of Strategic Thought in IS ............................................. 14
Evolution of IS Usage in the Foodservice Industry.............................................. 16
Systems Planning .................................................................................................. 20
Planning Success.......................................................................................... 22
Strategic Orientation .................................................................................... 24
Managerial Fit............................................................................................. 27
Foodservice Planning and Development ..................................................... 28
x
Systems Implementation....................................................................................... 31
Training and Support ................................................................................... 31
User Attributes and Competence .......................................................................... 33
The Strategic Use of Systems ............................................................................... 34
Strategic Use in the Foodservice Industry ................................................... 37
System Success ...................................................................................................... 38
Research Questions and Model.............................................................................. 44
Summary ................................................................................................................ 47
3. Research Methodology ................................................................................................. 49
Research Design.................................................................................................... 49
Research Variables................................................................................................ 51
System Use .................................................................................................. 52
Training and Support ................................................................................... 53
Manager Characteristics............................................................................... 53
Organizational Characteristics ..................................................................... 53
Managerial Fit.............................................................................................. 54
RMIS Quality............................................................................................... 54
Decision-Making Support Satisfaction ........................................................ 55
Instrument Development....................................................................................... 56
Pre-testing .................................................................................................... 58
Instrument Validity ...................................................................................... 58
Face Validity................................................................................... 58
Content Validity.............................................................................. 58
Criterion-related Validity................................................................ 59
Construct Validity........................................................................... 59
Instrument Reliability ..................................................................... 60
Population and Sampling ...................................................................................... 61
Sample Size.................................................................................................. 61
Data Collection ..................................................................................................... 62
Response Rate.............................................................................................. 62
Generalizability of Sample .......................................................................... 63
Non-response Error...................................................................................... 64
Statistical Analysis................................................................................................ 65
Summary ............................................................................................................... 67
4. Results and Discussion ............................................................................................... 70
Research Question 1 ............................................................................................. 71
Manager Characteristics............................................................................... 71
Gender..................................................................................................... 71
Education ................................................................................................ 72
Hours Worked per Week, Age, and Experience ..................................... 72
Restaurant Characteristics............................................................................ 74
xi
Segments ................................................................................................. 74
Guest Check and Number of Employees ................................................ 74
Ownership ............................................................................................... 75
Sales Volume .......................................................................................... 76
Financial Success.................................................................................... 77
Research Question 2 ............................................................................................. 78
Classification Scheme.................................................................................. 78
Competitive Rating ...................................................................................... 81
Current Software Usage............................................................................... 82
Contingency Theory..................................................................................... 84
Financial Success ......................................................................................... 90
Research Question 3 ............................................................................................. 91
Training........................................................................................................ 91
Support......................................................................................................... 94
Research Question 4: ........................................................................................... 96
Correlations.................................................................................................. 98
Research Question 5 .......................................................................................... 100
Model specification.................................................................................... 100
Evaluation of Fit ........................................................................................ 101
Significance of Regression Model............................................................. 102
Significance of Variables........................................................................... 103
Regression Assumptions............................................................................ 103
Sensitivity Analysis of Regression Equation............................................. 104
Research Question 6 ........................................................................................... 108
ANOVA Assumptions ............................................................................... 112
Manager Comments ................................................................................... 113
Positives .............................................................................................. 113
Shortcomings ...................................................................................... 114
Barriers................................................................................................ 114
Wish List............................................................................................. 114
Summary ............................................................................................................. 115
Research Question 1 ................................................................................... 115
Research Question 2 ................................................................................... 116
Research Question 3 ................................................................................... 117
Research Question 4 ................................................................................... 118
Research Question 5 ................................................................................... 118
Research Question 6 ................................................................................... 119
Appendix: Regression Assumptions .................................................................... 120
Assumption 1 .............................................................................................. 120
Assumption 2 .............................................................................................. 120
Assumption 3 .............................................................................................. 121
Assumption 4 .............................................................................................. 122
Assumption 5 .............................................................................................. 123
5. Conclusion ................................................................................................................. 124
xii
Theoretical Contributions .................................................................................. 124
Methodological Contributions ............................................................................ 126
Limitations .......................................................................................................... 127
Suggestions for Future Research ........................................................................ 128
Implications for Practice .................................................................................... 129
Concluding Remarks .......................................................................................... 129
131
A. Survey Instrument ......................................................................................... 131
B. Cover Letters................................................................................................. 140
List of References ........................................................................................................... 143
xiii
LIST OF TABLES
Table
3.1 Operationalization of dependent variable, DSS, and its antecedents..................... 56
3.2 Factor loadings for primary constructs .................................................................. 60
3.3 Restaurant brands in the sample ............................................................................ 63
3.4 ANOVA for DSS for each week collected ............................................................ 64
3.5 Coefficients for association.................................................................................... 65
3.6 Summary of research objectives and data analysis........................................... 68-69
4.1 Manager demographics: gender............................................................................. 71
4.2 Manager demographics: education ..................................................................... 72
4.3 Manager demographics: hours worked per week, experience, and age................. 73
4.4 Standardized years of experience for 40-year old manager................................... 74
4.5 Restaurant demographics: guest check and number of employees........................ 75
4.6 Restaurant demographics: ownership .................................................................... 76
4.7 Restaurant demographics: sales volume ................................................................ 76
4.8 Factor analysis for application usage..................................................................... 79
4.9 Factor analysis: MSA and Bartlett test of sphericity ............................................ 79
4.10 System application classification scheme.............................................................. 80
4.11 Differences among segments for strategic orientation........................................... 82
4.12 Application use percentages by segment ............................................................... 83
xiv
4.13 Number of applications used by each segment...................................................... 84
4.14 ANOVA for differences in application use among segments................................ 85
4.15 Tamhane post-hoc analysis for segment differences in application usage ............ 86
4.16 T-tests analysis for differences in application usage between chains and
independence restaurants ....................................................................................... 88
4.17 Tamhane post-hoc analysis for sales level differences in application usage ......... 89
4.18 Tamhane post-hoc analysis for success level differences in application usage..... 91
4.19 Cramer’s V for types of training and quality ratings ............................................. 93
4.20 Spearman’s Rho for training availability and quality ratings ................................ 95
4.21 Variable names for the RMIS model ..................................................................... 97
4.22 Descriptive statistics for RMIS variables .............................................................. 97
4.23 Correlation matrix for RMIS model’s factors and outcomes................................. 98
4.24 Correlations of factors and DSS ............................................................................ 99
4.25 Spearman’s Rho for segment and DSS................................................................ 100
4.26 Regression model summary................................................................................. 102
4.27 Adequacy of regression model............................................................................. 102
4.28 Regression coefficients ........................................................................................ 103
4.29 Collinearity statistics for regression equation...................................................... 104
4.30 Regression models ............................................................................................... 106
4.31 Comparison of significant coefficients for DSS and FIT .................................... 107
4.32 Means of quality ratings....................................................................................... 108
4.33 Ranking of quality ratings.................................................................................... 109
4.34 Comparison of means of segments and quality ratings ....................................... 110
xv
4.35 ANOVA of segments and system quality............................................................ 110
4.36 Tukey post hoc analysis of system quality differences........................................ 111
4.37 Test of homogeneity of variances ........................................................................ 113
4.38 Results of hypothesis testing – segments............................................................. 117
4.39 Results of hypothesis testing – training and support ........................................... 117
4.40 Results of hypothesis testing – independent variables and DSS.......................... 118
4.41 Results of hypothesis testing – regression equation ............................................ 119
4.42 Residual statistics for the regression equation ..................................................... 120
4.43 Correlations of r values........................................................................................ 123
xvi
LIST OF FIGURES
Figure
2.1 Evolution of restaurant technology........................................................................ 18
2.2 Evolutionary phase processes ................................................................................ 19
2.3 Stages of development of systems planning .......................................................... 21
2.4 Digital age stage..................................................................................................... 21
2.5 Strategic grid.......................................................................................................... 25
2.6 System fit ............................................................................................................... 28
2.7 System success model............................................................................................ 39
2.8 A model of factors contributing to RMIS success ................................................. 48
4.1 Sales volume by segment....................................................................................... 77
4.2 Financial success ratings........................................................................................ 78
4.3 Competitive ratings of IT....................................................................................... 81
4.4 Types of training .................................................................................................... 92
4.5 Availability of support ........................................................................................... 94
4.6 Regression equation ............................................................................................. 100
4.7 Plotting of residuals ............................................................................................. 121
4.8 Linear relationships of residuals .......................................................................... 122
4.9 Test for homoscedasticity .................................................................................... 122
1
CHAPTER 1
INTRODUCTION
As information technologies develop, organizations need computer information
systems that help them achieve their business, strategic, and competitive goals.
Accountants, a primary provider of business information, need to focus their attention on
providing information that adds competitive value to the users (Brecht and Martin, 1996).
In exploring the broad scope of accounting information systems, accountants should
explore the importance of providing information across strategic, managerial, and
operational boundaries.
Accountants have a history of experience in record keeping and data management.
Accountants have also played significant roles in the business by serving as external
auditors of financial statements and internal providers of information for operations.
Typical operational accounting functions include the tracking of sales, controlling
inventory, and processing payroll. Managerial decision-making is often supported by
budgets, forecasts, and variance analyses. Capital budgeting and spending plans provide
strategic support. In its customary state, however, the information provided to
organizations has become increasingly insufficient (Brecht & Martin, 1996).
This study focuses on the use of accounting information in the foodservice
industry. In the foodservice industry, the use of accounting information has been
2
primarily operational (Ellison & Mann, 2000). Based on a literature review, discussions
with foodservice experts, and survey research, this study investigates the use of
accounting and management information systems to improve restaurant operations and
provide competitive value to users.
The Foodservice Industry
The foodservice industry plays an important role in the economy of the United
States. It is a $407.8 billion industry generating 4% of the Gross National Product
(National Restaurant Association, 2002). The industry has experienced real sales growth
over the last nine years, and is projected at 3% in real terms over the next year. The
restaurant industry is also the largest non-governmental employer of individuals and
teenagers. Furthermore, the restaurant industry has become an integral part of the
American life with over half of all adults eating out one meal a day (National Restaurant
Association & Deloitte & Touche, 2000).
The National Restaurant Association (NRA) classifies restaurants into four main
groups: (1) full service (average check under $10), (2) full service (average check
between $10 and $25), (3) full service (average check more than $25) and (4) limited
service (fast food). The largest of these segments is limited service (also known as quick
service) comprising 48.7% of all eating establishments. Full-service restaurants make up
the second largest segment with 30.3% of the market (NRA, et. al., 2000). Quick service
restaurants are known for offering value for the dollar. Full-service restaurants serve as
places where customers can socialize with family and friends.
3
The foodservice industry is a dynamic industry. For example, the demographics
of its customer base are changing. The average age of adults will be 37.2 in 2010, versus
32.3 in 1990 (NRA et. al., 2000). The labor pool of teenagers has also been declining
over the last decade. A restaurant’s continued success is dependent on the effective
management of many factors: restaurant theme, brand recognition, site location,
ambiance, the competition, employee recruitment and retention, food quality, technology,
and service quality, to name a few.
Competitive Pressures
Restaurant operators face complex market forces as they compete for market
share. Porter’s (1985) five forces model of competition illustrates the dynamics of
competition. The competitive environment is made up of the rivalry among existing firms
contending with new entrants, substitutes, and the bargaining power of suppliers and
customers. As more consumers purchase meals away from home, rivals try to gain
market share as new players try to enter the market. For example, many supermarkets
have entered into the market by offering home replacement meals as substitutes to dining
out. Furthermore, some family and upscale restaurants have added separate entrances and
counters to serve carryout customers thereby becoming more competitive with rival quick
service restaurants.
The bargaining power of suppliers and customers also contribute to the
competition. The fewer the suppliers, the more control they can usually exert to
influence prices, terms, and quality (Porter, 1985). The customer serves as an additional
powerful market force. The industry’s product has a dual component – food and service.
4
If dissatisfied with either, customers can easily change from one restaurant to another.
Research has shown that causes of dissatisfaction are numerous ranging from the obvious
– such as poor food quality – to the not so obvious – such as customers not liking their
servers’ attitude (Chang & Hoffman, 1998). As a result of high mobility and low
switching costs, customers wield bargaining power in the Porter competitive forces
model.
Given the competitive nature of the industry, foodservice operators are
continually seeking ways to improve their sales and profitability. Controlling food and
labor costs often leads to increased profitability. Technology is also utilized as a means to
automating processes to improve efficiencies. Automation can occur in both the food
preparation processes and the decision-making processes. Foodservice equipment such as
ovens and fryers can be automated for cooking times, quantities, and temperatures.
Decision-making processes such as forecasting or ordering can be automated through the
use of information technology (IT).
As companies build competitive advantages in the new Digital Economy, firms
are relying on their IT departments to design, develop, and deploy on-line solutions
(International Quality and Productivity Center, 2001). Today companies are using IT to
connect to their stakeholders – customers, suppliers, employees, and management. Part of
the IT solution involves the use of accounting information to add competitive value to its
users. Since IT requires a high level of financial and human investment, research on MIS
can provide valuable insights to MIS directors. By surveying restaurant managers on
5
systems, this study aims to provide IT directors with information about the current state
of IT planning, implementation, and MIS usage in the foodservice industry.
Accounting and Management Information Systems
Information technology (IT) and management information systems (MIS) are
interchangeable terms (Kearns, 1997). They are a set of interrelated computerized
components that work together to collect, retrieve, process, store, and distribute
information for the purpose facilitating planning, control, coordination, analysis, and
decision-making in organizations (Laudon & Laudon, 1998). In the foodservice industry,
IT is commonly used for order processing, marketing, accounting, and site selection of
new restaurant units. Whether a small independent or part of a large chain, many
restaurants use established point-of-sale systems and accounting packages to process
financial data.
In the late 1990s, the industry entered a stage of IT proliferation with a growing
number of technologies available. Improved technologies such as increased bandwidth,
DSL lines, and satellite links are changing the way the industry is doing business. The
use of e-commerce, e-mail, intranet and extranet development is now common among
restaurant chains.
In terms of improving restaurant profitability, management accounting
information systems have been used to support managers in their decision-making
processes. Variances, budgets, and forecasts are typical accounting applications that help
managers run their restaurants more effectively and efficiently. The use of accounting
information, however, extends beyond these traditional applications to include non-
6
financial as well as financial reports that are presented in a timely manner in a variety of
ways and for a variety of purposes (Choe, 1998).
Restaurant Management Information Systems (RMIS) are systems that support
management in their decision-making processes including traditional financial reporting
as well as variance reporting and forecasting. RMIS have been used to improve store
performance in many ways such as:
• Automating manual processes
• Producing reports that support managerial decision-making
• Decreasing duplication of efforts through system integration
• Improving communications among managers, headquarters, and
restaurants
• Decreasing food delivery times
• Improving service quality
• Producing reports that aid managers in sales, labor, and food cost
management, and
• Producing forecasts that assist managers in ordering, planning food
production, and scheduling labor.
By effectively deploying RMIS at the operational level, firms might be able to
develop and leverage their unique operational resources and capabilities (Zhang & Lado,
2001).
Statement of Problem
The research in this study focuses on the use of RMIS where operational
efficiencies can best be achieved through the effective use of IT. General IT research
tends to focus on the executive level rather than the operational level. In the foodservice
7
industry, however, profits are made or lost at the restaurants. With the average profit
margins of 5%, pennies saved at the store level have a significant impact on the bottom
line (NRA, 2000).
This research also addresses IT issues specific to the foodservice industry. Prior
foodservice IT research has been limited to descriptive statistics and case studies that
focus on current IT usage trends. Descriptive research and antidotal work do not test the
interrelationships among variables. Many popular business articles, including those in
foodservice, seem to imply that all organizations should adopt the latest technology.
Contingency theory, however, suggests that no universal information system is applicable
to all organizations in all circumstances (Otley, 1980). On the other hand, contingency
theory has been criticized for lacking a substantive basis to suggest which variables are
important (Fisher, 1995; Otley, 1980).
This study expands the current foodservice IT research base by including the
empirical testing of models and interrelationships among variables regarding the
managerial use of systems. This study also suggests which contingent variables are the
most significant in the development of effective RMIS.
Research Approach
This study uses survey research. A survey was administered to restaurant
managers to gather data regarding system features and effectiveness. The frame for the
study included the members of the Central Ohio Restaurant Association as well as all the
other restaurants in Columbus, OH.
8
The research objectives for this study are:
1) To describe the characteristics of the foodservice managers and restaurants in this
study;
2) To describe current IT trends in the foodservice industry and to investigate the
contingent nature of IT use;
3) To identify the current level and quality of system training and support provided
to restaurant managers;
4) To describe the relationships between the variables in the “RMIS” model;
5) To determine how system characteristics (number of applications offered, system
quality, and report quality), manager attributes (user proficiency), implementation
characteristics (quality of training and support), and organizational characteristics
(segment) impact restaurant managerial decision-making support satisfaction
ratings; and
6) To assess the IT strengths and weaknesses and summarize recommendations
made by mangers to improve IT.
Significance of the Study
Foodservice specific research addresses issues of academic and professional
interest. Both hospitality academicians and practitioners ranked IT as one of the top
“five” research areas needed in hospitality (Cobanoglu, 2001). In their critique of
academic hospitality literature, Kirk and Pine (1998) called for more IT foodservice
research that is of a planning or strategic nature. DeLone and McLean (1992), developers
of the model of system success, also recommended more empirical research testing of
conceptual models and interrelationships among variables. As a result of the lack of
research, strategic opportunities to improve organizational performance have been missed
by companies (Bakos & Tracey, 1986). This study tests interrelationships among
variables in order to suggest ways to improve organizational performance.
9
In the foodservice industry, firms possess varying viewpoints of the importance of
RMIS. Some companies view systems as a low cost provider of data whereas other firms
view them as highly strategic (Huber and Pilmanis, 2001). No matter what the strategic
orientation, the primary goal of an effective system is to meet the needs of its users, or in
this case, the restaurant managers.
The results of this study provide clear guidelines useful to practitioners in
developing RMIS for more effective restaurant management. This study collects
information directly from the end users, the restaurant managers. Overall quality and fit
of systems are evaluated for the various industry segments. Information gathered on
training and support can be used in developing training programs. The detail of the study,
the distinctive combination of variables, and specificity to foodservice, make this study
unique.
Limitations of the Study
This study has its limitations. Due to the nature of the survey and confidentiality
concerns, many foodservice chains and managers were hesitant to provide data. To
encourage a level of trust, the researcher used her residential market of Central Ohio as
the frame for this study. With the support of the Central Ohio Restaurant Association
(CORA), the researcher mailed surveys to all CORA members and general managers in
the Columbus, OH area.
The sample is a purposive sample. It includes restaurants from all sectors:
(independents, small chains, and national chains) and segments: (casual dining, family,
and quick-service). Central Ohio has also been used as a test market for many firms in the
past due to its demographics. The major drawback with this type of sample frame,
10
however, is research bias. The researcher may or may not be correct in the estimation of
the representative ness of the sample.
Outline of Dissertation
This report consists of five chapters:
Chapter 1 provides a discussion of the competitive nature of the industry, RMIS
usage in the foodservice industry, purpose and significance of this study, the research
objectives, and the explanation of terms.
Chapter 2 provides a review of literature relevant to the study of information
systems. Conceptual and empirical research is presented to provide a foundation for the
research questions presented in this chapter. Studies directly related to the topics of
accounting and management information systems, strategic management, and foodservice
management are discussed in the literature review.
Chapter 3 describes the research methodology including a description of the
research design, sample, instrument, and procedures followed. Validity and reliability of
the survey instrument are discussed. Survey implementation as well as non-response rates
are discussed. Justification of the statistical techniques is given.
Chapter 4 presents survey results in the narrative form and in tables including
descriptive, correlational, and multivariate statistics. Findings for each research question
are discussed and interpreted.
Chapter 5 presents conclusions regarding the study’s contribution to the
foodservice field, limitations of this study, and suggestions for further research.
11
Explanation of Terms
Following is a list of definitions for terms utilized throughout this dissertation:
Accounting Information Systems: specialized subset of MIS with the purpose of
collecting, processing, and reporting information related to financial transactions
(Gelinas, Sutton, & Oram, 1999).
Casual Dining: full-service segment with table and bar service with an average check
greater then $10 (i.e. Outback, P.F. Changs, and Cameron Mitchell restaurants).
Competitive Advantage: occurs when a firm experiences above-normal returns and
implements a value-creating strategy not implemented by numerous other firms in that
market or industry (Barney, 1997).
Competitive Value: specific, critical leverage points where a firm can use IT to
enhance its competitive position (Laudon & Laudon, 2000).
Decision-support systems: the coupling of the intellectual resources of individuals
and the computer to improve the quality of decisions (Turban and Aronson, 1998).
Family Dining: full-service segment with table service, but no bar service and an
average guest check less than $10 (i.e. Big Boy, Dennys, and Bob Evans).
Information Technology (IT) or Management Information System (MIS): terms used
interchangeably defined as a set of interrelated components working together to collect,
retrieve, process, store, and distribute information for the purpose of facilitating planning,
control, coordination, analysis, and decision making in organizations (Laudon & Laudon,
1998).
Quick Service: segment with no table service, sometimes drive-thru service, with an
average guest check of $5.00 (i.e. Mc Donald’s, Wendy’s, and White Castle)
12
Restaurant or Foodservice Industry: all establishments where food is regularly served
outside of the home. Such establishments include formal restaurants, hotel or motel and
department store restaurants, coffee shops, family restaurants, specialty and ethnic
restaurants, and fast food establishments (Payne-Palacio & Theis, 1997).
Restaurant Management Information Systems (RMIS): systems that support
management in their decision-making processes including traditional financial reporting
as well as variance reporting and forecasting (Choe, 1998).
System Competitiveness: ability of a system to give competitive value.
System Fit: the level of agreement between user information needs and the computer
applications offered.
System Effectiveness or Success: the effectiveness of a system to communicate
information at a technical level (accurately and efficiently), semantic level (clarity of
conveying the intended meaning), and effectiveness level (influence of information on
the receiver) (DeLone & McLean, 1992).
System Utilization: level of use reported by restaurant managers of system
applications.
User Satisfaction: rating by users of their interaction with information systems
(Delone & McLean, 1992).
Value Chain: a sequence of activities performed by an organization that adds value
or utility to the product produced or service provided (Gelinas, Sutton, & Oram, 1999).
13
CHAPTER 2
LITERATURE REVIEW
The last three decades have seen a proliferation of studies related to the IS
planning and the strategic use of MIS. Attempts have been made to develop theories of
strategic planning and to define dependent variables such as system success. Many
studies have been conceptual and antidotal (Kearns, 1997). One recent study, published
in the Cornell Hotel Restaurant Quarterly, was highly prescriptive in nature. In this
article, Ansel and Dyer (1999) developed a framework for the development of IS in
foodservice, but without presenting a theoretical basis. This study presents a theoretical
framework that was tested with empirical data regarding the use of RMIS in the
foodservice industry.
This chapter is organized into eight sections. The first section presents an
overview the literature related to historical tradition of IS strategic thought. The second
section reviews the foodservice literature in IS. The third section reviews IS literature as
it relates systems planning, and fourth to implementation. The fifth section discusses user
characteristics and competence, and the sixth section reviews the strategic management
literature as it relates competitive advantage. The seventh section discusses the literature
that relates to defining the dependent variable: system success. The final section
summarizes the research objectives and hypotheses for this study.
14
Historical Development of Strategic Thought in IS
Reviewing the historical tradition of strategic systems can aid in developing a
theoretical framework for the foodservice industry. Leavitt (1965) was one of the first
recognized writers to view technology as a critical part of industrial organizations. He
identified four variables – structure, people, task, and technology – that interacted together
on a regular basis.
Next came the recognition of the development of IS. Gibson and Nolan (1974)
introduced the “stages theory” by identifying four stages of IT development within firms –
initiation, expansion, formalization, and maturity – and matched them with respective
human resource and managerial issues.
Alignment first became an issue when Rockart (1979), in defining the strategic role
of IS in an organization, developed the “critical success factor” approach to systems
development. Successful systems are those aligned with the “critical success factors” or the
information needs of the chief executives within the firm (Rockart, 1979).
The 1980’s saw the integration of Porter’s “five forces” model into the literature
base. Porter and Millar (1985) explained how technology transforms the value chain and
changes the nature of competition. The value chain is a company’s system of
interdependent activities (inbound logistics, operations, outbound logistics, marketing
and sales, and service) that are connected by linkages. Linkages are the relationships
among the activities in the value chain. Technology can transform the value chain by
lowering costs, enhancing differentiation, changing competitive scope, and spawning new
businesses (Porter, 1985).
15
Cash and Konsynski (1985) further explored this notion that systems yield
competitive advantage. They looked at inter-organizational uses of IS and purported that
competitive boundaries can change due to the inter-firm adoption of technology.
Other researchers began to examine management’s role in system success.
Leonard-Barton and Kraus (1985) developed a framework for IT implementation
regarding personnel. If implementation is to succeed, the implementation team must
include four individuals: (1) a sponsor, usually a fairly high-level person who supports
the project internally with financial, human resource, and political support; (2) a
champion, who is salesperson, diplomat, and problem solver for the project; (3) a project
manager, who oversees administrative details; and (4) an integrator, a communicator that
manages conflicting priorities.
The literature base of the late 1980’s and 90’s focused on the role of information
and its use to provide competitive advantage. Technological advances, such as increased
bandwidth, networks, and the Internet, changed the ways of doing business. Drucker
(1988, p.45) predicted the flattening of firms into “knowledge-based” organizations
where companies are “composed largely of specialists who direct and discipline their
own performance through organized feedback from colleagues, customers, and
headquarters.” According to Evans and Wurster (1995), the new economics of
information deconstructs the traditional value chain by unbundling it. This unbundling
may cause “incumbents to be victims of their own obsolete physical infrastructures and
their own psychology” (Evans & Wurster, 1995, p. 82).
More recently in the 1990s, resource-based theorists have written that IT in itself
cannot provide a sustainable competitive advantage unless it is bundled with company
16
resources such as organizational, financial, human, or physical capital (Keen, 1993;
Powell & Dent-Micallef, 1997). If fused with a resource, then IT becomes more difficult
to imitate, thereby providing sustainable competitive advantage (Mata, Fuerst, & Barney,
1995).
The following ideas emerged from the historical development of strategic systems
thought: (1) technology is embedded in organizational structure, (2) systems are
developed in stages (3) systems should be aligned with user information needs, (3)
technology can transform, as well as deconstruct the value chain, (4) personnel roles are
vital to IS implementation, (5) the information age is changing the dynamics of doing
business, and (6) IT affects competition.
Evolution of IS Usage in the Foodservice Industry
Information technology can enable managers to make tactical, operational, and
strategic decisions. According to Brian Sill, a foodservice management consultant, all
stages of the restaurant production and service chain must act in concert to deliver quality
products at the right prices to the right guests at the right times (Collins & Malik, 1998).
Restaurant technology can monitor and coordinate these activities in timely and focused
manner. Failure to do so can result in excess inventory, poor food and service quality,
underutilized capacity, and excess costs (Collins & Malik, 1998).
The most common use of systems is the use of point-of-sale system (POS). POS
applications eliminate arithmetic errors, improve guest check control, increase average
guest check, reduce labor costs, improve reaction to trends, reduce credit card expenses,
and reduce undercharges (Collins & Malik, 1998). Colins and Malik (1998) theorize that
17
these applications improve profitability. Firms that do not fully utilize the applications
available to them are losing opportunities to increase their profitability as shown in
Figure 2.1 (p. 18).
Indeed some companies have seen improved profitability by using IT. For
example, Donatos, a Midwestern pizza company, reported that it saved over $1 million in
dough costs by implementing a dough forecasting application into its systems in 1999
(Huber & Pilmanis, 2001). The lower cost of Internet technology and increased
bandwidth also allowed Donatos to set up Intranets where information was distributed to
all managers at a fast and efficient rate (Huber & Pilmanis, 2001).
The level of use of information systems in the foodservice industry is diverse, and
has been classified into one of three phases. Gamble (1994) proposed three phases of IT
adoption: (1) clerical, (2) integrated administrative, and (3) tactical (Figure 2.2, p. 19).
Ellison and Mann (2000) classified clerical processes as the use of IT to replace
manual processes such as purchasing, inventory control, production, sales, marketing,
menu planning, employee scheduling, payroll, and financial reporting. They defined the
integrated administrative processes as IT linkages between and among processes within
the foodservice operation as well as to external departments including reservation
systems linked with food service production, point of sales and production scheduling,
equipment interfaces, purchasing linked with suppliers, nutrient analysis, internal and
external e-mail, and transfer of operating data via intranet systems to corporate offices.
They also defined tactical processes as Internet use of information systems for market
information, marketing and purchasing analysis, supply chain management, data
warehousing and /or mining, and the use of systems for forecasting.
18
Figure 2.1: Evolution of restaurant technology (Adopted from Collins and Malik,
1996).
Financial Accounting Statements
Cost Ratios
POS Applications
Sales and Production Forecasting
Labor Scheduling
Intranets/Extranets
Data Mining
Service Management
Ideal Labor and Food Management
Reservations/Capacity Management
Yield Management
1980 1985 1990 1995 2000
Lost
Opportunity
3%
6%
9%
12%
15%
% of Sales
19
Clerical Processes Integrated Administrative
Processes
Tactical Processes
Purchasing Suppliers and Purchasing Internet information systems
Inventory Purchasing and inventory or production Data warehousing/Data mining
Production Reservations/Production Menu or sales mix analysis
Recipes/Menus Nutrient analysis and menus EFR/EDI/Product analysis
Service/Distribution Recipes/menus and production Productivity analysis
Point of Sale Production/Distribution and Human resources Forecasting
Marketing/Promotion All areas of production and financial Financial analysis/simulation
Human Resources All areas of production and financial
Financial E-mail (internal and external)
Figure 2.2: Evolutionary phase processes (Adapted from Ellison & Mann, 2000 and
Gamble, 1989 and 1984).
The use of technology is diverse in the foodservice industry: from minimal to
extensive. Survey research in hospitality has shown that most restaurant companies score
highest in their usage in the clerical arena; integrated usage is lower; and tactical usage is
lowest (Whitaker, 1986; Ellison & Mann, 2000). These findings support the notion that
most restaurants use technology as a data processing tool to process sales and accounting
data; integration with other processes such as ordering from vendors is limited; and
strategic use of systems is underutilized.
Chien, Hsu, and Huss (1998) conducted a study of independent restaurants in
Iowa (n=144), and reported similar results to those of Ellison and Mann (2000). They
found that the most highly utilized software packages were office products (word
processing and spreadsheets) and accounting packages (over 80%). Less than half of the
20
independents used POS systems, time and attendance systems, and recipe costing. Even
fewer operators used more advanced applications such as employee scheduling (28.9%)
or food production forecasting (21.1%).
Based on the discussion of IT usage in the foodservice industry, this study
examines current IT usage trends in the foodservice industry. The study examines usage
by design (sales, food cost control, labor, financial reporting, service quality, office, and
communication software), for various segments (casual dining, family, and quick
service), and by type of ownership (independent or chain). The following hypotheses are
examined in this study:
H1a: Casual dining, family, and quick-service restaurants use systems differently
to best meet specific industry needs.
H1b: Chains will utilize more software applications than independently owned
restaurants.
H1c: There is a positive relationship between sales volume and application use.
H1d: There is a positive relationship between perceived level of financial success
and the number of computer applications used by restaurant managers.
Systems Planning
The planning phase of systems development is important in developing successful
systems (McFarlan, 1981; Ledererer & Salmela, 1996). King (1988) developed a
typology of information systems planning development in Figure 2.3 (p. 21). The threestage
evolution includes systems planning, strategic IS planning, and the information age
which are illustrated with their respective characteristics, issues, and planning styles. The
model is adept at classifying the stages of IT development through 1990.
21
Stage Characteristics Major Issues Planning Style
Systems Planning Explosive use of EDP
Resource Constraints
System integration
System justification/prioritization
Human resource management
Data security/integrity
Tactical
Bottom-up
Project Management
Strategic IS Planning Involvement of top management
Involvement of users
Linkage with business strategy
Accessing top management
Analyzing user requirements
Defining information architecture
Identifying critical success factors
Top-down with
Bottom-up
implementation
Information Age IS used for competitive advantage
Decentralization of IS
Telecommunications
Identifying IS opportunities
Integrating with telecommunications
Environmental scanning
Aligning with organizational structure
Integrated
Figure 2.3: Stages of development of systems planning (Adapted from King, 1998).
Concepts in this model related to this literature review include involvement of top
management and users, linkages with business strategy, the identifying critical success
factors, and using IS for competitive advantage.
The late 1990s and 2000s, brought the advent of a new stage. This study models
the Digital Age stage in Figure 2.4:
Stage Characteristics Major Issues Planning Style
Digital Age Participation of all key
personnel
IS used for competitive parity
Strategic use of systems
Outsourcing of IS
Telecommunications
Identifying IS opportunities
Integrating with improved
telecommunications
Defining role of application
service providers (ASPs)
Integrated
Outsourced
Figure 2.4: Digital age stage
22
This updated stage summarizes the recent trends such as improved
telecommunications and the movement to Application Service Providers (ASPs). Rather
than purchasing software and some hardware, firms lease applications and equipment
from service providers. These providers own the central processing equipment and
software. As a result, IT complexity is moved out of the firms and into the ASPs that
manage upgrades and hardware. The end result of using ASPs are: capital investment is
limited, fewer IT corporate personnel are needed, smaller firms get big company tools and
centralized databases, and firms gain access to the latest technology (Oden, 2000).
Additional important characteristics of the Digital Age involve the participation of
all key personnel, the strategic use of systems, and the idea that systems yield competitive
parity rather than competitive advantage (discussed later in this literature review).
Planning Success
Due to the significance of IS planning; there have been numerous attempts to
develop methodologies to assist professionals in planning IS. These methods have
focused on the topics of alignment (Horovitz, 1984; King, 1988; Reich & Benbasat,
1998), the planning function and the role of user participation (McKeen & Guimaraes,
1994; Teo, Ang, & Pavri, 1997, Reich & Banbasat, 1998; Lederer & Sethi, 1996; Earl,
1993; Thong, Yap, & Raman, 1996), and the utilization of systems for competitive
advantage (Porter & Millar, 1985; McFarlan, 1984; Powell & Dent-Micallef, 1997).
Participation between IS personnel and users, however, seems to be the key to
effective IT development. Participation has also been described as collaboration,
partnering, or social alignment (Bruce, 1998; Ward & Peppard, 1996; Teo & Ang, 1999;
Reich & Benbassat, 1998). IS literature supports the contention that communication with
23
users enhances system performance (Rockart, 1979; Munro, et. al., 1980; Teo, et. al.,
1997, Lederer & Salmela, 1996; Segars et. al., 1998); planning efforts with top
management and end-user support alignment (Teo, Ang, & Pavri, 1997, Reich &
Banbassat, 1998; Lederer & Salmela, 1996; Earl, 1993; Thong, et. al., 1996); and
collaboration among departments is positively correlated to IS planning success
(Lawrence & Lorsh, 1967; Sabherval, 1999). Interestingly, Jarvenpaa and Ives (1991)
conducted research specific to executive participation, and found that executive
involvement (a psychological state) was more strongly correlated to IT success than actual
executive participation (the act of participating).
Reich and Banbassat (1998) developed a model of “social” alignment to capture
the IS planning process. They defined social alignment as the level of mutual
understanding between IT managers and the system’s users. They further defined the
antecedents to social alignment as communication, planning, and shared domain
knowledge. Shared domain knowledge is the ability of IT developers and end-users, at a
deep level, to understand and participate in others’ key processes and to respect each
other’s unique contribution and challenges (Reich, et. al., 1998).
Not only are the IT department’s understanding of business (Teo & Ang, 1999)
related to planning success, but also their technical abilities (Kwon & Zmud, 1987).
Technical skills to build bridges between old and new systems, recognize opportunities
and apply new technologies, and convert inputs into effective outputs of IT personnel
determines how effective the system is (Ross, Beath, & Goodhue, 1996). In fact, Mata,
et. al. (1995) cites IT managerial skills as the only significant source of competitive
advantage. IT managerial skills include management’s ability to conceive, develop, and
24
exploit IT applications to support and enhance other business functions (Mata. et. al.
1995). In addition, the ability to develop long-term IT interests has been deemed as an
important planning function by some researchers (Feeny, et. al., 1998). The process of
environmental scanning, the process by which the IT department copes with emerging
technologies, has been also recently found to be a significant antecedent to planning
success (Maier, Rainer, & Snyder, 1997; Teo & Ang, 1999)
Strategic Orientation
The strategic orientation of a firm seems to have a significant influence on the
planning success. Researchers have found that firms have different strategic orientations.
Some firms might use IT to respond to external threats to maintain competitive parity
whereas other firms might view IT as a means to achieving competitive advantage. Miles
and Snow (1978) recognized four viable strategic orientations: prospector, defender,
analyzer, and reactor and defined them as:
• Defenders include firms with conservative competitive strategies that engage
in little or no product development. They have a tendency toward operational
efficiency.
• Prospectors include firms with aggressive competitive strategies that pioneer
in product and market effectiveness.
• Analyzers include firms with moderate competitive strategies. They tend to
blend aspects of the Prospectors and Defenders into a single strategy of
efficiency and effectiveness.
• Reactors are organizations with no distinctive competitive strategy. They
make decisions at random and in response to their external environment.
Tavakolian (1989) used the Miles and Snow (M & S) classification scheme and
found that IT structure is strongly related to strategic orientation. Thomas, Litschert, and
Ramaswamy (1991) also used M & S, and found that different CEO profiles are
25
associated with different strategic orientations and have performance implications. Snow
and Hrebiniak (1980) found that firms with different strategic orientations developed
different competencies. For example, they found analyzers to be the strongest in relation
to competitive advantage.
Another approach to defining strategic orientation was developed by McFarlan
(1984). McFarlan developed a strategic grid and placed firms in one of four IS
environments. McFarlan’s strategic grid, as extended by Ward (1987), is presented in
Figure 2.5 (p. 25).
Figure 2.5: Strategic grid (Adopted from McFarlan (1984) and Ward (1987).
The grid position and attributes are:
• Strategic – systems area critical to achieving future success and are the basis
for current competition.
• High Potential – systems that are largely experimental and have only future
possible benefits.
• Key Operational – systems that provide efficiency and are critical to current
success. Includes traditional applications such as order processing or
inventory management.
High Potential Strategic
Key Operational Support
Strategic Impact of Existing Systems
Strategic Impact
Of Future
Applications
High Low
Low
High
26
• Support – systems that are not critical to current operations but are valuable
for other reasons, such as they provide benefits to an office or work group.
These packages are less critical and may be achieved by use of packages, or
outsourced services.
Raghunathan and Raghunathan (1990) empirically tested the McFarlan grid (n =
187) to investigate the contingent nature of planning. They found support for the strategic
grid framework and differences in planning aspects among firms. Whyte, Bytheway, and
Edwards (1997) researched the system attributes success in relation to the three grid
positions of strategic, key operational, and support. They found strong relationships
between strategic orientation and certain service attributes. “Strategic” systems were
integrated, user-friendly, and provided good reports. “Support” systems, which are less
critical and often outsourced, were rated as less reliable and competent.
Other grids have since been developed with Ward and Peppard (1996) developing
a typology which classifies IT departments. Those relationship types are: (1) financial –
emphasis is on IT serving as a profit center – where value exceeds costs, (2) contractual –
IT is a business service that should operate in a cost effective way, (3) organizational –
the value of IT is embedded in unique ways into chosen areas of business competence, or
as s means of delivering the integration other competencies, and (4) Intimate – IT is an
integral support for business competencies, and a partner with aspects of business
strategy in innovating and creating strategic IT applications.
The typologies discussed are similar in definition, but with different names for the
classification schemes. In 1997, Maier, Rainer, and Snyder used a resource-based
typology and classified firms as: exploiters/innovators, competitors/early adoptee, and
participants/effective efficient followers. In the foodservice industry, the Computer
27
Sciences Corporation (CSC) (2001) used the classification topology of leaders, quick
followers, late adopters, and non-adopters.
Whatever the typology, as described earlier, most researchers have found
relationships between strategic orientation and system success (Zahra & Covin, 1993;
Whyte, Bytheway, & Edwards, 1997). This study asks managers questions about the
strategic orientation of their firm’s IT: industry leader, close follower, middle of the
pack, somewhat behind, or laggard. These orientations are then compared to system
success.
Managerial Fit
It is unlikely that two firms investing in the same amount in IT will experience the
same performance benefits (Weill, 1992). As previously discussed, participation and IT
departmental technical skills are important factors related to planning success. One
common approach to promote user participation is the “critical success factor” (CSF)
approach to systems planning. Rockart (1979) wrote of this approach as a way to design
systems to meet executive needs. This approach involved two or three interviews with
key executives to determine their key information needs, or critical success factors.
Systems were then designed to support the CSFs. Common CSFs for the foodservice
industry might be cost reduction, sales growth, increased market presence, and improved
food and service quality (Huber & Pilmanis, 2001).
The CSF approach has been historically successful in developing systems for
senior and middle level managers (Munro & Wheeler, 1980 ). Munro and Wheeler
(1980) investigated the CSF approach by conducting a field study. They found that the
28
process of determining MIS requirements consists of understanding a business unit’s
objectives; identifying critical success factors, specific performance measures, and data
required to measure performance. Henderson, et. al., (1987) extended CSF theory by
providing a strategic framework to address priority needs and to include decisionoriented
analysis. The extended methodology addressed the full range of management
support systems and alignment to the strategic planning effort. First, the CSFs were
subordinate to the company’s business strategy. Second, the strategic data became part of
the executive support systems, management information systems, and decision support
systems. System needs deemed most important were those that enhanced both decisionmaking
process and led to the successful attainment of the CSFs. This leads to system
“alignment” or “fit” to user needs. Figure 2.6 is a model of managerial “system fit.”
Figure 2.6: System fit
Foodservice Planning and Development
In the foodservice industry, systems planning can occur in a variety of ways.
Some organizations have IT departments and develop all their software in-house whereas
Managerial Needs
Computer Applications
Offered
Fit
Level of agreement between
managerial needs and
computer applications
29
others may outsource all or some of their IT function. Software packages can be
developed in-house (proprietary), purchased from a vendor with varying levels of
customization, or leased from an application service provider (ASP). According to a pilot
study (n = 23) conducted in 2001, software development had been evenly distributed
among proprietary, off-the-shelf, ASPs, or a combination thereof (Huber, 2002).
The use of ASPs is a current trend in software development in the foodservice
industry (Foodservice Technology Conference (FS TEC), 2000). An industry panel of
CEOs and CFOs (from Morton’s of Chicago, Red Robin Int’l, Fresh Concepts, and Texas
Land & Cattle Steak House) supported the idea of utilizing ASPs. “If you’re a CIO or
head of business, you won’t be buying computers anymore. You won’t buy software
either. You’ll rent all your resources from a service provider. Foodservice should
concentrate on core business: operations, customers, and quality control. Systems should
be easy to use and ubiquitous as your phone or cable television (FS TEC, 2000).” David
Oden (2000), CFO at Texas Land and Cattle, found that ASPs provided cost savings from
integration, reduced home office analysis time, and increased the number of stores as
well as the ability of area directors to manage stores. As Jim McCloskey stated, CFO of
Red Robin International, “we are not in technology business (FS TEC, 2000).”
The use of ASPs moves technology ownership outside of the corporation, but that
does not change the level at which is used. ASPs provide tactical, operational, and
strategic uses of IT also. Tactical reporting would include daily store polling with a
drilldown viewer and report builder. Operational applications would integrate alerts and
payroll to the POS system. A strategic application would provide key operating data
30
anywhere, anytime, and sorted as desired by the user giving product mix and sales
analysis by store, region, hour, and category.
Use of an ASP, however, would affect participation in the planning process.
Participation would be limited to the selection of the ASP and the ability of users to
customize the software. A pilot study of the foodservice firms reported high crossdepartmental
participation process (73%) in the software development process (Huber,
2002). The industry appears to recognize the importance of participation in the planning
process.
The following ideas emerge from the IS planning literature base: (1) planning has
been an evolutionary process among firms, (2) participation among stakeholders –
developers, users, and top management – are key to planning success, (3) the CSF
approach to designing systems encourages collaboration between IT developers and users
to promote system fit, and (4) cross-departmental participation is recognized in the
foodservice industry as a key to planning success.
Since the focus of this study is on restaurant managers, the research questions
concerning planning are limited by the lack of manager participation in the planning
process with the exception of owner/managers. Managers are asked, however, the type of
package they use (proprietary, off-the-shelf, or ASPs) and asked questions about system
quality and information quality. System and information quality is one aspect of user
satisfaction, a dependent variable in this study, which is discussed later in the literature
review.
31
Systems Implementation
Researchers not only emphasize the importance of the planning process, but also
stress the importance of effective implementation (Lederer & Sethi, 1996; Munro, et. al.,
1980). In fact, implementation can be a source of enormous gain when performed
effectively (Chew, Leonard-Barton, & Bohn, 1991). End-user participation,
communication, perceived usefulness, and perceived use of the system have been
hypothesized to relate to the successful implementation of IS (Gatian, Brown, Hicks
1995; Barki & Hartwick, 1989; Davis, 1989). End-user acceptance or the willingness to
use the system has also been closely tied to user satisfaction (Kim & Lee, 1986). Top
management support, end-user motivation, and the communication skills of the training
personnel were found to be critical success factors related to end-user training success
(Lee, et. al., 1995).
Designer-user interaction correlates with conversion effectiveness (Kwon &
Zmud, 1987). The IT staff’s credibility with top management and integrity also appears
to be an antecedent to implementation success (Teo & Ang, 1999; Davis, Schoorman,
Mayer, & Tan, 2000). Credibility is developed through positive interactions between IT
personnel and users by providing reliable services, fulfilling commitments and matching
deliveries with promises; being responsive to user needs, and coming up with creative
ideas on using IT strategically (Bashein, et. al., 1997; Feeny & Willcocks, 1997; Teo &
Ang, 1999.).
Training and Support
Most studies support the hypothesis that training and support are associated with
system success. Magal, Carr, and Watson (1988) found that support during and after
32
implementation as well as training are both related to system success (Magal, Carr, &
Watson, 1988). Rivard & Huff (1984) found that five predictors of IS support success:
user independence, satisfaction with the support center set-up, user friendliness, user
attitude, and satisfaction were related with the degree of support. Magal (1991) reported
similar results: the quality of the support center, the quality of computer applications, and
the degree of user self-sufficiency were related to system success.
One empirical study, conducted by Delone (1988), reported conflicting results.
Delone found that employee training was not associated with system success. On the other
hand, Delone (1988) did find that executive computer training was positively associated
with system success. These findings were limited by a small sample size (n = 23).
Another study, with a large sample size (n = 478), did find that internal support (from
within) and external support (from vendors) were associated with user satisfaction
(Igbaria, Zinatelli, & Cavaye, 1998).
Other studies have also found vendor support associated with system success.
Thong, Yap, and Raman (1994) examined IS implementation in small businesses and
found the vendor support role to be significant as it relates with system success. Thong,
Yap, and Raman (1994) developed a measure of vendor support and validated a measure
consisting of six items: (1) adequacy of vendor support during implementation, (2)
adequacy of support after implementation, (3) quality of technical support, (4) adequacy
of training provided, (5) quality of training provided, and (6) the relationship between
vendor and the firm.
The importance of training and support is evident in most studies. The reality of
firm support of training and support is a different issue. Lee, et. al. (1995) found that the
33
majority of the firms in their sample did not consider training and support a priority. If not
properly funded, Tait and Vessey (1998) found the resource constraints of time and money
have had a significant negative effect on successful system implementation.
Based on this discussion of training and support issues, restaurant managers are
asked about the types of IT training they received (in-house, on-the-job, seminars, and
videos) and the level of support (none, during regular business hours, 24/7). Furthermore,
managers asked to rate the quality of the support they received and to comment on any
deficiencies.
The following hypotheses are examined in regards to training and support:
H2a: There is a positive relationship between types of training and training
quality ratings.
H2b: There is a positive relationship between the level of the “help desk” support
and support quality ratings.
User Attributes and Competence
User attributes are described by DeLone and McLean (1992) as possible
extraneous variables. Education, gender, age, and work experience are all types of user
attributes. These user attributes are included in the study so to rule out the effects of the
extraneous variables.
The construct, user competence, is a relatively new construct. Training programs
usually focus on improving user technical abilities. Kim, et. al. (1986) did not find a
strong relationship between user abilities and end-user satisfaction. On the other hand,
Munro, Huff, Marcolin, and Compeau (1997) did find that user competence correlated
with user satisfaction. According to Munro, et. al. (1997) user competence consists of
three dimensions: breadth, depth, and finesse. Breadth refers to users’ skills and
34
knowledge. Breadth is broad if the user can use many applications, and narrow if the user
can only use, for example, one application. Depth represents the completeness of the
end-users’ computing capability and refers to the mastery of the features and functions of
applications. Finesse refers to the ability to creatively apply end-user computing.
To control for extraneous variables related to the user, restaurant managers are
asked questions regarding various attributes such as age, education, and years of
experience. User competence levels are also assessed
The Strategic Use of Systems
The strategic use of systems is examined in the context of strategic management
theory. Strategic management often refers to a firm’s ability to align itself with the forces
that drive its environment (Olsen & Roper, 1998). Porter (1980) developed the five
forces model of competition. The competitive environment is made up of five attributes:
(1) the rivalry among firms contending, (2) the threat of new entrants, (3) the threat of
substitutes, (4) the bargaining power of suppliers, and (5) the bargaining power of
customers. Competition advantage, according to Porter (1980), can be primarily
achieved in two ways:
• cost leadership – firms earn above-average profits because their costs are
lower than rivals and
• product differentiation – value is added to the product in areas considered
significant to the customer such as in features or service.
The resource-based school of strategic theory offers an alternative view of
strategic management. Although firms must react to external threats, capitalizing on firm
resources and avoiding or fixing weaknesses is what primarily yields competitive
35
advantage (Zhang & Lado, 2001). “Strategy is a pattern of resource allocation that
enables firms to maintain or improve their performance” (Barney, 1996, p. 27).
Firm resources are defined as (1) financial capital – money a firm can use to
implement strategies, (2) physical capital – physical technology in the firm, (3) human
capital – skills, experience, and relationships among firm employees, and (4)
organizational capital – the sum collection of the firm including the firm’s reporting
structure, planning, controlling and coordinating systems, culture and reputation, and
informal relationships (Barney, 1996).
The resource-based literature sets the criteria for competitive advantage as
“sustainable.” Since IT is easily imitated and readily available to all firms in
competitive-factor markets (Clemons & Row, 1991), IT does not lead to sustainable
competitive advantage in most cases. In fact, most empirical studies support the lack of
competitive sustainability. Competitive parity is re-established after a short period of time
in regards to technology implementation (Feeny & Ives, 1990; Kearns, 1997; Powell &
Dent-Micallef, 1997). Therefore, the ability of IT to generate a sustained competitive
advantage depends on the strategy being rare and costly to imitate, either through direct
duplication or substitution (Barney, 1996; Feeny & Ives, 1990).
Competitive advantage, according to Rumelt (1984), occurs when one of the
following imitation barriers are erected:
• time compression diseconomies – resources are developed over time where
knowledge is firm specific and not easily transferable among firms,
• first-mover advantages – resources create reputation, standardization, and
customer-switching costs that can not be overcome by new entrants,
36
• embeddedness of resources – resources are inextricably linked to
complementary resources in a firm, and
• causal ambiguity – resources are costly to imitate since imitating firms may
not understand the relationships between resources and capabilities.
Given these potential barriers to imitation, can IT become a rare, non-imitatable
resource? Or is it possible that IT, when “bundled” with other rare resources, becomes
unique, thereby creating sustainable competitive advantage? In fact, can certain
information systems become a core competence of a firm?
Powell and Dent-Micallef (1997) conducted research (n=65) on “IT bundling” by
combining IT with human and business resources. They found that certain advantages
exist when IT is linked with certain resources. “IT-intensive” firms rated IT performance
as significantly higher than “IT-lagging” firms. Their findings, however, did not prove
sustainability. This implies that bundling is simply not enough to yield sustainable
competitive advantage.
On the other hand, Bharadwaj (1999) found that certain firms possessing an ITcapability
did yield a sustainable competitive advantage. His empirical analysis found that
high IT-capability firms tended to outperform non-IT capability firms on a variety of
profit and cost-based performance measures. His findings support “the notion of IT as an
organizational capability is created by the synergistic combination of IT resources copresent
with other organizational resources and capabilities” (Bhardwaj, 1999, p. 22).
Therefore the key to creating a sustainable competitive advantage may occur when IT
becomes a capability or core competence. Ross, Beath, and Goodhue (1996) defined an IT
capability as the careful management of: (1) a highly competent IT human resource, (2) a
37
shared technology base, and (3) a strong partnering relationship between IT and business
management. Similarly, Prahalad and Hamel (pg. 81, 1990) defined a core competence as
“communication, involvement, and a deep commitment to working across organizational
boundaries. It involves many levels of people and all functions. A core competence should
be difficult for competitors to imitate. Duplication is difficult if it is a complex
harmonization of individual technologies and production skills.” For example, a rival
company might copy another firm’s technology, but imitation is prevented when the rival
cannot integrate the technology into its processes and culture
Strategic Use in the Foodservice Industry
In the foodservice industry, research suggests that there is a disparate view of
technology between business and IT-executives (Computer Sciences Corporation, 2001).
More IT directors (70%) felt that IT was integrated with the firm’s business strategy than
the business executives (40%) of similar firms. Viewpoints of the role of technology also
differed among executives; a majority of executives viewed IT as a “cost of doing
business” (45%) whereas others viewed it as an “investment” (35%); still others viewed it
as both (10%) (Computer Sciences Corporation, 2001).
Technology appears to be an enabler of performance. In 2001, the Computer
Sciences Corporation (CSC) reported the following metrics can improve as a result of
technology improvements: customer counts, sales, average check, and net profitability. In
the future, survey respondents expected technology to play a pivotal role in generating
value for organizations by helping firms meet labor challenges such as reducing store
manager administrative time, improving employee productivity, and training personnel.
38
Regarding the issue of sustainability, Huo (1998) found that foodservice firms
that were “investment intensive” experienced sustainable gains in sales, cash flows, and
net income over a period of three years. The researcher, however, was limited to the
number of years of data available and two measures of sustainability – profit margin and
return on sales.
As the result of the discussion on the strategic use of systems, restaurant managers
are asked questions pertaining to the competitive value of their systems.
System Success
The final outcome of systems planning is the implementation of successful and
effective systems. There are innumerable measures for the dependent variable – system
success. DeLone and McLean (1992) developed taxonomy for measuring information
system success and found that there were nearly as many measures as studies. Based on
communications theory (Shannon & Weaver, 1949; Mason, 1978) and a literature review,
DeLone and McLean (1992) defined system success as the effectiveness of a system to
communicate information at a technical level (accurately and efficiently), semantic level
(clarity of conveying the intended meaning), and effectiveness level (influence of
information on the receiver). They measured information success on six major
dimensions: system quality, information quality, use, user satisfaction, individual impact,
and organizational impact. Figure 2.7 (p. 39) illustrates the DeLone model of system
success.
39
Figure 2.7: System success model (Adopted from DeLone and McLean, 1992).
System quality focuses on the information system itself. The most common
measures of systems quality to be: response time, accessibility, flexibility of systems,
integration of systems, and ease of use and learning (DeLone & McLean, 1992; Davis,
1989; Bailey & Pearson, 1983; Srinivasan, 1985; Berlardo, Karwan, & Wallace, 1982).
Information quality focuses on the information provided by the system. Quality
information is both relevant and reliable (FASB, 1995). The Financial Accounting
Standards Board defines relevant information as timely, accurate, and useful whereas
reliable is defined as free from bias, verifiable, and neutral. Systems literature similarly
defines information quality as report accuracy, timeliness, understandability, reliability,
and relevance (Bailey, et. al, 1983; King & Epstein, 1983; and Srinivasan, 1985).
Use often relates to the quantitative use of the IS. Common numerical measures
are frequency of use, number of minutes, use of information, number of sessions, etc.
(Culnan, 1983; Fuerst & Cheney, 1982; Ginzberg, 1981; Srinivasan, 1985). Another
interpretation of use relates to applications usage such as the use in support of cost
System
Quality
Information
Quality
Use
User
Satisfaction
Individual
Impact
Organizational
Impact
40
reduction, decision-making, or strategic planning (Zmud, Bounton, & Jacobs, 1987;
Saarinen, 1996).
User information satisfaction (UIS) is the most widely used single measure of the
dependent variable of IS success (DeLone & McLean, 1992. The first recognized UIS
instrument was developed by Bailey and Pearson (1983) which measured UIS on 39
factors based. Ives (1983) shortened the instrument and suggested three UIS measures:
EDP staff and services, information product; and knowledge or involvement. The Ives
instrument, however, was not developed for end-user computing research (Doll &
Torkzadeh, 1988). Doll and Torkzadeh (1988) modified the Ives instrument, and
developed a shorter 12-item instrument designed to specifically measure end-user
satisfaction. Five factors – content, accuracy, format, ease of use, and timeliness –
emerged as a result of pilot study as a measure of user satisfaction. The researchers
reported instrument reliability of .92 and a criterion-related validity of .76. The UIS
instruments are appealing measure of IS success, given their high degree of face validity
and availability (DeLone & McLean, 1992).
Individual impact has to do with the effect of information on the behavior of the
recipient. It seems reasonable to assume that successful systems would positively
influence user attitude. Most researchers measure individual impact as the impact of IS on
decision-making – time involved, confidence in, and quality of – and the improved
understanding and ability to identify problems and solutions (Gosler, Green, & Hughes,
1986; Hughes, 1987; Luzi & Mackenzie, 1982; Srinivasan, 1985, and Zmud, Blocher, &
Moffie, 1983).
41
Sanders (1984) created an instrument to measure decision-making success
satisfaction (DSS) by measuring the ability of a system to assist users in decision-making
and better job performance (Sanders, 1984). Vandenbosch (1999) measured executive
decision-making effectiveness and its relationship to competitive advantage. She
examined four factors – scorekeeping, improving understanding, legitimizing decisions,
and enabling competitiveness. The loadings of all the factors were highly significant
(greater than .8). The dependent variable in Vandenbosch’s study was competitive
advantage. Only, two factors were correlated with competitiveness –focusing attention and
legitimizing decisions with r-values of .45 and .30, respectively.
Organizational impact has to do with the effect of information on organizational
performance. Organizational impact is often measured in terms of competitive advantage.
Measuring competitive advantage may be rooted in several different concepts: business
value, impact on competitive forces model, financial impact, and operational efficiency, to
name a few. Sethi and King (1994, p. 1604) developed a measure tool for competitive
advantage (CAPITA) and defined CAPITA as referring “to the benefits accruing to the
firm, in terms of changing the firm’s competitive position, that are by a single IT
application.” The CAPITA construct was comprised of five factors – efficiency
(efficiency gains relative to competitors), functionality (increase market share, customer
loyalty, and monopoly power), threat (bargaining power of suppliers and customers),
preemptiveness (leadership), and synergy (integration and difficult to imitate).
“A firm is said to have competitive advantage when it is implementing a value
creating strategy not simultaneously being implemented by any current or potential
competitors” (Barney, 1991, p. 102). Bharadwaj (1999) used “matched sample
42
comparison groups” to help remove extraneous variables, adjusted sample units for the
“financial halo effect” (past performance influences current performance), and used
financial measures for profits and costs to measure competitive advantage. MIS
researchers, however, have tended to avoid performance measures due to the difficulty of
isolating the IS effect from the other effects which influence organizational performance
(DeLone, et. al., 1992). Some researchers have looked at improved productivity, cost
effectiveness, and economic performance as measures of competitive advantage (Jenster,
1987; Lincoln, 1986; Ein-Dor, Segev, & Steinfeld, 1981). Porter (1985) used cost
leadership, product development, or improving market linkages, as measurements of
improved competitive advantage.
Sustainable competitive advantage has been defined as competitive advantage that
is maintained for a period of time due to lack of duplication (Barney, 1991). Sustainable
competitive advantage occurs when the rewards are “substantial enough to justify the
costs and risks associated with bring the prime mover” (Feeny & Ives, 1990, pg. 29).
Theoretically, sustainability does not depend on a period of time. Feeny and Ives (1990)
state that the timeline will vary and is sustainable if protected by “competitive
asymmetry.” Several researchers (Kearns, 1997; Powell & Dent-Micallef, 1997),
however, have used a period of three years to measure sustainability.
Based on this discussion of system success, the measure for system success is
decision-making support satisfaction (DSS). System quality and information quality serve
as antecedents to decision-making support satisfaction. This study, however, also uses
43
system quality and information quality as outcomes when analyzing their relationship
with different types of software packages (proprietary, off-the-shelf software, and ASPs).
Based on the discussion in this and prior sections of this chapter, the following
hypotheses are examined by this study:
H3a: There is a positive relationship between system use and DSS.
H3b: There is a positive relationship between perceived system quality and DSS.
H3c: There is a positive relationship between perceived report quality and DSS.
H3d: There is a positive relationship between perceived system fit and DSS.
H3e: There is a positive relationship between perceived user competency and DSS.
H3f: There is a positive relationship between perceived levels of IT training
quality and DSS.
H3g: There is a positive relationship between IT support and DSS.
H3h: There is a relationship between industry segment and DSS.
In addition to the hypotheses studying relationships between the aforementioned
variables and DSS, this study also examines the effect of these variables on the two
outcomes, DSS and FIT. The following hypotheses will be tested:
H4a: The proportion of variability in DSS can be explained by system usage,
quality of training and support, system quality, report quality, user
competence, and segment.
H4b: DSS and FIT are two separate constructs with different sets of antecedents
that explain variability.
44
Research Questions and Model
Companies are continually seeking ways to more effectively plan and implement
information systems. The literature review presented the following aspects of the
information systems literature:
• The history of strategic thought
• The evolution of computer applications development in the foodservice
industry
• The role of planning and participation in the developmental process
• Systems planning typologies
• Managerial alignment to critical success factors
• Theories regarding training and support issues
• User attributes and competence
• The strategic use of systems, and
• The multi-dimensional definition of system success.
The literature base of IS literature is varied, but applied research in the
foodservice industry is limited. Much of the IS research emphasizes the use of systems at
the top management levels. Executive systems are important in the foodservice industry,
but profits and losses are made at the restaurant level in an extremely competitive
environment. Therefore, a study of systems use at the restaurant level can be meaningful.
This study focuses on the operational use of systems. More specifically, this study
examines the use of restaurant management information systems (RMIS) by managers at
45
the restaurants. RMIS not only includes traditional financial reporting applications, but
also includes reporting such as variance reporting or forecasting that aid managers in
their decision-making processes. RMIS is often broad-scoped (including external, nonfinancial
information, and future-oriented material), timely, and aggregated (variety of
ways to present data or sum data within periods of time or areas of interest) (Choe, 1998).
Because restaurant managers are the target group to be surveyed, RMIS is the
appropriate system to study. The model for the study is presented on page 48. The
dependent variable used to measure RMIS success is decision-making support
satisfaction (DSS). Based on the literature review, the following combination of variables
is examined as antecedents to DSS:
• System Use – number of applications used by the restaurant manager.
• Training – manager ratings of the quality of training.
• Support – manager rating of the quality of support.
• System Quality – manager ratings regarding hardware quality.
• Report Quality – manager ratings of report quality.
• User competence – manager rating of proficiency.
• Organizational Characteristics – industry segment.
The following research objectives and hypotheses materialized from the
discussion in this literature review and is reflected in the “Model of Factors Contributing
to RMIS Success” presented on p. 48.
46
Research Objective 1: To describe current IT trends in the foodservice industry and to
investigate the contingent nature of IT use.
H1a: Casual dining, family, and quick-service restaurants use systems differently
to best meet specific industry needs.
H1b: Chains will utilize more software applications than independently owned
restaurants.
H1c: There is a positive relationship between sales volume and application use.
H1d: There is a positive relationship between perceived level of financial success
and the number of computer applications used by restaurant managers.
Research Objective 2: To identify the current level and quality of system training and
support provided to restaurant managers.
H2a: There is a positive relationship between types of training and training
quality ratings.
H2b: There is a positive relationship between the level of the “help desk” support
and support quality ratings.
Research Objective 3: To determine how system characteristics (number of applications
offered, system quality, fit, and report quality), and organizational attributes (industry
segment), and implementation characteristics (quality of training, support, and user
competency) relate to restaurant managerial decision-making support satisfaction ratings.
H3a: There is a positive relationship between system use and DSS.
H3b: There is a positive relationship between perceived system quality and DSS.
H3c: There is a positive relationship between perceived report quality and DSS.
H3d: There is a positive relationship between perceived system fit and DSS.
H3e: There is a positive relationship between perceived user competency and DSS.
H3f: There is a positive relationship between perceived levels of IT training
quality and DSS.
H3g: There is a positive relationship between IT support and DSS.
H3h: There is a relationship between industry segment and DSS.
47
Research Objective 4: To determine how system characteristics (number of
applications offered, system quality, and report quality), implementation
characteristics (quality of training and support and user competency), and
organizational characteristics (segment) impact restaurant managerial decisionmaking
support satisfaction.
H4a: The proportion of variability in DSS can be explained by system usage,
quality of training and support, system quality, report quality, user
competency, and segment.
H4b: DSS and FIT are two separate constructs with different sets of antecedents
that explain variability.
In addition to the research objectives, the characteristics of the foodservice
managers and restaurants in this study are presented. In addition, comments on IT
strengths and weaknesses, as assessed by restaurant managers, are discussed at the
conclusion of this study.
Summary
This chapter included a review of relevant literature from foodservice, strategic
management, and information systems planning journals. Based on the literature review,
the next chapter presents the methodology for this study. The variables are
operationalized and statistical analyses discussed. Chapter 3 also presents information on
the research design, instrumentation, and sample selection.
48
Figure 2.8: A model of factors contributing to RMIS success
Organizational
Factors:
Segment
Systems:
Breadth, System Quality, and
Report Quality
RMIS Success
Systems Implementation:
Training, Support, and User
Proficiency
49
CHAPTER 3
RESEARCH METHODOLOGY
The methods and procedures used to answer the research questions are described
in this chapter. The first section describes the research design and, the second, the
research variables. The third section describes the instrument development, validity, and
reliability. The fourth section discusses the population and sample. The fifth section
explains the data collection process. The sixth section describes the statistical analysis
techniques that were used in this study.
Research Design
This study includes descriptive, associational, and multivariate research.
Descriptive statistics involve tabulating, depicting, and describing sets of data (Hopkins,
1986). This study identifies and describes the characteristics of the surveyed restaurants
and their managers. In addition, it describes the levels of training and support provided to
the managers and the computer applications utilized by them on the job.
Associational research is a form of descriptive research, since there is no attempt
to manipulate independent variables. The purpose of associational (also known as
correlational research) is to describe relationships used to determine the extent to which
two or more things are related to each other or co-vary (Vogt, 1999). This research,
however, has one primary limitation. Interpretation of results only implies that certain
50
variables are related, but does not suggest causation (Vogt, 1999). A correlation of zero,
however, does eliminate the probability that there is a causal relationship between the
variables under consideration (McCracken, 1999).
This research also uses multivariate techniques to examine data. The research is
causal-comparative or research that compares groups after they have experienced varying
levels of the independent variable (McCracken, 1999). Since the independent variable has
occurred prior to the research study, this research is also known as ex post facto research
(McCracken, 1999).
The ex post facto research design used in this study is the static group comparison
design (Campbell and Stanley, 1963):
(X) O
O
Ex post facto research deals with antecedents that might influence O independent
of X in addition to evaluating alternative explanations for O. “Causal interpretation of a
correlation depends on both the presence of a compatible, plausible, causal hypothesis
and the absence of plausible rival hypotheses to explain the correlation on other grounds”
(Campbell & Stanley, 1963, p. 65). Multivariate techniques have been used to analyze
this study’s data. Multivariate analysis refers to statistical methods that simultaneously
analyze multiple measurements on the object under investigation (Hair, Anderson,
Tatham, & Black, 1998). The difficulty with multivariate techniques is that variables are
not manipulated, subjects are not randomly assigned to groups, and the lack of control
over the treatment leads to problems of interpretation. Often dependent variable scores
51
are the result of many complex interactions rather than the influence of a single
dependent variable. Extraneous variables or alternative explanations must either be
controlled or eliminated for ex post facto research to be worthwhile. By using foodservice
chains from the same industry, certain extraneous variables such as type of industry and
industry dynamics are held constant for all firms.
Research Variables
The research objectives for this study were presented in Chapter 1. The research
questions and hypotheses were developed in Chapter 2. This section operationalizes the
variables presented in the research model on page 48.
System Use
System use is operationalized based on the number and types of applications
utilized at the restaurant. Applications could be classified as clerical, administrative, and
tactical (Figure 2.1, p. 19) or by processes –operational, management, and strategic
(Luconi, Malone, & Morton, 1986) or as transactional, informational, and strategic
(Mirani & Lederer, 1998). The proposed classification scheme for this study uses the
following seven categories: financial reporting, sales management, office management,
communications, inventory and food cost management, human resources management,
and service quality.
The Powell and Dent-Micallef (1997) instrument was modified for the restaurant
industry to measure technology use. Respondents were asked if they use the following
19 technologies at their restaurants (1 = yes; 0 = no). The measures and related variable
names are:
52
1. Food costs APP1
2. Labor costs APP2
3. Sales results (mix, average guest check, customer counts) APP3
4. Variances (actual vs. budget) APP4
5. Server performance APP5
6. Sales forecasts APP6
7. Food production schedules APP7
8. Labor schedules APP8
9. Inventory tracking APP9
10. Customer history, loyalty, and/or complaints APP10
11. Vendor prices APP11
12. Service delivery times APP12
13. Word processing/spreadsheets APP13
14. Bookkeeping and financial reporting APP14
15. Training employees APP15
16. Menu or recipe development APP16
17. E-mail APP17
18. Automated pager/cell phone notification of problems APP18
19. Video monitoring from a remote location APP19
Training and Support
Training and support are both aspects of the implementation of systems. Training
deals with teaching managers how to use the systems. Support deals with the regular IT
support provided to the users.
Managers were asked about the types of technical training they received (special
seminars, on-the-job training, video or computer-based training, self-trained, or none).
They were also asked for the hours of their “help desk” (offered during regular business
hours, all hours the restaurant is open, 24/7, or none)
Managers were asked to rate the quality of the training and support on an ordinal
scale. Quality was measured using a 1-4 scale (inadequate, fair, good, and excellent). The
measures for quality of training and support and related variable names are:
Rate the quality of the training you received T1
Rate the quality of the “help desk” S1
Lastly, managers were asked to write comments related to training and support
issues on the survey.
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Managerial Characteristics
Managers were asked for demographic information to learn more about the
sample. They were asked to report their highest level of education, number of hours they
work per week, number of years in the foodservice industry, age, and gender.
User competence was also measured, which is a relatively new construct (Kim, et.
al., 1995). It might be considered an extraneous variable by some (DeLone & McLean,
1992). User competence was measured by asking a question regarding proficiency using
a 1-5 scale (no skill, novice skill, moderate skill, highly proficient skill, and mastery
skill). The measure for user competence and related name is:
How would you rate your technological proficiency skills as a manager? UCOMP
Organizational Characteristics
Managers were asked information about the organizations that they work for.
Managers were asked to report average sales volume, guest checks, and industry
segments. Restaurants were classified into three categories: casual service, family style,
and quick service. Casual dining restaurants generally include those with wait service,
guest checks averaging $10 and higher, and bar service such as the Cameron Mitchell
restaurants, Red Lobster, Olive Garden, and Outback. Family dining restaurants include
those also with wait service, but with guest checks of less than $10, and no bar service
such as Frish’s, Bob Evans, Tee Jayes, and Dennys. The quick service restaurants include
the fast food concepts such as Wendys, Arbys, McDonalds, and Steak Escape.
Managers were also asked to rate the financial success of their restaurants and the
competitive nature of their systems. The question related to the competitive nature of the
system is adopted from the instrument of Jarvenpaa and Ives (1991) and pertains to the
54
current level of use of IT in the firm based on Miles and Snow’s framework (p. 23). The
measure for competitiveness is:
How would you describe your firm’s use of information technology at your restaurant? (1 =
laggard, 2 = somewhat behind, 3 = middle of the pack, 4 = close follower, and 5 = industry leader)
The measure for financial success is:
How would you rate the financial success of your restaurant? (1 = not successful, 2 = minimally
successful, 3 = successful, and 4 = highly successful)
Minimally successful is defined as “falls below expectations and competitors,”
successful as “meets expectations,” and highly successful as “exceeds expectations and
competitors.”
Managerial Fit
Rockart (1979) originally wrote of system fit to managerial needs. Managerial fit
is measured by asking the following question:
How would you describe your computer systems’ fit to your needs as a manager? (1 = poor fit, 2
= below average fit, 3 = moderate fit, 4 = good fit, and 5 = perfect fit)
RMIS Quality
Based on the model presented by DeLone and McLean (p. 37), RMIS quality was
measured by surveying managers about system quality and information quality. The
instruments developed by Bailey and Pearson (1983), Ives and Chervany (1983) and Doll
(1988) were used to develop this construct. Doll’s 12-item instrument, regarding end-user
computing, was the primary source for this measure. Respondents were asked to evaluate
system and report quality using a 1-5 scale (1 = poor; 5 = excellent).
55
The measure for system quality measures is:
Rate the quality of your computer systems:
Dependability (frequency of crashes) SQ1
Easy to use (front and back of house) SQ2
Level of integration (between modules, reducing duplication of efforts) SQ3
Responsiveness (speed, easy to access) SQ4
Degree of flexibility (ability to update, make changes) SQ5
The measure for information quality is:
Timeliness of reports RQ1
Accuracy of reports RQ2
Readability of reports RQ3
Customizability of reports RQ4
Comprehensiveness of reports RQ5
Decision-Making Support Satisfaction
Decision-making support satisfaction (DSS) is the primary outcome of this study.
Rockart and Delong (1988) describe decision-making support as the way a system
enhances the way an executive thinks about business. Executive support systems should
help users with better planning and control. Good systems leverage the executive’s time
so to take better advantage of the executive’s experience, expertise, and perspective.
Good systems also educate the executive about the use and potential of the system.
Questions from the Sanders (1984) and Vandenbosch (1999) instruments were adapted
for this study.
Respondents were asked to evaluate the extent that their computer systems help
them with various decision-making tasks using a 1-5 scale (1 = not at all; 5 = to a great
extent). The measures and related variable names are:
The computer system helps me to:
Evaluate operational efficiency DS1
Dig behind the numbers DS2
Track progress toward goals DS3
Anticipate problem areas DS4
Takes the complexity out of my job DS5
Keeps me close to “what is going on” in the restaurant DS6
56
Increases the speed of at which I make decisions DS7
Improves the quality of my decisions DS8
The primary outcome of this study is decision-making support (DSS). Table 3.1
presents a summary of the constructs related to DSS and their respective measurements.
Construct Measurement
Decision-Making Support
Satisfaction
The averaged score of 8 items measuring the respondent’s
satisfaction with the decision-making support the system
provides.
Applications Utilized The number of 19 computer applications utilized by the
restaurant manager.
System Quality The averaged score of 5 items measuring the respondent’s
satisfaction with system quality.
Report Quality The averaged score of 5 items measuring the respondent’s
satisfaction with system quality.
Training One item asking respondent’s perception of the training provided
by the firm.
Support One item asking respondent’s perception of the support provided
by the firm.
Manager Characteristics One item asking user proficiency.
Organization Characteristics Classifications based on segment, sales volume, success, and
competitiveness of system.
Managerial Fit One item asking respondent’s perception of system fit to
managerial needs.
Table 3.1: Operationalization of the dependent variable, DSS, and its antecedents.
Instrument Development
A survey instrument was developed to address the research objectives presented
earlier in this study (p. 45 - 46). A copy of the survey instrument is in Appendix A. Some
questions from existing surveys have been used to develop this survey. High content and
construct reliability was reported for all questions adopted (> .7).
57
Pre-testing
The pretest of this instrument took place in four sequential stages as
recommended by Dillman (2000).
Stage 1: Colleagues and analysts reviewed the instrument. Inquiry included an
evaluation of questions, categories, and relevancy. The reviewers included: Dr. Thomas
George (hospitality professor and adviser), Dr. Chu (information systems professor and
committee member), Dr. Parsa (hospitality professor and committee member), Dr.
Johnson (hospitality professor and committee member), Dr. Dennis (accounting
professor) and Christian Selch (systems administrator).
Stage 2: Interviews were conducted with a sample representing the target
audience for cognitive and motivational qualities. Two restaurant managers were asked to
respond to the questionnaire in the presence of the author, who asked them to think out
loud as they completed the questionnaire. The author probed the respondents to get an
understanding of how each question was being interpreted and whether the intent of each
question was being realized (Dillman, 2000).
Stage 3: A few people who have had nothing to do with the questionnaire
development were asked to answer it completely. This step was used to catch obvious
and glaring errors missed by prior reviewers.
Stage 4: Three pilot studies of the survey were conducted. A group of MIS
personnel from different companies were asked to fill out the survey at a foodservice
technology conference in October, 2001. Two additional groups of restaurant managers
were asked to fill out the survey at a pizza and ice cream conference in March, 2002.
58
The pilot tests were conducted to see if response categories for scalar questions
were distributed across categories and that correlations build in such a way to build
scales. Also, the pilot study allowed for the estimation of response rate and to see if some
variables are so highly correlated that they could be eliminated.
After receiving responses from the pilot-test and running test statistics, minor
changes were made to survey.
Instrument Validity
The survey instrument was checked for four types of validity: face, content,
criterion-related, and construct.
Face Validity.
Face validity is the degree to which the instrument appears to be appropriate for
an intended audience. This is determined whether, on the face of it, measures make sense.
In determining face validity, expert judges during Stage 1 were asked whether the
measures seemed valid (Vogt, 1999).
Content Validity.
Content validity is the degree to which the instrument looks or appears to measure
the intended content area regarding the representative ness or sampling adequacy of the
content area. It is not a statistical test, but a judgment call.
First, to assess content validity of the measurement tool, the experts from Stage 1
were asked to review the instrument (Fraenkel and Wallen, 1996). The instrument
was modified until all experts approved the survey questions and format.
59
Second, a pilot test in Stage 2 was conducted with a group of forty restaurant
mangers. The researcher observed the individuals as they completed the survey and
answered questions. Obvious problems with the instrument were corrected on the final
instrument.
Criterion-related Validity.
Criterion-related validity is the extent to which the results of an instrument
compare with an external variable called the criterion. There are two types of criterionrelated
validity, which are concurrent validity and predictive validity.
Concurrent validity measures the degree to which performance on an instrument
is related to performance on other instruments intended to measure the same variable
(Fraenkel & Wallen, 1996). Most measures on this study’s instrument have been adopted
from other instruments, therefore concurrent validity is not be a problem.
Predictive validity occurs when the scores obtained on an instrument are used to
predict the future ability of performance (Henderson, 1999). Correlations between this
study’s dependent and independent variables have been calculated for fit. If the fit is
good, then the instrument has predictive validity. The regression equation related to
primary outcome of this study, DSS, had an adjusted r2 is .45. Predictive validity for this
instrument is not a problem.
Construct Validity.
Construct validity refers to the nature of the psychological construct or
characteristic being measured by the instrument. Factor analysis is one way of
determining construct validity that reduces large numbers of items into a smaller number
60
of factors. Items that “go together” will load together on a particular factor.
Factor analysis was conducted on the three constructs to determine construct
validity. For the three main constructs, the factor loadings were significant. If n > 150,
then a factor loading of .45 or higher is considered significant (Hair, et. al., 1998).
The research instrument appears to possess strong construct validity. The
component matrices for this study’s constructs are presented in Table 3.2:
System Quality
Report Quality
Decision-making
Support Satisfaction
Dependability .797 Timeliness .804 Evaluate .707
Ease of Use .773 Accuracy .795 Dig .738
Integration .738 Readability .809 Track .771
Responsiveness .823 Customization .687 Anticipate .813
Flexibility .744 Comprehensive .842 Complex .691
Close .769
Speed .758
Quality .832
Table 3.2: Factor loadings for primary constructs.
Instrument Reliability
Reliability relates to the consistency of results. The results should be dependable,
stable, consistent, and accurate. Cronbach’s alpha was used to measure internal
consistency. Cronbach’s alpha measures accuracy by determining if each sub sample
produces the same rank order of individuals. It if does, the instrument is reliable.
As a general rule, reliabilities should not be below .80 (Carmines and Zeller,
1979). The reliability for all three primary constructs in this study is above .80.
• Cronbach’s alpha for System Quality is .8322.
61
• Cronbach’s alpha for Report Quality is .8392.
• Cronbach’s alpha for Decision-making Support Satisfaction is .8955.
Given the reported Cronbach’s alpha for these three constructs, the instrument
appears to be reliable.
Population and Sampling
The target accessible population is restaurant general managers. The frame for
this study includes all the members from the Central Ohio Restaurant Association and all
the restaurants in the Columbus Yellow pages that were listed in the infoUSA’s Business
Mailing Lists software package.
Sample Size
A total of 243 surveys were collected (N = 1718). Of that amount, 194 had POS
systems, 27 had no systems, and 22 had only back-office systems. According to the
recommended sample sizes for the following statistical analyses, an appropriate number
of responses were collected.
Factor analysis was used to test construct validity. A minimum of 5 – 20 cases is
recommended per variable (Hair, Tatham, and Black, 1998). Factor analysis was used to
test the construct validity of system quality, report quality, and decision-making support
satisfaction. At a minimum, 40 cases must be collected. Factor analysis was also used for
classifying applications into groups for which 95 cases must be collected.
Multiple regression was used to predict DSS and fit. The minimum is 5 cases per
variable and 15-20 cases per independent variable were recommended (Hair, et. al.;
1998). The number of independent variables is 7. Therefore, a minimum sample of 50-55
cases must be collected.
62
Since the restaurants responding with POS systems number 194, the sample size
collected was large enough to calculate the aforementioned statistics in this study.
Data Collection
Two lists were used for the distribution of the surveys. The first list was the
membership from the Central Ohio Restaurant Association (CORA). The second list was
from the yellow page listings as reported in infoUSA’s Business Mailing Lists software
package.
One to three contacts were made with respondents to encourage high response
rates.
• A brief advance notice letter was sent to CORA members.
• Questionnaires were addressed to the general managers with a return reply
envelope.
• The researcher made a personal visit to at least one restaurant of any brand
that had not mailed their surveys back after the first two weeks.
Response Rate
The population of restaurants in Central Ohio consists of 1718 restaurants as of
August, 2002. A total of 243 surveys were received back from managers. The response
rate was calculated by using the formula: completed surveys returned ÷ surveys mailed
less undeliverable surveys. This gives a response rate of 14.1% (243/1718).
Although seemingly low, this response rate is not uncommon for this type of
study in hospitality or business. A low response rate, however, could limit the
generalizability of the results to the population.
63
Generalizability of Sample
Since this study did not use a random sample of restaurants in the United States,
the generalizability of the sample to the restaurant population is a concern. The sample
from Central Ohio includes responses from 243 restaurants comprised of 102 casual
service restaurants, 94 quick service restaurants, and 47 family style restaurants. This
includes responses from 51 national and regional brands, 14 local brands, and 73
independent restaurants (Table 3.3). In fact, thirty of the country’s “top 100” brands are
included in this sample.
Given the breadth of the sample, this sample appears to be representative of the
restaurants in Central Ohio region. Since the sample also included the results of 51
national brands, this sample might also be considered somewhat representative of the
national population.
National and
Regional Chains
(51)
American Bandstand, Arbys, Bob Evans, Blimpies, Burger King, BW3, Charleys,
Chic-Fil-A, CozyMels, Cracker Barrel, Dairy Queen, Damons, Dave and Busters,
Dennys, Dominos, Donatos, Dunkin Donuts, Frishes, Fuddruckers, Golden Corral,
HOPS, Jersey Mike, Little Tokyo, Lone Star, Longhorn, Marconi Grill, Max &
Ermas, McDonalds, Mongolian BBQ, OCharleys, Outback, Papa Johns, PF
Changs, Panera, Pizzaria Uno, Ponderosa, Rallys, Red Lobster, Red Robin, Ruby
Tuesday, Skyline, SmokeyBBQ, Spageddies, Steak Escape, Steak n Shake,
Subway, Taco Bell, Tim Horton’s, Wall Street Deli, White Castle, Wendys
Local Chains (14) Aladdins, Cameron Mitchell, China Dynasty, Cup O Joe, First Watch, Hoggys,
Ianoca Pizza, Mark Pi’s, MCL Cafeteria, Minicos Pizza, Rax, Roosters, Salvis, Tee
Jayes
Independents (74) Central Ohio independent restaurants
Table 3.3: Restaurant brands in the sample.
64
Non-response Error
Dillman (1978) wrote of an approach to measure bias. By examining the value of
the study’s dependent variable by date of response offers an indication of whether the
decision to respond was influenced by non-random events or motives. If the decision to
respond was random, then the timing of the response should not matter.
Non-response error can be dealt with in a similar manner. Late respondents are
compared to early respondents. Late respondents are assumed to be similar to nonrespondents.
If no differences are found, then respondents can be generalized to the
population (Miller, 2000)
A one-way analysis of variance was conducted comparing the means of DSS on
the surveys based on the week they were collected. The means were compared for
surveys returned during Week 1, Week 2, Week 3, Week 4, and for surveys collected by
hand (Table 3.4).
Sum of
Squares
Degrees of
Freedom
Mean
Square
F Sig.
Between
Groups
1.933 4 .483 .721 .579
Within
Groups
122.691 184 .670
Total 124.646 187
Table 3.4: ANOVA for DSS for the week collected.
The means for each group were compared to each other for the dependent
variable, DSS for 184 surveys using one-way analysis of variance. Table 3.4 shows that
65
there were no significant differences between groups. These results are accepted as
supporting the absence of non-response error in the sample responses.
Statistical Analysis
Data were coded and entered in the computer using the Statistical Package for
Social Sciences (SPSS 11.0). All null hypotheses were set at an alpha level of .05. Null
hypotheses were rejected if the probability associated with the calculated value of the test
statistics is equal or less than .05.
The primary statistical methods used for this study include descriptive and
multivariate statistics. A variety of statistical techniques were used to analyze the data in
this study. Table 3.6 (p. 68 – 69) summarizes the proposed analyses for each research
question.
To answer research questions 1 and 2, descriptive statistics, such as frequency
distributions and percentages, were computed. Mean, standard deviation, and ranges were
used to describe ratio variables. Ranking of applications’ use was also presented.
To answer question 3, descriptive statistics were used to describe the types and
quality of training and support (T & S) that were provided to restaurant owners. In
addition, correlations were calculated to investigate the associations between the types of
training or support offered and the quality ratings.
To answer question 4, correlations between independent variables and dependent
variable were calculated. The correlation coefficient is used to describe the relationship
between the variables. Values usually range from –1.0 to +1.0. The magnitude (or
strength) of the relationship is indicated by the numerical value of the coefficient. The
direction (or nature) of the relationship is indicated by the sign (+ or –) of the coefficient
66
(Warmbrod, 1999). Conventions for interpreting the association (relationship) of
correlation coefficients are presented in Table 3.5.
Coefficient Description
.70 or higher Very strong association (relationship)
.50 to .69 Substantial association
30 to .49 Moderate association
.10 to .29 Low association
.01 to .09 Negligible association
Table 3.5: Coefficients for association (Davis, 1971).
Regression analysis was used to answer question 5. The appropriate type of
regression for this study is multiple regression, meaning that there are more than two
categories of the dependent variable.
The objective of regression analysis is to predict the single dependent variable
from known independent variables. The first objective of regression is to maximize the
overall predictive power of the relevant independent variables. The second goal of
regression is to have high correlations between the independent variables and the
dependent variable, but a low correlation among independent variables.
The output of multiple regression is an equation that represents the linear
composite of the independent variables. Several statistics are presented with the equation:
• R2, the coefficient of determination, indicates how powerful an explanation the
regression equation provides.
• s2, the standard error of the estimate of Y, measures the accuracy of the fit
67
The assumptions related to multiple regression were analyzed for specification
errors, measurement errors, errors regarding residuals, and multicollinearity.
To answer question 6, an ANOVA was used to compare quality ratings (DSS,
RQ, and SQ) among the segments. Content analysis was used to categorize IT strengths
and weaknesses and to record items that managers wanted to add to their systems.
Summary
This chapter included a discussion of the research methodology and survey
development and implementation. Operationalization of the variables was also discussed,
and justification for statistical techniques given. Chapter 4 presents the results of this
study.
68
Research Objectives and Questions Levels of
measurement
Data Analysis
Demographics: To describe the characteristics of
foodservice managers and restaurants in this study.
1. What are the characteristics of the managers and
restaurants in the study?
• For managers: gender, education, hours worked
per week, age, and work experience.
• For organizations: segments, average guest
check, number of employees, ownership, sales
volume, and financial success.
Nominal
Ordinal
Ratio
Frequency
distributions
Percentages
Means
Standard deviations
Ranges
Research Objective 1: To describe current IT trends in
the foodservice industry and to investigate the contingent
nature of IT use.
2. What are the current trends of IT in the foodservice
industry today? Do offerings among segments differ?
• IT trends: classification schemes, strategic
orientation, and current software usage.
• Contingent nature of IT use: according to
segment, sales volume, chains versus
independents, and financial success.
Nominal
Ordinal
Ratio
Factor Analysis
Chi Square
ANOVA
T-tests
Research Objective 2: To identify the current level and
quality of systems training and support provided to
restaurant managers.
3. What are the current levels and quality of training and
support provided to managers:
• Types of IT training: none, in-house, on-the-job,
seminars, videos, or computer-based training?
• Types of support do foodservice firms offer
restaurant managers: none, during business
hours (9-5), all hours the store is open, or 24/7?
Nominal
Ordinal
Frequency
Distributions
Histograms
Cramer’s V
Spearman’s Rho
Content Analysis
Table 3.6: Summary of research objectives and data analysis
69
Table 3.6 (continued)
Research Objective 3: To determine how system
characteristics (number of applications offered, system
quality, fit, and report quality), manager (user
proficiency) and organization (segment) attributes, and
implementation characteristics (quality of training and
support) impact restaurant managerial decision-making
support satisfaction ratings.
4. What are the relationships between the variables in
the research model (p. 45) and decision-making support
success?
a. Number of applications utilized and DSS.
b. System quality and DSS.
c. Report quality and DSS.
d. Fit and DSS.
e. User competence and DSS.
f. Quality of IT training.
g. Quality of IT support.
Ordinal
Ratio
Means
Standard Deviations
Pearson’s r
Sprearman’s Rho
Research Objective 4: To determine how system
characteristics (applications offered, system quality, and
report quality), manager attributes (user proficiency),
organizational characteristics (segment) impact DSS
ratings?
5. To what extent can variability in the dependent
variable, DSS, be explained by the independent variable
set: RQ, SQ, TQ, HQ, TOTAPP, DUMSEG, and PROF?
Ordinal
Ratio
Multiple Regression
Recommendations: To assess IT strengths and
weaknesses and summarize recommendations made by
managers to improve IT.
6. What are the strongest and weakest aspects of systems
offered today? What did managers have to say about IT
strengths and weaknesses?
Narrative
Nominal
Ratio
Means
ANOVA
Content Analysis
70
CHAPTER 4
RESULTS AND DISCUSSION
This study investigated the role of computer technology in the foodservice
industry. Specifically, this study addressed the following six research objectives with
their related research questions and hypotheses.
1) To describe the characteristics of the foodservice managers and restaurants in
this study;
2) To describe current IT trends in the foodservice industry and explore the
contingent nature of IT use;
3) To identify the current level and quality of system training and support
provided to restaurant managers;
4) To describe the relationships between the variables in the “RMIS success”
model;
5) To determine how system characteristics (number of applications offered,
system quality, and report quality), manager attributes (user proficiency),
implementation characteristics (quality of training and support), and
organizational characteristics (segment) impact restaurant managerial
decision-making support satisfaction ratings, and to perform sensitivity
analysis on the regression equation;
6) To assess the IT strengths and weaknesses and summarize recommendations
made by managers to improve IT.
In order to investigate the research objectives, a survey was developed and a
given to restaurant managers. 1718 surveys were distributed among restaurant mangers in
the Central Ohio area with a 14.1% usable response rate (n = 243). The data were
71
analyzed using the Statistical Package for Social Sciences (SPSS) 11th version.
Demographics were gathered on all 243 respondents. Since only 194 respondents owned
POS systems, the analysis related to POS systems used a sample of n = 194.
Research Question 1:
What are the characteristics of the managers and restaurants for the sample?
The survey contained questions to assess the demographic and professional
characteristics of the sample. The demographic characteristics included gender and age.
The professional characteristics included education level, number of years experience in
the industry, and number of hours worked per week. Organizational characteristics
included segment, ownership type, sales volume, financial success, average guest check,
and number of employees.
Manager Characteristics
Gender
The majority of the respondents in the study were male (69%). The variability of
the distribution is shown in Table 4.1. The casual and family segments were
predominantly male at 71% and 79%, respectively. The quick service segment employed
the most female managers (36%) with males making up 64% of the managerial sample.
Casual Family Quick Service
Gender (%) (%) (%)
Male 71 71 37 79 59 64
Female 28 28 10 31 34 36
Total 99 100 47 100 93 100
Number of responses (%) Percentage of responses
Table 4.1: Manager demographics: gender.
72
Education
Analysis of the data revealed that most managers have some level of college
education. The levels of education for the sample were reported as: high school (17%),
some college (29%), associates (10%), bachelors (38%), and graduate school (6%). The
variability of education is presented in Table 4.2. Among the three segments, the casual
segment had the most highly educated managers with 50% possessing a bachelor’s
degree or higher. Family style was second with 44%, and quick service was the lowest at
38%.
Casual Family Quick Service
Education (%) (%) (%)
High school 13 13 7 15 20 22
Some college 24 24 14 30 31 33
Associates 13 13 5 11 7 8
Bachelors 44 44 19 40 28 31
Graduate degree 6 6 2 4 7 8
Total 100 100 47 100 93 100
Number of responses (%) Percentage of responses
Table 4.2: Manager demographics: education.
Hours Worked per Week, Age, and Experience
Statistics related to number of hours worked per week, age, and work experience
are shown in Table 4.3 (p. 73). These findings illustrate that managers in all segments
worked more than a typical 40-hour work week. Casual service managers worked the
most hours at 55.3 hours. The family (53.5 hours) and quick service (52.5 hours)
segments worked fewer hours than casual service managers, but still more than the
typical 40 hours.
73
Family style managers were oldest (43 years old) and most experienced managers
(18.6 years). Quick service managers were the least experienced managers (14.8 years),
but had a mean age of 39.1 years old. The casual service managers were the youngest
managers at 37 years old, but had more work experience than the quick service managers
(15.3 years).
Means Casual Family Quick Service
Hours worked per week 55.3 hrs
(S.D. = 13)
(Range 2 - 96)
53.5 hrs
(S.D. = 11)
(Range 25 - 83)
52.5 hrs
(S.D. = 13)
(Range 6 – 90)
Years of experience 15.8 yrs
(S.D. = 9)
(Range 1- 55)
18.6 yrs
(S.D. = 8.6)
(Range 5 - 40)
14.8 yrs
(S.D. = 9)
(Range 1 – 38)
Age 37 yrs old
(S.D = 8.9)
(Range 24 - 68)
43 yrs old
(S.D. = 10.4)
(Range 26-73)
39.1 yrs old
(S.D. = 10.9)
(Range 19 - 76)
(n = 230)
Table 4.3: Manager demographics: hours worked per week, experience, and age.
Relatively speaking, given their age and number of years of experience, casual
service managers were the most experienced managers. If all the mean ages were
converted to 40 year old managers, casual service managers would have 18.8 years of
experience (mean of 15.8 plus 3 years) as compared to 15.7 years of experience for
quick-service (mean of 14.8 plus .9 years) or 15.6 years for family style managers (18.6
years minus 3 years). Another way to standardize age is to calculate average experience
per year, and then convert to experience for a 40-year old manager. Table 4.4 (p. 74)
shows the results of these calculations where family style and casual service managers
had approximately 17 years of experience. Quick service managers had substantially less
experience (15.14 years) that the other managers.
74
Equivalent years of experience for a
40-year old manager
Casual service 17.08 yrs
Family style 17.130
Quick service 15.14
Table 4.4: Standardized years of experience for 40-year old manager.
Restaurant Characteristics
Industry Segment
For the 243 restaurants participating in this study, 42% (102) were identified as
casual service restaurants (with table service and bar service, average check > $15),
38.6% (94) were identified as quick service restaurants (limited service, drive thru), and
19.4% (47) were identified as family style (with table service, family-oriented, no bar
service, average check < $15). Of the total group participating, 194 restaurants had POS
and back office systems, 22 had back office or home office systems only, and 27 had no
computer systems.
Guest Check and Number of Employees
The average guest check for the sample was $12.17. The variability of the average
guest check is demonstrated in Table 4.5 (p.75). Casual service restaurants had an
average guest check of $20.22 compared to the lower guest check averages of the quick
service and family style segments, $5.97 and $7.73, respectively.
75
Means Casual Family Quick Service
Guest check $ 20.22
(S.D. = $11.96)
(Range $6.50 - $75)
$ 7.73
(S.D. = $3.04)
(Range $2.50 - $20)
$ 5.97
(S.D. = $2.25)
(Range $2 - $15)
Number of employees 50
(S.D. = 27.6)
(Range 1 - 150)
42
(S.D. = 28)
(Range 1 – 140)
21
(S.D. = 17.9)
(Range 1 - 100)
(n=236)
Table 4.5: Restaurant demographics: guest check and number of employees.
On average, the casual service segment also had the largest staffs with 50 full-
and part-time employees per restaurant. The family restaurant segment was a close
second with 42 employees. The quick service segment, without a need for a “wait”
service staff, used the fewest employees, averaging 21 employees.
Ownership
The ownership structures of establishments varied greatly (Table 4.6, p. 76). The
quick service segment was highly franchised with over 50% of the restaurants operating
as franchisees. Franchising among the casual service and family style segments was
minimal (10% and 2%, respectively) for the sample.
Most of the casual service and family style national brands were corporate owned
sometimes with the manager serving as an operating partner. Independent restaurant
owners were most prevalent among the casual service (42%) and family (51%) segments.
Only 21% of the quick service restaurants were owned by independent owners.
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Casual Family Quick Service
Ownership (%) (%) (%)
Franchisee 10 10 1 2 50 53
Independent 42 42 24 51 20 21
Chain-owned 49 48 22 47 24 26
Total 101 100 47 100 94 100
Number of responses (%) Percentage of responses
Table 4.6: Restaurant demographics: ownership.
Sales Volume
Analysis of sales volume revealed that casual service restaurants had the highest
average sales volume. The variability of the sample is presented in Table 4.7.
Casual Family Quick Service
Sales Volume (%) (%) (%)
Under $100,000 5 5 0 0 5 5
$ 100 – 250,000 5 5 1 2 13 14
$ 250 – 500,000 6 6 7 15 24 26
$ 500 – 1m 18 18 18 39 30 33
$ 1m – 2m 42 42 12 26 15 16
$ 2m – 5m 23 23 8 17 5 5
Over $5m 1 1 0 0 0 0
Total 100 100 46 100 92 100
Number of responses = 238 (%) Percentage of responses
Table 4.7: Restaurant demographics: sales volume.
The most noticeable contrast was between quick service and casual service. The
majority of quick service restaurants (79%) had sales below $1 million. On the other
hand, the majority of casual service restaurants (67%) had sales above $1 million. The
77
majority of family style sales (64%) were between $500,000 and $2 million. Figure 4.1 is
a graphical portrayal of the sales volumes for the three segments.
Sales Volume
2-5m
1-2m
500-1m
250-500
100-250,000
under $100
Percent
50
40
30
20
10
0
SEGMENT
casual service
quick service
family service
Figure 4.1: Sales volume by segment.
Financial Success
Restaurant managers were asked to rate the financial success of their restaurants.
Less than 1% rated their restaurants as “not successful” and 18.8% as “minimally
successful.” Most of the restaurant managers rated their restaurants as successful (60.7%)
or highly successful (19.7%). The variability of the sample is in presented in Figure 4.2
(p. 78).
78
Financial Success
highly successful
successful
minimally successful
not successful
Percent
70
60
50
40
30
20
10
0
20
61
19
Figure 4.2: Financial success ratings.
Research Question 2
What are the current usage trends of IT in the foodservice industry today? Do offerings
among segments differ?
The survey contained questions to assess software usage. Managers were asked if
they used 19 different computer applications.
Classification Scheme
Many schemes have been developed over the years to classify application use. A
principal component analysis was used to explore which, if any, factors would emerge. A
six-factor model explaining 65.05% of the variance emerged. Table 4.8 (p.79) displays
the rotated varimax solution.
79
Factor Analysis
1 2 3 4 5 6
Food cost .729
Labor cost .699
Sales (average check, counts, mix) .497
Variance (budget vs. actual) .632
Server performance .881
Sales forecasts .844
Food production .660
Labor scheduling .670
Inventory tracking .733
Customer history/loyalty .767
Vendor price comparisons .548
Service delivery times .631
Word processing/spreadsheets .838
Bookkeeping/financial reporting .485
Training .363
Menu/recipe analysis .671
E-mail .775
Pager notification .771
Remote video monitoring .687
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 10 iterations.
Table 4.8: Factor analysis for application usage.
The latent root criterion (eigenvalues greater than one) was used to determine how
many factors to extract. Loadings greater than .50 were considered practically significant.
The assumptions of multicollinearity were also met by the model (Table 4.9). The
Bartlett test of sphericity (which tests for multicollinearity) was significant, and the
measure of sample adequacy (MSA) was rated as at .76, which is considered almost
meritorious (Hair, et. al. 1998).
Measure of Sampling Adequacy .760 df 171
Bartlett's Test of Sphericity Chi-Square 845.726 Sig .000
Table 4.9: Factor analysis: MSA and Bartlett test of sphericity.
80
Further analysis was used to further simplify the factor analysis. Training and
sales (which had communalities below .50) were dropped from the classification scheme;
server performance (which loaded on its own factor) was moved to the “service”
category; and word processing was moved to the administrative category. Pager
notification and video monitoring, rarely used by all of the segments, were combined into
the “advanced technologies” category. As a result, a five-category classification scheme
was developed (Table 4.10):
Cost Analysis Forecasting Administrative Service Advanced
Technologies
Food costs Sales forecasts Word processing
Spreadsheets
Customer
history/loyalty
Pager notification of
problems
Labor costs Food production
forecasts
Bookkeeping
financial reporting
Service delivery
times
Remote video
monitoring
Variances Labor scheduling Menu and recipe
analysis
Server Performance
Vendor prices E-mail
Inventory tracking
Table 4.10: System application classification scheme.
Traditionally foodservice literature has classified applications according to
evolutionary processes such as clerical, integrated administrative, and tactical (Ellison
and Mann, 2000). This study’s classification scheme focused on the areas of management
decision-making: cost control, forecasting, administrative, and service quality. The cost
analysis category included applications used to control food and labor costs. The
forecasting category included sales reporting and forecasting models. The administrative
category included applications used in the office such as word processing, e-mail, and
bookkeeping. The service category included applications related to service quality such
81
as the tracking of server performance or service delivery times. The advanced technology
category included emerging applications not readily used by any of the industry
segments.
Competitive Rating
Restaurant managers were asked to rate the competitiveness of their systems.
They could rate their systems from the high of “industry leader” to the low of “laggard.”
Figure 4.3 reveals that the ratings were normally distributed among the categories. The
chi square statistic was calculated to investigate if there were differences among the
segments. The Pearson chi square was 16.125 with 8 degrees of freedom, significant at
.041. Therefore, differences in ratings do exist among the segments.
RATE
industry leader
close follower
middle of the pack
somewhat behind
laggard
Percent
50
40
30
20
10
0
8
14
46
27
5
Figure 4.3: Competitive ratings of IT.
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A contingency table was used to analyze where those differences were (Table
4.11). Casual service tended to have systems that were in the middle of the pack. The
quick-service segment had a majority of cases that were “close followers” and “industry
leaders” than the other segments. The family style segment had a stronger tendency to
use systems that were rated as “somewhat behind.”
laggard somewhat
behind
middle of
the pack
close
follower
industry
leader
Total
Segment
Casual
service
Actual Count 4 21 46 9 9 89
Expected Count 4.6 23.7 41.1 12.3 7.3 89.0
Quick Actual Count 6 14 28 14 6 68
Expected Count 3.5 18.1 31.4 9.4 5.6 68.0
Family Actual Count 0 17 16 4 1 38
Expected Count 1.9 10.1 17.5 5.3 3.1 38.0
Table 4.11: Differences among segments for strategic orientation (Chi Square)
Current Software Usage
Current usage trends are shown for the various segments in Table 4.12 (p. 83).
Industry averages were also calculated. The applications used the most by restaurant
managers, ranking in the top quartile, were: sales analysis (91.8%), labor cost analysis
(80%), bookkeeping (70.8%), and inventory tracking (67.2%). The applications in the
second quartile were: food cost analysis (65.6%), sales forecasts (61.5%), word
processing (61.5%), e-mail (58.5%), and variance analysis (57.4%). The applications in
the third quartile were: server performance (49.7%), food production schedules (39%),
menu development (38.5%), vendor price comparisons (37.4%), and training employees
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(35.4%). The applications used the least by restaurant managers, ranking in the fourth
quartile, were: service delivery times (25.1%), customer history/loyalty (21.5%), video
monitoring (8.7%), and pager notification (3.6%).
A few applications appeared to be more “segment” specific. The casual and
family style segments utilized server performance analysis and e-mail more than the
quick service segment. Service delivery times and video monitoring were more
predominantly used by the quick service segment.
Applications Casual Family Quick service Overall
Food costs 64.8% 55.3% 73.5% 65.6%
Labor costs 76.1 81.6 83.8 80
Sales analysis 92.0 92.1 91.2 91.8
Variances (budget vs. actual) 50.0 57.9 67.6 57.4
Server performance 62.5 55.3 29.4 49.7
Sales forecasts 55.7 60.5 70.6 61.5
Food production schedules 36.4 31.6 47.1 39
Labor scheduling 51.1 63.2 64.7 58.5
Inventory tracking 62.5 55.3 80.9 67.2
Customer history/loyalty 18.2 15.8 29.4 21.5
Vendor price 30.7 31.6 50.0 37.4
Service delivery times 15.9 15.8 42.9 25.1
Word processing/spreadsheets 75.0 50.0 51.5 61.5
Bookkeeping/financial reports 80.7 73.7 57.4 70.8
Training employees 35.2 31.6 38.2 35.4
Menu development 46.6 39.5 27.9 38.5
E-mail 64.8 63.2 48.5 58.5
Pager notification 3.4 5.3 2.9 3.6
Video monitoring 1.1 7.9 19.1 8.7
n = 62 n = 94 n = 47
Table 4.12: Application use percentages by industry segment.
In addition to the percentage use by industry segments, the average number of
applications per segment was calculated (Table 4.13, p. 84). Quick service restaurants
used an average of 10 applications out of a possible 19 computer applications. Casual and
family-style used an average of 9 applications. These differences, however, are not
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statistically significant. An ANOVA was calculated, and system use by segments did not
significantly differ at .05.
SEGMENT N Minimum Maximum Mean Std. Deviation
casual service 89 1 18 9 4.35691
quick service 68 3 17 10 3.39498
family-style 38 2 15 9 3.48875
Table 4.13: Number of applications used by each segment.
Contingency Theory
Contingent theory suggests that no universal information system is applicable to
all organizations in all circumstances. Segment, ownership type, sales level, and financial
success were all examined in regards to application use. The following hypothesis was
tested in regards to system use and segments:
H1a: Casual dining, family, and quick-service restaurants use systems differently
to best meet specific industry needs.
It would be expected that restaurants with table service would use server
performance analysis, whereas the quick service segment would track service delivery
times rather than evaluating server performance. Based on prior research, it is also
expected that the full-service restaurant would use administrative applications (word
processing, financial reporting, and menu analysis) more than the quick service segment.
ANOVA was used to statistically test if usage among industry segments differed.
The results are presented in Table 4.14 (p. 85):
85
Between
Groups
Within
Groups
DF
between
DF
Within
F Sig.
Food cost analysis .855 43.124 2 192 1.903 .153
Labor cost analysis .224 30.976 2 192 .694 .501
Sales results .004 14.683 2 192 .026 .974
Variances 1.279 46.393 2 192 2.647 .073
Server performance 4.472 44.276 2 192 9.697 .000*
Sales forecasts .935 45.219 2 192 1.985 .140
Food production schedules .733 45.646 2 192 1.542 .216
Labor schedules .758 46.596 2 192 1.561 .213
Inventory tracking 2.074 40.921 2 192 4.866 .009*
Customer history .660 32.294 2 192 1.962 .143
Vendor prices 1.652 44.020 2 192 3.603 .029*
Service delivery times 3.204 33.483 2 192 9.188 .000*
Word/spreadsheets 2.612 43.541 2 192 5.760 .004*
Bookkeeping/financial 1.978 38.360 2 192 4.950 .008*
Training .113 44.472 2 192 .244 .784
Menu/recipe analysis 1.271 44.882 2 192 2.719 .068
E-mail 1.032 46.322 2 192 2.139 .121
Pager notification .014 6.749 2 192 .199 .820
Remote video monitoring 1.251 14.267 2 192 8.420 .000*
*significant at .05 level
Table 4.14: ANOVA for differences in application use among segments.
Significant differences (at .05 levels) were found among industry segments for
several applications: server performance, inventory tracking, service delivery times, word
processing, bookkeeping, and video monitoring. As a result, we the null hypothesis
associated with H1a – there are no significant differences in system use among segments
– is rejected.
The Tamhane post hoc analysis (which does not assume equal variances) was
used to determine where the differences were between segments. The post hoc results are
presented for the items that are significantly differ in Table 4.15 (p. 86):
86
Mean
Difference (I-J)
Std. Error Sig.
Application (I) SEGMENT (J) SEGMENT
SERVER quick service casual service -.34* .076 .000
family style -.26* .099 .032
INV quick service casual service .19* .071 .023
family style .26* .095 .026
SERVDEL quick service casual service .27* .072 .001
family style .27* .085 .006
VENDOR casual service quick service -.20* .078 .039
family style -.01 .091 .999
WORD casual service quick service .23* .077 .011
family style .24* .095 .039
BOOK casual service quick service .22* .074 .009
family style .06 .084 .852
MONITOR casual service quick service -.18* .049 .001
family style -.07 .046 .378
* The mean difference is significant at the .05 level.
Table 4.15: Tamhane post-hoc analysis for segment differences in application usage.
Differences in usage applied to six software applications. Some of these
differences from the post hoc analysis were expected. For example, analysis of server
performance was utilized among casual and family style restaurants, but not in quick
service. Service delivery times, however, were tracked predominantly by the quick
service segment, not the casual and family style segments.
Inventory tracking and vendor price comparisons were utilized by quick service
more than the other segments. One possible explanation might be that more quick service
restaurants used POS systems with automated inventory ordering systems that might have
the ability to compare vendor bids. After analyzing the POS data, however, this was
found not to be the case. Only 26.5% of quick service restaurants had automated
inventory ordering, the least of all the segments.
87
A more likely explanation for the differences might be linked to the menus. Quick
service menus are usually limited and involve simpler recipes than the other segments. As
a result, programming a computer to track inventory would be easier for the quick service
segment. Further limited menus, might lend better to shopping for the best price.
Other differences between the segments were in the use of office products, word
processing and bookkeeping. Casual service managers utilized the office products more
than the quick service managers. Lastly, video monitoring from a remote location,
although not widely utilized in the industry, was used by quick service managers more
often than the other segments.
Two additional hypotheses were tested in regards to contingency theory.
H1b: Chains will utilize more software applications than independently owned
restaurants.
National chains have more financial resources to invest in systems development
than independently owned restaurants. T-tests were used to examine the differences
between chains and independent restaurants (Table 4.16, p. 88). Indeed, differences were
found for thirteen of the nineteen applications. Restaurant managers from national chains
used operational and forecasting applications (food cost, labor cost, variances, sales
forecasts, food production schedules, labor schedules, inventory tracking, customer
history, vendor price comparisons, service delivery times, training, menu analysis, and
monitoring) more than managers at independent restaurants. There were no major
differences, however, between chains and independents on the use of sales tracking,
server performance evaluations, word, bookkeeping, e-mail, and pager notification.
Regarding the use of operational computer applications, the independents ranked the
88
same as the chains, but were behind in their use of almost every other type of application.
As a result, the null hypothesis associated with H1b – there are no significant differences
in system use among segments – is rejected.
t Degrees of
freedom
Sig. (2-
tailed)
Mean Difference Std. Error
Difference
Food cost analysis -3.935 169.436 .000 -.26* .067
Labor cost analysis -4.849 129.121 .000 -.28* .057
Sales results .115 193 .909 .00 .040
Variances -5.764 177.249 .000 -.38* .067
Server performance .926 193 .356 .07 .072
Sales forecasts -2.722 178.570 .007 -.19* .070
Food production schedules -4.478 192.907 .000 -.30* .066
Labor schedules -3.400 179.701 .001 -.24* .070
Inventory tracking -7.423 145.377 .000 -.46* .062
Customer history -2.143 192.854 .033 -.12* .058
Vendor prices -2.724 191.601 .007 -.19* .068
Service delivery times -3.166 191.536 .002 -.19* .060
Word/spreadsheets 1.140 188.338 .256 .08 .070
Bookkeeping 1.185 190.367 .237 .08 .065
Training -2.828 192.282 .005 -.19* .067
Menu/recipe analysis 3.659 175.869 .000 .25* .069
Email -.130 193 .897 -.01 .071
Pager notification -.122 193 .903 .00 .027
Remote video monitoring -3.148 149.743 .002 -.12* .037
* The mean difference is significant at the .05 level.
Table 4.16: T-tests analysis for differences in application usage between chains and
independent restaurants.
H1c: There is a positive relationship between sales volume and application use.
Another contingent assumption of this study would be “high sales volume”
managers would use more computer applications than “low sales volume” managers.
ANOVA was used to see if there were differences in software use based on five volume
levels: under $250,000; $250 - $500,000; $500,000 - $1m; $1m - $2m; over $2m. Very
few significant differences were found between the groups due to sales volume. There
were differences on the use of e-mail by the “over $2m” sales group and the “$500,000”
89
group, the use of word and bookkeeping programs between the “$500,000” and “$1m”
groups, and service delivery times between the “under $100,000” and the “$1m” group.
Out of 285 possible combination of differences (5 sales levels for 19 applications for
chain versus independents), only 6 differences were found due to sales volume (Table
4.17. This overwhelmingly suggests that the sales level of the restaurant does not dictate
the type of system a restaurant uses. As a result, we fail to reject the null hypothesis –
there are no significant differences in system use based on sales volume.
Mean Difference (I-J) Std. Error Sig.
Application (I) SALESV (J) SALESV
1-2m .00 .092 1.000
CUSTOMER under $100 100-250,000 -.33 .167 .716
250-500 -.21 .085 .282
500-1m -.23 .059 *.004
1-2m -.20 .049 *.001
2-5m -.21 .072 .088
SERVDEL under $100 100-250,000 -.56 .176 .183
250-500 -.21 .085 .282
500-1m -.31 .065 *.000
1-2m -.28 .054 *.000
2-5m -.09 .051 .728
1-2m -.18 .074 .201
WORD 500-1m under $100 -.34 .212 .940
100-250,000 .13 .181 1.000
250-500 .00 .125 1.000
1-2m -.31 .087 *.009
2-5m -.30 .103 .076
EMAIL 500-1m under $100 .02 .255 1.000
100-250,000 .20 .162 .984
250-500 -.24 .120 .526
1-2m -.26 .089 .068
2-5m -.33 .103 *.025
* The mean difference is significant at the .05 level.
Table 4.17: Tamhane post-hoc analysis for sales level differences in application usage.
90
Financial Success
An exploratory analysis was conducted to see if application use differed among
the financially successful and unsuccessful restaurants. The following hypothesis was
tested:
H1d: There is a positive relationship between perceived level of financial success
and the number of computer applications used by restaurant managers.
ANOVA was used to determine if there were significant differences in system use
among the minimally successful restaurant, successful, and highly successful restaurants
(Table 4.18, p. 91). There were significant differences among the groups. First, highly
successful restaurants used food cost analysis, labor cost analysis, variances, and food
production schedules more than the successful and minimally successful restaurants. In
addition, minimally successful restaurants used sales forecasts, labor forecasts, service
delivery times, and training less often than the highly successful restaurants. Significant
differences were found among the restaurants based on perceived levels of financial
success. As a result, the null hypothesis associated with H1d – there are no significant
differences in system use based on sales volume – is rejected.
Foodservice operators should note that the highly successful restaurants used
seven more software applications than the minimally successful restaurants. The highly
successful managers were given more tools, which may have helped them to better
manage their restaurants.
91
Mean
Difference (I-J)
Std. Error Sig.
Dependent Variable (I) FinSuccess (J) FinSuccess
Food cost analysis highly minimally .28* .111 .047
successful .21* .074 .019
Labor cost analysis highly minimally .17 .091 .198
successful .16* .056 .019
Variance analysis highly minimally .30* .115 .037
successful .26* .080 .004
Food production highly minimally .36* .114 .007
successful .31* .087 .002
Labor scheduling highly minimally .32* .116 .025
successful .16 .083 .174
Service delivery times highly minimally .28* .102 .024
successful .19 .087 .107
* The mean difference is significant at the .05 level.
Table 4.18: Tamhane post-hoc analysis for success level differences in application usage.
Research Question 3
What are the current levels and quality of training and support provided to restaurants
managers?
Training and support in the industry appears to be adequate, but not outstanding.
Most managers rated the quality of the training and support they received as either fair
(2.0) or good (3.0) with mean scores of 2.5 and 2.6 (out of 4.0), respectively.
Training
Most restaurant managers (92.2%) reported that they received some form of
training on the use of their computer systems. The data in the sample is distributed over
five categories as presented in Figure 4.4 (p. 92).
The overwhelming majority of managers (82%) reported that “on-the-jobtraining”
was the principal mode of training received. Some restaurants or software
companies (24%) provided “special seminars” or “on-site training” to managers. Other
92
managers reported receiving “video or computer-based training” (9.9%) or were “selftrained”
(38.5%). Only 7.8% of all managers reported receiving “no training”
whatsoever. Obviously these managers had to train themselves also.
24
82
9.9
38.5
7.8
Special
seminars
On-the-job
training
Video or
computer
Self-trained No training
Percentages
Figure 4.4: Types of training.
The following hypothesis was tested in regards to the types of support offered and
the manager ratings of training quality (TQ).
H2a: There is a positive relationship between types of training and training
quality ratings.
Cramer’s V was used to determine if there was an association between the types
of training received and TQ (Table 4.19, p. 93). The results were: low association
between “special seminars” and TQ; low associations between “video and computerbased
training” and “self-taught” training and TQ; moderate association between “on-thejob
training” and TQ and “no training” and TQ.
93
TQ Sig. (2-tailed)
Special Seminars .293** .001
On-the-job training .359** .000
Video or computer-based .190 .070
Self-taught .153 .206
No training .470** .000
N 192
** Correlation is significant at the .01 level (2-tailed).
Table 4.19: Cramer’s V for types of training and quality ratings.
The association between “no training” and TQ was expected. The quality of
training would be considered inadequate if no training was received. On-the-job training
and special seminars also appear to be important means of training. As a result of these
findings, the null hypothesis associated with H2a – there is no relationship between types
of training and training quality ratings – is rejected.
Managers were also asked for written comments about training on the survey.
Fifteen managers responded and a content analysis was performed. Only two managers
wrote positive statements about the training they received. The remaining thirteen made
the following comments:
• Nine managers reported that the training was too basic. They needed more
advanced training in order to better use their systems.
• Three managers wanted on-going training and updates.
• One manager felt POS training should be a part of new manager training.
• Two others felt that “on-the-job training” was the only real way to learn how
to use the POS systems.
94
Additional analyses were conducted regarding training and other variables. One
analysis compared TQ to the variety of training offerings offered by firms. The Spearman
rho was .169, considered significant at the .05 level. Even so, the low association does
not yield an interesting result. Another relationship examined was that between TQ and
user proficiency. There was a negligible relationship (.046) meaning the training offered
by the restaurants had no impact on user proficiency.
Support
Most restaurants (91.5%) reported that support was provided to them. The data in
the sample is distributed over four categories as presented in Figure 4.4.
Availability
24/7 all hours open regular bus hrs no support
Percent
70
60
50
40
30
20
10
0
58
12
24
6
Figure 4.5: Availability of support.
The most frequently reported type of support was provided “24 hours a day, 7
days per week” (56.5%). The remaining frequency was distributed among support being
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provided during “regular business hours” (22.4%) and “all hours the restaurant is open”
(12%). Only 6.3% of restaurants reported that “no support” was provided to them.
The following hypothesis was tested in regards to the types of support offered,
and the manager ratings support quality ratings.
H2b: There is a positive relationship between the level of the “help desk” support
and support quality ratings.
Spearman’s Rho was used to determine if there was an association between
AVAIL and HQ. The results revealed a moderate association between AVAIL and HQ
(Table 4.20). Given a significance level of .05, the null hypothesis associated with H2b –
there are no relationship between help desk support and support quality ratings – is
rejected.
HQUAL Sig. (2-
tailed)
AVAIL .401 .000
N 188
Table 4.20: Spearman’s Rho for training availability and quality ratings.
Managers were also asked for written comments about support on the survey.
Fifty-three managers responded and a content analysis was performed. Sixteen managers
wrote positive things about the support provided to them whereas thirty-seven managers
complained. The following results were reported:
• Sixteen managers were happy with their support desks. Five managers
commented on how much they liked the in-house support provided by their
companies.
96
• Thirteen managers complained about poor support where the “help desk”
technician did not know the answer to their questions.
• Five managers complained of inconsistent results from using the “help desk”
– the quality of the answer depended on the person who answered the phone.
• Three managers complained that it took too long to receive an adequate
answer.
• Two managers described the “help desk” personnel as being “unfriendly.”
• Five managers complained about the hours of the “help desk” – either they
needed to be extended or the personnel were not available as promised.
• Twelve managers complained about the expense, often a charge per call or by
the minute.
Since the availability of the “help desk” was not related to how managers rate the
quality of service, other factors must precede the “quality” rating. Given the narrative
comments, possible antecedents to “training quality” might be the quality of the answer,
timeliness of the answer, and consistency of answers.
Research Question 4
What are the relationships between the variables in the research model (p. 48) and
decision-making support satisfaction?
The RMIS model to be tested in this study is presented in Chapter 2 (p. 48). The
variable names and descriptions for the model are listed in Table 4.21 (p. 97).
97
Variable Description Scale
DSS Decision-making support satisfaction 1-5
RQ Report quality 1-5
SQ System quality 1-5
TQ Training quality 1-4
HQ “Help desk” quality 1-4
NOAPP Number of applications 1-19
PROF User proficiency 1-5
SEG Segment 1-3
FIT System fit 1-5
Table 4.21: Variable names and scaled for RMIS model.
The means and standard deviations for these variables are presented in Table 4.22.
There were 194 usable surveys for the analysis presented in the table.
Mean Std.
Deviation
N
DSS 3.0689 .83649 194
TOTAPP 9.32 3.877 194
SQ 3.5919 .73527 194
RQ 3.7794 .71837 194
FIT 3.477 .7967 195
PROF 3.223 .8343 194
TQUAL 2.472 .8329 184
HQUAL 2.537 .9111 184
Table 4.22: Descriptive statistics for RMIS variables.
The average number of applications used by restaurant managers was 9
applications out of a total of 19 possible uses. The manager average ratings on system
quality (3.6/5.0), report quality (3.8/5.0), and system fit (3.5/5.0) were higher than their
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ratings on decision-support satisfaction (3.1/5.0) ratings. Managers felt fairly proficient in
their use of system (3.2/5.0). Also ratings on training quality and “help desk” quality
were 2.5/4.0.
Correlations
The correlation matrix for the RMIS variables is presented in Table 4.23.
DSS TOTAPP SQ RQ FIT PROF TQUAL HQUAL
DSS 1.000
TOTAPP .447 1.000
SQ .459 .159 1.000
RQ .488 .262 .633 1.000
FIT .529 .289 .586 .575 1.000
PROF .277 .301 .201 .221 .322 1.000
TQUAL .431 .261 .359 .341 .385 .181 1.000
HQUAL .331 .331 .333 .326 .332 .081 .450 1.000
Table 4.23: Correlation matrix for RMIS models factors and outcome.
The following hypotheses were addressed from the data provided by the
correlation matrix.
H3a: There is a positive relationship between system use and DSS.
H3b: There is a positive relationship between perceived system quality and DSS.
H3c: There is a positive relationship between perceived report quality and DSS.
H3d: There is a positive relationship between perceived system fit and DSS.
H3e: There is a positive relationship between perceived user competency and DSS.
H3f: There is a positive relationship between perceived levels of IT training
quality and DSS.
H3g: There is a positive relationship between IT support and DSS.
99
H3h: There is a relationship between industry segment and DSS.
The statistic that describes the magnitude of the relationships for these hypotheses
is the Pearson-product moment correlation. The nature of the relationship for these
relationships is positive and linear. As DSS also increases, so does the other variable. The
magnitude of the relationships and level of associations listed in Table 4.24.
Variable Pearson R Level of association
TOTAPP .447 Moderate
SQ .459 Moderate
RQ .488 moderate
FIT .529 substantial
PROF .277 low
TQUAL .431 moderate
HQUAL .331 moderate
Table 4.24: Correlations of factors and DSS.
We will reject all of the null hypotheses associated with H3a-h. The strongest
relationship among the variables was between DSS and FIT. There were moderate
relationships among DSS and TOTAPP, RQ, SQ, HQ, and TQ. The weakest relationship
was between DSS and PROF.
The Spearman Rho was used to calculate the relationship between DSS and the
nominal variable, segment (Table 4.25, p. 100). The correlation between DSS and
segment is negligible and insignificant at the .05 level. We fail to reject the null
hypothesis associated with H3h. Segment appears to be unrelated to DSS ratings.
100
DSS Sig. (2-tailed)
Segment .043 .553
N 194
Table 4.25: Spearman’s Rho for segment and DSS.
Research Question 5
To what extent can variability in the dependent variable, DSS, be explained by the
independent variable set: RQ, SQ, TQ, HQ, TOTAPP, DUMSEG, and PROF?
Model specification
Regression analysis was used to address this research question. The regression
equation is in Figure 4.6.
DSS = β0 + β1report quality + β2system quality + β3training quality + β4“help desk”
quality + β5total applications + β6dumsegment + β7proficiency + ε
Figure 4.6: Regression Equation.
Variable “dumseg” is a dummy variable that looks at the effect of segment on the
regression equation. Since the Spearman Rho in the previous analysis showed no
relationship between segment and DSS, segments have been collapsed into two segments:
full service and quick service. Family dining and casual service were reclassified into the
full service segment for this equation.
The coefficients of interest in this study are β1, β2, β3, β4, β5, and β7. They are
expected to have a positive relationship with DSS. According to Delone and McLean
(1992), report quality, system quality, and system use are antecedents to DSS. Two new
variables, training quality and “help desk” quality, were added to this study to learn the
101
effects of implementation on DSS. Lastly, two potential extraneous variables, DUMSEG
(as discussed earlier) and user competency, were added to the model to examine their
potential effects on the equation. The variable, fit, has been excluded from the initial
model for reasons to be discussed later in this section. It will be added to the model later
in the sensitivity analysis portion of this analysis.
Evaluation of Fit
The R for the full model is the Pearson product moment correlation coefficient.
The R indicates the magnitude and linear relationship of the dependent variable with the
linear combination of the independent variables.
The R for the full model is .687 (Table 4.26, p. 102). The magnitude of the
relationship is substantial (Davis, 1971), and the direction of the relationship is positive.
In other words, as Y increases, the linear combination of Xs increases as well.
R2 is the measure of goodness of fit and is the coefficient of determination, which
indicates the proportion of variance in the dependent variable explained by the linear
combination of all the independent variables. The coefficient varies between 0 and 1. The
higher the R2, the greater the explanatory power of the regression equation, and better the
prediction of the dependent variable.
The R2 for this model is .472 (Table 4.26, p. 102) and the adjusted R2 is .451. The
proportion of variance in DSS explained by the linear combination RQ, SQ, TQ, HQ,
TOTAPP, DUMSEG, and PROF is 45.1%.
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Model R R2 Adjusted R2 Std. Error of the
Estimate
Durbin-
Watson
1 .687 .472 .451 4.89919 1.953
a Predictors: (Constant), HQUAL, PROF, SQ, TOTAPP, TQUAL, DUMSEG, RQ
b Dependent Variable: DSS
Table 4.26: Regression model summary.
Significance of the Regression Model
The following hypothesis relates to the research model:
H4: The proportion of variability of DSS can be explained by system usage,
quality of training and support, system quality, report quality, user
competency, and segment.
The research hypothesis is accepted. The F-statistic overwhelmingly supports the
rejection of the null hypothesis associated with the traditional F-test at the 5% level
(Table 4.27). The regression assumptions of autocorrelation, homoscedasticity, and
multicollinarity were not violated. The above tests support the adequacy of the estimated
regression model.
Model Sum of Squares d of f Mean
Square
F Sig.
1Regression 3797.767 7 542.538 22.604 .000
Residual 4248.369 177 24.002
Total 8046.135 184
a Predictors: (Constant), HQUAL, PROF, SQ, TOTAPP, TQUAL, FIT, RQ b
Dependent Variable: DSS
Table 4.27: Adequacy of regression model.
103
Significance of Variables
Based on the model purported by Delone and Mclean (1992), it is no surprise that
the standard features of the RMIS success model works well. The coefficient estimates of
TOTAPP, SQ, RQ, and TQ are reasonable in terms of magnitude and sign (Table 4.28).
The coefficients are significant at the 1% levels. The new variable added to this model,
TQUAL, also contributed significantly to the equation whereas the other new variable,
HQ, did not. The potential extraneous variables, PROF and DUMSEG, were not
significant and did not contribute to the model.
Unstandardized
Coefficients
Standardized
Coefficients
T Sig.
Model B Std. Error Beta
1(Constant) .794 2.400 .331 .714
TOTAPP .464 .105 .274 4.399 .000
SQ .394 .084 .217 2.957 .004
RQ .436 .133 .238 3.280 .001
DUMSEG -1.019 .780 -.073 -1.004 .193
PROF .277 .488 .034 .567 .571
TQUAL 1.377 .515 .174 2.677 .008
HQUAL .330 .479 .045 .689 .492
a Dependent Variable: DSS
Table 4.28: Regression coefficients.
Regression Assumptions
To minimize measurement error in the regression analysis, certain assumptions
should not be violated. The assumptions about the residuals and homoscedasticity were
not violated by this analysis. All five assumptions regarding the residuals are discussed in
104
detail in the appendix of this chapter (p. 120-123). In addition, the regression equations
works best if each independent variable is highly correlated with Y, but not with each
other. If variables are highly correlated with each other, then they might be measuring the
same thing.
To access multicollinearity, the tolerances were examined in the collinearity
diagnostics. If any tolerances are less than .1, multicollinearity might be a problem. All of
the tolerances were greater than .1, thus multicollinearity does not appear to be a problem
in this analysis (Table 4.29).
Collinearity Statistics
Model Toleranc
e
VIF
1(Constant)
TOTAPP .767 1.304
PROF .842 1.188
TQUAL .707 1.414
HQUAL .696 1.437
SQ .554 1.804
RQ .567 1.763
DUMSEG .950 1.052
a Dependent Variable: DSS
Table 4.29: Collinearity statistics for regression equation.
Since the regression assumptions were not violated, the regression equation for
this model can be relied upon as adequate.
Sensitivity Analysis of Regression Equation
Sensitivity analysis was conducted on the regression equation to test for
exploratory purposes and to test for robustness (Table 4.31, p. 107). Models 1, 2, and 3
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all measure the effects of the coefficients on the dependent variable, DSS. Model 1
represents the original equation, with Model 2 adding the variable, FIT, and Model 3
adding the variable, RATE (the competitive nature of the system). The coefficients of
TOTAPP, SQ, RQ, and TQUAL loaded in each model, and appear to be quite robust. FIT
loaded only in Model 2, and when RATE was added in Model 3, FIT no longer loaded.
Therefore, FIT may not be an antecedent to DSS, but could be measuring some other
effect.
The variables added to Models 2 and 3 also do not seem to significantly impact
the adjusted R2. Therefore Model 1, as purported by theory, is a reliable model to use to
predict DSS. Therefore, significant antecedents to decision-making support are the total
number of applications offered to managers, service quality, report quality, and the
quality of training received by managers.
FIT, however, may be an additional dependent variable of interest. If FIT is not an
antecedent to DSS, could DSS is an antecedent to FIT?
The same regression equation used in Model 2 was used for Model 4, except FIT
and DSS were swapped for each other. An interesting model emerged with SQ, RQ,
PROF, and DSS as antecedents to fit with an adjusted R2 of .470. TOTAPP and TQUAL
did not significantly contribute to the FIT regression equation.
FIT appears to be a separate construct from DSS with its own antecedents.
According to Model 4, the antecedents to FIT were system quality, report quality,
decision-making support, and user proficiency. Sensitivity analysis was performed on
Model 5 with DUMCHAIN and RATE being added to the model. Variable “dumchain” is
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a dummy variable that looks at the effect of ownership (chain vs. independent) on the
regression equation.
For Model 5, the adjusted R2 jumped to .608. The new variables, DUMCHAIN
and RATE were significant, and DSS became insignificant. This implies that systems that
FIT manager needs were those with system and report quality and where the user felt
proficient in using it. The systems that had a better FIT were also those that were more
competitive (an industry leader versus middle of the pack or behind) and used by
restaurants associated with chains.
Table 4.30 compares the constructs of DSS and FIT, and the coefficients that
were significant for each model. Common to both constructs, DSS and FIT, are system
quality and report quality.
Coefficient DSS FIT
SQ √ √
RQ √ √
TOTAPP √
TQ √
PROF √
RATE √
DUMCHAIN √
Table 4.30: Comparison of significant coefficients for DSS and FIT.
In addition, MIS theory purports that DSS is the desired outcome of successful
systems. This study would suggest, however, that FIT might be a more appropriate
outcome. First, with an adjusted R2 of .608, the predictive value of Model 5 is
impressive. Second, FIT is broader in scope than DSS. Third, FIT seems to capture
107
Model 1 Model 2 Model 3 Model 4 Model 5
Dependent
DSS DSS DSS FIT FIT
Independen
t
TOTAPP .464**
(.105)
.440**
(.104)
.426**
(.104)
2.160E-03
(.058)
5.063E-03
(.012)
SQ .391**
(.132)
.284*
(.135)
.297**
(.136)
5.360E-02**
(.094)
4.832E-02**
(.014)
RQ .436**
(.133)
.333*
(.136)
.300**
(.138)
5.020E-02**
(.016)
1.370E-02
(.015)
DUMSEG -1.019
(.780)
-.917
(.766)
-.919
(.765)
-3.513E-02
(.094)
-.141
(.090)
PROF .277
(.488)
2.471E-02
(.487)
-3.311E-03
(.487)
.143*
(.058)
.107**
(.051)
TQUAL 1.377**
(.515)
1.180**
(.510)
1.154*
(.510)
8.276E-02
(.063)
5.332E-02
(.051)
HQUAL .330
(.479)
.234
(.471)
.220
(.471)
4.860E-02
(.057)
6.141E-02
(.051)
FIT 1.686*
(.607)
1.278
(.699)
RATE .614
(.523)
.368**
(.049)
DSS 2.493E-02**
(.009)
1.355E-02
(.089)
DUMCHAI
N
-.280**
(.089)
Adjusted
R2
.451 .471 .472 .470 .608
F-statistic 22.604 21.494 19.30 21.364 26.645
D of
Freedom
184 184 184 184 184
**Significant at .01 level *Significant at .05 level
Table 4.31: Regression models.
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certain aspects of systems development, PROF, RATE, and DUMCHAIN, that are not
captured by DSS.
The significance of DUMCHAIN implies that chains, with their financial and
human resources, have opportunities to develop, plan, and select systems that best fit
their managers’ needs. In general, independents do not possess these same resources.
Strategically speaking, chains must also have systems that are competitive with their
peers. Perhaps that is one reason, RATE, the competitiveness of the system, is a
significant coefficient. Lastly, PROF is significant, and incorporates the role of the user
into the fitness model.
Research Question 6
What are the strongest and weakest aspects of the systems offered today? What did
managers have to say about IT strengths and weaknesses?
Three aspects of RMIS quality were measured by this study. Those dimensions
were: system quality, information quality, and decision-making support quality. The
overall scores for the three dimensions are presented in Table 4.32:
Quality Ratings
(out of 5)
System 3.59
Report 3.78
DSS 3.07
Table 4.32: Means of quality ratings.
Managers gave report quality the highest score (3.78) and system quality the
second highest score (3.59). The ratings for DSS (3.07) were considerably lower than
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both RQ and SQ. The specific aspects that made up each construct are listed in Table
4.31 (p. 104). Each aspect has been ranked with the highest score first. The scores for
both system quality and report quality seem to be relatively good on a scale of 5.0. The
strongest aspects of system quality were “ease of use” (3.864) and “dependability”
(3.618). The strongest aspects of report quality were “readability” (3.995) and accuracy
(3.990). The weakest aspects of systems quality were “lack of integration” (3.451) and
“flexibility” (3.443). The weakest aspect of reports quality was the “lack of
customizability” (3.301).
Managers were not as satisfied with DSS as with SQ or RQ. In fact, the highest
DSS rating (3.245) was lower than the lowest ratings for systems quality (3.443) or report
quality (3.301). The strongest DSS items were “evaluates operational efficiency” (3.245)
and “keeps me close to what is going on in the restaurant” (3.186). The weakest DSS
aspects were “increased the speed at which I make decisions” (2.941) and “helps me to
anticipate problem areas” (2.688).
System Quality Report Quality DSS Quality
Ease of use 3.864 Readability 3.995 Evaluate operational 3.245
Dependability 3.618 Accuracy 3.990 Keep close 3.186
Responsiveness 3.565 Timeliness 3.861 Track progress 3.178
Integration 3.451 Comprehensiveness 3.736 Improve quality 3.149
Flexibility 3.443 Customizability 3.301 Dig behind the #s 3.103
Take complexity out 3.062
Increase speed 2.941
Anticipate problems 2.688
Table 4.33: Ranking of quality ratings.
110
An analysis of variance was calculated to see if there were any significant
differences in the quality ratings among segments. The only significance difference was
system quality (Table 4.34). There were no significant differences for report or DSS
quality among the segments at the .05 level.
ANOVA
Between
Groups
Within
Groups
DF
between
DF
Within
F Sig.
DSS 1.805 133.242 2 191 1.294 .277
SQ 3.942 100.939 2 192 3.749 .025
RQ .823 98.775 2 192 .795 .453
Table 4.34: Comparison of means of segments and quality ratings.
Post hoc tests were performed to determine where the segments differed regarding
system quality. The Tukey post-hoc test was used, and the findings showed that the
differences occurred between the casual service and quick-service segments (Table 4.35).
Dependent Variable: SQ
ANOVA
Mean
Difference (I-J)
Std. Error Sig.
(I) SEGMENT (J) SEGMENT
casual service quick service -.3195* .11678 .019
family style -.1521 .14051 .526
quick service casual service .3195* .11678 .019
family style .1675 .14685 .490
family style casual service .1521 .14051 .526
quick service -.1675 .14685 .490
* The mean difference is significant at the .05 level.
Table 4.35: ANOVA of segments and system quality.
111
Further analysis was then conducted to pinpoint which systems aspects differed
between the two segments (Table 4.34, p. 110). The ANOVA revealed differences with
two aspects of SQ: “dependability” (3.399 for casual service and 3.868 for quick service)
and “flexibility” (3.281 for casual service and 3.761 for quick service) (Table 4.36). In
addition, a significant difference was found between the quick and family segments on
systems “flexibility” (3.761 for quick service and 3.263 for family style).
Mean
Difference (I-J)
Std. Error Sig.
Dependent
Variable
(I) SEGMENT (J) SEGMENT
DEPEND casual service Quick service -.469** .1471 .005
family style -.285 .1770 .243
quick service casual service .469** .1471 .005
family style .183 .1850 .583
family style casual service .285 .1770 .243
quick service -.183 .1850 .583
FLEX casual service quick service -.480** .1585 .008
family style .018 .1900 .995
quick service casual service .480** .1585 .008
family style .498** .1991 .035
family style casual service -.018 .1900 .995
quick service -.498** .1991 .035
* The mean difference is significant at the .05 level.
Table 4.36: Tukey post hoc analysis of system quality differences.
Given the ANOVA post-hoc analysis, the quick service segment appears to
provide better systems than the casual service segments in regards to certain aspects of
system quality – dependability and flexibility. Regarding the family style segment, quick
service dominates in flexibility. There could be a number of reasonable explanations for
the differences. Perhaps it is an issue of resources.
112
Most of the QS restaurants with POS systems were franchisees of large national
chains (53%). Contrast this with the casual service restaurants and family style
restaurants, many of which are owned by independents (42% and 51%, respectively).
Certainly, the larger chains would have more resources to invest in the in-house
development of systems over the independent owners. Due to a lack of resources,
independents would be forced to purchase off-the-shelf software.
Descriptive data supports this theory. A greater proportion of quick service
restaurants (36.8%) used proprietary software than casual service restaurants (18%) and
family style (31.6%). Therefore, larger chains have been developing in-house software.
When the Spearman rho was calculated, however, the relationship between software type
(proprietary vs. off-the-shelf) and system dependability and flexibility was negligible in
both cases (-.030 and .044, respectively). Therefore, statistically speaking, software type
does have an impact on system quality.
Another possible explanation for the differences might be that the financial
success of the restaurant is related to system quality. Perhaps the more successful
restaurants purchase the more dependable and flexible systems. In order to test this
theory, and the Pearson r was calculated. A low association was found between financial
success and dependability (.224), but no relationship between financial success and
flexibility (.129). Therefore the Pearson r provides only limited support for the theory
that financial success is related to system quality.
ANOVA Assumptions
As with regression analysis, it is important for the ANOVA assumptions not to be
violated. For system, report, and DSS quality, the assumptions were not violated. Those
113
assumptions are (1) the groups were independent of each other, (2) the variances were
equal as evaluated by the Levene statistic (Table 4.37), and (3) the variables had normal
distributions.
Levene Df1 Df2 Sig.
DSS .578 2 191 .562
SQ .424 2 192 .655
RQ .981 2 192 .377
Table 4.37: Test of homogeneity of variances.
Manager Comments
Managers were asked to add comments about their computer use (“wish list,”
strengths, shortcomings, and barriers to improvements, etc.) to the survey. Seventy-two
managers responded, and a content analysis was performed. The following results were
reported:
Positives
Twenty-three managers wrote positive comments about their systems:
• Four managers rated their systems as dependable.
• Eight mangers described their systems as easy to use or user friendly.
• Four managers reported that although their systems were little outdated or a
bit slow, they did what they were supposed to do.
• Two managers rated the applications their systems offered as excellent.
• Five managers described their systems as “time savers.”
114
Shortcomings
Seventeen managers cited the following as system shortcomings:
• Two managers reported that their systems did not deliver what was promised
by the vendor.
• Five managers complained that they needed better equipment (i.e. printers).
• Two managers reported that their systems were not user-friendly or
customizable.
• Four managers complained that their systems had too many “bugs” or
glitches.
• Four managers complained that their systems were too slow.
Barriers
Seven managers expressed concerns over the following barriers to improvement:
• Six managers complained that it was too costly to upgrade or to purchase
restaurant-specific software.
• One manager needed more information to determine the “best” choice for his
restaurant.
• Three managers believed that their systems were underutilized due to a lack of
training.
Wish List
Managers had much to say about their “wish lists.” Thirty-nine managers wrote
about changes that they would like have made to their systems:
• Five managers, not having POS systems, wanted to add them to their
restaurants.
• Fourteen managers wanted to upgrade to better systems with better
applications such as bookkeeping (1), inventory systems (3), menu analysis
(2), food cost analysis (2), pricing analysis (1), sales detail (2), labor
forecasting and modeling (2), and theoretical food cost modeling (1).
115
• Nine managers wanted integrated systems where their POS systems
communicated with their back office systems and/or with headquarters.
• Two managers wanted application to improve service quality such as
customer loyalty systems and reservation systems.
• Three mangers wanted e-mail.
• Six managers wanted advanced technologies such as video monitoring (2),
voice command (1), touch screen (2), and a palm pilot-based server system
(1).
In conclusion, one recurring theme in this portion of the study is that
managers wanted more flexible, customizable, and integrated systems. Besides those
deficiencies, system quality and report quality appear to be well-rated. Applications
supporting DSS, however, appear to be a major weakness in overall IT quality. This
becomes more apparent as managers ask for more advanced software applications
such as labor and food cost modeling. A major concern, however, for nearly all
foodservice operators was the benefits derived from upgrading systems compared to
the costs of implementing them.
Summary
This study addressed six research questions. A summary of the findings for each
research questions is summarized below:
Research Question 1: What are the characteristics of the managers and restaurants in
this study?
The managers in this study were mostly male, college-educated, and in their late 30s.
They worked over 50 hours a week and had over 15 years of restaurant work experience.
116
The restaurants in this study were fairly evenly split between the full and quick service
sectors. Most had POS systems with sales between $500,000 and $5 million, and
considered their establishments successful. The average guest checks ranged from $2.50
to $75. Most quick service restaurants were franchises whereas most of the family and
casual service restaurants were either corporate-owned or managed by operating partners.
Research Question 2: What are the current usage trends of IT in the foodservice industry
today? Do offerings among segments differ?
A principal component analysis was performed and a six-factor model emerged
(p. 79). That model was revised into a five-factor classification scheme (p. 80). The five
factors were: cost analysis, sales & forecasting, administrative, service, and advanced
technologies. In addition, the “competitive ratings” of systems were found to be
normally distributed among the categories of: laggard, somewhat behind, middle of the
pack, close follower, and industry leader.
The most utilized applications were those that pertained to their day-to-day
operations. The least utilized were those that pertained to service quality and emerging
technologies. To statistically test whether or not systems were used equally by segments,
contingency theory was employed to test several hypotheses. The hypotheses and
findings are summarized in Table 4.38 (p. 117).
117
Hypotheses Results
H1a: Casual dining, family, and quick-service restaurants use systems
differently to best meet specific industry needs.
Accepted
H1b: Chains will utilize more software applications than independently
owned restaurants.
Accepted
H1c: There is a positive relationship between sales volume and
application use.
Rejected
H1d: There is a positive relationship between perceived level of
financial success and the number of computer applications used
by restaurant managers.
Accepted
Table 4.38: Results of hypothesis testing – segments.
Research Question 3: What are the current levels and quality of training and support
provided to restaurants managers?
Most managers received some form of training on their systems, albeit, most often
as “on-the-job” training. Technical support was quite comprehensive with more than 50%
of the restaurants receiving support “24/7.” Two hypotheses were tested in this part of
the study. The hypotheses and findings are summarized in Table 4.39.
Hypotheses Results
H2a: There is a positive relationship between types of training and training quality
ratings.
Accepted
H2b: There is a positive relationship between the level of the “help desk” support
and support quality ratings.
Accepted
Table 4.39: Results of hypothesis testing: training and support.
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Research Question 4: What are the relationships between the variables in the research
model (p. 48) and decision-making support satisfaction?
The relationships between the independent variables in the RMIS model were
linear with the dependent variable, DSS. The strongest associations were between the
independent variables, TOTAPP, SQ, RQ, FIT, TQUAL, and DSS. The weakest
association was between PROF and DSS. The hypotheses and findings are summarized in
Table 4.40.
Hypotheses Results
H3a: There is a positive relationship between system use and DSS. Accepted
H3b: There is a positive relationship between perceived system quality and DSS. Accepted
H3c: There is a positive relationship between perceived report quality and DSS. Accepted
H3d: There is a positive relationship between perceived system fit and DSS. Accepted
H3e: There is a positive relationship between perceived user proficiency and
DSS.
Accepted
H3f: There is a positive relationship between perceived levels of IT training
quality and DSS.
Accepted
H3g: There is a positive relationship between IT support and DSS Accepted
H3h: There is a relationship between industry segment and DSS. Rejected
Table 4.40: Results of hypothesis testing: independent variables and DSS.
Research Question 5: To what extent can variability in the dependent variable, DSS, be
explained by the independent variable set: RQ, SQ, TQ, HQ, TOTAPP, DUMBSEG, and
PROF?
The adjusted R2 for this study’s theoretical model was .451. The coefficients
TOTAPP, SQ, RQ, and TQ were significant at the 1% levels. HQ and DUMSEG did not
contribute significantly to the model. Also, the regression assumptions of autocorrelation,
homoscedasticity, and multicollinarity were not violated. The above tests support the
adequacy of the regression model.
119
Sensitivity analysis was performed on the regression equation where FIT was
entered as the dependent variable instead of DSS. FIT appeared to be a separate
construct from DSS. The antecedents to FIT were: SQ, RQ, PROF, DUMCHAIN, and
RATE. The R2 for this model jumped to .608. Service and report quality, user
proficiency, ownership, and competitive rating of the systems all significantly contributed
to the regression equation. FIT appears to be a broader measure of system success and
might be a more appropriate outcome than DSS for the foodservice industry.
The hypothesis and related findings are summarized in Table 4.41.
Hypothesis Results
H4a: The proportion of variability of DSS can be explained by system usage, quality
of training and support, system quality, report quality, user competency, and
segment.
Accepted
H4b: DSS and FIT are two separate constructs with different sets of
antecedents that explain variability.
Accepted
Table 4.41: Results of hypothesis testing: regression equation.
Research Question 6: What are the strongest and weakest aspects of the systems offered
today? What did managers have to say about IT strengths and weaknesses?
Managers appeared to be pleased with system and report quality except in the
areas of system integration, system flexibility, and report customization. The overall
ratings for DSS were lower than those for SQ and RQ. Specifically, the software
applications did not “increase the speed at which managers made decisions” nor “help
them to anticipate problem areas.”
Narrative comments rated primary system strengths as “dependability” and “easeof-
use.” Slowness, inadequate equipment, and “bugs” were listed as chief problems.
120
Furthermore, managers expressed concerns about the high cost of upgrading, and wanted
more training to better utilize existing systems. Lastly, managers expressed a desire to
upgrade to better systems with better software applications.
Appendix – Regression Assumptions
The following are tests of the regression assumptions for the model presented on
Table 4.28 (p. 103).
Assumption 1: The residuals are independent and errors associated with any observation
are not correlated with the error with any other observation.
(i) The Durbin Watson statistic is used to evaluate the assumption that the
residuals are not correlated with each other. The Durbin Watson ranges from
1-4 with no autocorrelation = 2.0.
(ii) The Durbin Watson for the full model is 1.971 (Table 4.26, p. 102). This is
close to the ideal of 2.0, therefore, assumption #1 is not violated.
Assumption 2: Residuals have a mean = 0 and a standard deviation =1.
(i) To assess assumption #2, the means for the residual statistics is evaluated. The
mean should be =0 and the standard deviation =1.
(ii) According to the residual values on Table 4.42 (p. 120), the mean = 0, and
standard deviation =.981 (which is close to 1.0). Assumption #2 is not
violated.
Minimum Maximum Mean Std. Deviation N
Predicted Value 1.4305 4.4112 3.0988 .55623 186
Std. Predicted Value -2.999 2.359 .000 1.000 186
Residual -1.93 1.2951 .0000 .61379 186
Std. Residual -3.084 2.070 .000 .981 186
a Dependent Variable: DSS
Table 4.42: Residuals Statistics for regression equation.
121
Assumption 3: The residuals are normally distributed.
(i) To assess assumption #3, a histogram is used to see if the residuals are
normally distributed, and a normal probability plot of residuals to see if the
residuals fit with the diagonal.
(ii) The histogram on Figure 4.7 appears to be normally distributed. It is slightly
skewed to the right. The skewing, however, has no effect on the estimation of
the parameters (intercept and partial regression coefficients) of the regression
equation. In addition, regression analysis is quite robust for violations of the
normality assumption (Wambrod, 2000).The residuals also seem to fit along
the line of the normal probability plot (Figure 4.8, p. 122). Thus, assumption
#3 is not violated.
Regression Standardized Residual
2.00
1.50
1.00
.50
0.00
-.50
-1.00
-1.50
-2.00
-2.50
-3.00
Histogram
Dependent Variable: DSS
Frequency
30
20
10
0
Std. Dev = .98
Mean = 0.00
N = 186.00
Figure 4.7: Plotting of residuals.
122
Normal P-P Plot of Regression Stan
Dependent Variable: DSS
Observed Cum Prob
1.00 .75 .50 .25 0.00
Expected Cum Prob
1.00
.75
.50
.25
0.00
Figure 4.8: Linear relationship of residuals.
Assumption #4: The residuals have constant variance (homoscedasticity).
(i) To assess assumption #4, the plot of residuals with predicted Y is evaluated
for constant variance. If constant variance exists, a horizontal band can be
drawn around the data.
(ii) The scatter plot on Figure 4.9 does not seem to reveal any clustering. A
horizontal band can be drawn around the data. Assumption 4 has not been
violated.
Standardized Residual
3 2 1 0 -1 -2 -3 -4
Standardized Predicted Value
3
2
1
0
-1
-2
-3
-4
Figure 4.9: Test for homoscedasticity.
123
Assumption #5: The residuals are not correlated with the independent variables.
(i) To assess assumption #5, the Pearson Correlation table of the residuals and
independent variables is used.
(ii) The r values in the Table 4.43 are close to 0. This supports the assumption
that the residuals area not correlated with the independent variable. Therefore,
assumption 5 has not been violated.
Correlations
Pearson Residual
SQ .053
RQ .029
TOTAPP .004
PROF .031
DUMSEG -.003
TQUAL -.002
HQUAL -.024
Table 4.43: Correlations of r values.
124
CHAPTER 5
CONCLUSION
This chapter presents the concluding remarks for this study. It presents a
discussion of this study’s contributions to the foodservice and MIS fields, limitations, and
implications for future research.
Theoretical Contributions
This study makes several useful contributions to the fields of information systems
and hospitality research.
This study supports the notion that systems are developed in stages (Gibson, et.
al., 1974). Currently, foodservice firms seem to be either initiating systems or expanding
their systems to offer better functionality. Ellison and Mann (2000) described the
foodservice adoption phases as clerical, integrated administrative and tactical with more
restaurants in the clerical stage (p. 18). Differing from the Ellison and Mann findings, this
study found that the most commonly used applications (50% or more usage by all
segments) were equally distributed among all the processes. This study also demonstrated
that foodservice establishments have moved beyond the findings of the Chien 1998 study.
Independents are no longer limited to just using office and accounting products, but are
also using food and labor cost analyses, sales forecasts, server performance evaluations,
menu analysis, and e-mail on a regular basis.
125
Second, this study strongly supports the idea that systems should be aligned with
user needs (Rockart, 1979). Historically, this approach was highly successful with
planning systems for use by senior and middle managers (Munro, et. al., 1980). This
study suggests that this approach also works for operational managers. Given the adjusted
r2 of .608 for FIT, it appears as though focusing on the antecedents related to fit can help
practitioners develop more successful systems.
Third, chains were reported to have better fitting systems than independents. The
most likely reason for this finding is that the chains have the human and financial
resources to devote to develop better fitting systems. This supports the idea that IT
departmental competence is also related to system success (Ross, et. al., 1996). In
addition, there was a substantial relationship (.667) between fit and the competitive
ranking of the system. The competitive ranking of the system (industry leader, close
follower, middle of the pack, behind, and laggard) implies that there is competitive value
to systems (Porter, 1980). Therefore, systems might play a strategic role in a company’s
success.
Lastly, this study provided partial support for systems implementation theory.
Most studies support the idea that support provided during and after implementation is
associated with system success (Rivard, 1984; Magal, et. al, 1988; Magal, 1991). This
study found training related to system success, but not support. The availability (hours) of
support provided by the “help desk” did relate to support quality ratings, but not to
system success. The latter finding, however, might change if support was measured in a
different way.
126
Methodological Contributions
A significant methodological contribution of this study was the development and
testing of the model of system success in the foodservice industry. This study relied upon
regression analysis and sensitivity analysis to predict system success. All of the
regression assumptions were met, and several interesting regression models emerged.
The first regression model assessed the fit of the theoretical model to the
theoretical antecedents (p. 48). Incorporated in the model were elements of system
success model as purported by DeLone and McLean (1992). In particular, the model of
RMIS success, exhibited a fit of .450. Four antecedents – system use, system quality,
report quality, and training quality –were significant, whereas three antecedents – support
quality, user competence, and segment – were insignificant.
Sensitivity analysis was used on the regression analysis, and the concept of FIT
emerged as a potentially important dependent variable. This yielded an unusually high
adjusted r2 of .608. Five antecedents – system quality, report quality, user proficiency,
competitive rating, and ownership type –were significant, whereas three antecedents –
system use, training quality, and segment – were insignificant. As discussed in Chapter
4, the new measure, user competency, was significant. This model provides an alternative
to the McLean and DeLone model as a way to measure system success. The high adjusted
r2 of .608 implies that FIT might be a better measure of system success than decisionmaking
satisfaction for certain industries or levels of management.
Lastly, certain constructs used in prior studies were replicated in this study–
system quality, report quality, and decision-making satisfaction. The reliability of all of
these constructs was strong with Cronbach’s alphas of over .80 (p. 60). In addition,
127
construct validity was strong for all of the constructs with all their measures loading as
significant (p. 59).
Limitations
While this study’s contributions are important, discussion of its limitations is also
necessary. With the limitations in mind, this study may be more fully appraised and
future research more clearly directed.
First, this study used a localized mailing list with a 14.1% response rate. Despite
the quality of the mailing lists used, follow-ups revealed that many managers did not
receive the survey or discarded it as “junk mail.” This limits generalizability to the local
population. Also, since a random national sample was not used, generalizing to the
national population is problematic. Since a diversity of chains and independents were
represented in this sample, the study could be considered somewhat representative of the
national restaurant population as a whole. Future studies might consider using a random
national sample rather than a local sample.
Second, Likert scales were used to measure the perceptions of the participants. As
such, the measures were subject to the participant’s interpretation of the questions. Pilot
studies were used in order to minimize these problems. The report of internal
consistency, Cronbach’s alpha, was high meaning that the construct measures were
reliable. Interpretation problems, nonetheless, were noted on a few variables. In these
cases, either phone calls were made to respondents or secondary research was used to
clarify problem areas. Future studies need to take care in asking certain questions such as
ownership type, number of units in the chain, and who developed the POS system.
128
Third, Likert scale measurements can lead to response bias. Respondents do not
always answer honestly and may avoid the extreme ends of the scale.
Finally, caution needs to be taken when interpreting findings related to ANOVA
when the assumption of equal variances is violated. In those instances, the Tahame post
hoc analysis method (unequal variances assumed) was used to analyze the data. The
Tahame method may over or understate alpha.
Suggestions for Future Research
There are several areas that future research can improve upon this study.
First, the measure of support quality appears to be inadequate. This study used the
comprehensiveness of “help desk” hours to measure support quality. Manager comments,
however, suggest that it’s the “quality of the responses” from support personnel that
defines support quality.
Second, a question should be added to the questionnaire asking managers to rate
their overall satisfaction with the systems. This question was not on the survey because
FIT was thought to be a measure of overall satisfaction. During the data analysis,
however, FIT was deemed to be a separate construct, not an antecedent to decisionmaking
satisfaction.
Third, the measure of decision-making satisfaction (DSS), although acceptable,
had the weakest construct validity. The questions used to measure DSS should be reevaluated,
and perhaps molded specifically for the foodservice industry.
Finally, future testing of the dependent variable, FIT, would be educational. The
focus of this study was on the dependent variable, DSS. A future study should focus on
the dependent variable, FIT.
129
Implications for Industry
These findings are especially relevant for CIOs, software developers, and vendors
seeking ways to improve systems development.
This study clearly shows that one system does not fit all. Different industry
segments utilized systems differently as discussed in Ch. 4 (Table 4.12, p. 80).
Furthermore, financially successful restaurants utilized systems differently from
unsuccessful restaurants. Hence, practitioners can gather information from this study
regarding system success, segments, and fit to develop better systems.
From a practitioner perspective, this study also supports the importance of system
quality and report quality, both significant antecedents for DSS and FIT. Without those
two foundational elements, systems would probably receive low success ratings. In
addition, training quality was related to DSS. Therefore, companies wanting successful
implementation should emphasize in providing adequate IT training programs for
managers.
Lastly, manager comments give insight into managerial needs. Managers want
more flexible, integrated systems with customizable reports. They also called for more
sophisticated applications that will help them more effectively manage their restaurants.
Finally, managers wanted training to better utilize existing systems.
Concluding Remarks
This study makes significant progress in our understanding of the use of
management information systems in the foodservice industry. It provides readers with a
volume of information regarding the current use of systems, levels of training and
support, antecedents to system success, and an evaluation of IT strengths and
130
weaknesses. It is imperative that practitioners understand the complex, contextual
environment in which these systems exist in order to plan and develop the successful
systems.
131
APPENDIX A
Survey Instrument
132
Restaurant
Technology Survey
Research Sponsored by:
The Central Ohio Restaurant Association,
Otterbein College,
&
The Ohio State University
Hospitality Management Program
Columbus, OH
133
This survey is developed to investigate your satisfaction with technology use at your restaurant. Marsha
Huber, a doctoral student in the Hospitality Management Program at the Ohio State University, is
administering the survey.
All answers are confidential. You will not be identified. The researcher assures your anonymity.
1. Do you use a point-of-sale (POS) system? □Yes □No
If yes, does your system offer:
• Automated inventory ordering? □Yes □No
• Automated payroll processing? □Yes □No
2. Do you use a computer in your back office (or at your home office?)
□Yes □No
If you answered “NO” to Questions 1 and 2, go to Question 35
3. Who developed your POS system?
□ Developed and purchased from headquarters
□ Purchased from a vendor (brand name: __________________________)
□ Leased from an applications service provider (brand name: ___________)
□ Do not know
□ Other (please describe: _______________________________________)
134
4. What types of technical training did you receive on how to use your
computer systems (check all that apply):
□ Special seminars
□ On-the-job training
□ Video or computer-based training
□ Self-trained (taught myself)
□ No technical training was provided
□ Other (please describe) _________________
5. Rate the quality of the training you received on your computer systems:
□ Inadequate □ Fair □ Good □ Excellent
6. Which statement best describes the availability of your “help desk” or
technical support (check one):
□ During regular business hours (9 to 5)
□ All hours that restaurants are open
□ 24/7 (24 hours a day & 7 days a week)
□ No help desk is provided
□ Other (please describe) _________________
7. Rate the quality of the “help desk” (technical support) you have received on
your computer systems:
□ Inadequate □ Fair □ Good □ Excellent
8. Your comments on training and support:
135
9. Do you use the computer to analyze the following? (check all that apply):
□ Food costs
□ Labor costs (productivity)
□ Sales results (mix, average guest check, customer counts)
□ Variances (budget vs. actual results)
□ Server performance
10. Do you use the computer to forecast the following? (check all that apply):
□ Sales forecasts
□ Food production schedules
□ Labor schedules
11. Do you use the computer to track the following? (check all that apply):
□ Inventory
□ Customer history, loyalty, and/or complaints
□ Vendor prices
□ Service delivery times
12. Do you use the computer for administrative purposes: (check all that apply):
□ Word processing/spreadsheets
□ Bookkeeping and financial reporting
□ Training employees
□ Menu or recipe development
13. Do you use the computer for communication purposes? (check all that apply):
□ E-mail
□ Automated pager/cell phone notification of problems
□ Video monitoring from remote locations
136
Rate the quality of your computer systems: Poor Fair Excellent
14. Dependability of system (frequency of crashes) 1 2 3 4 5
15. Ease-of-use (front and back of house) 1 2 3 4 5
16. Level of integration (between modules, reducing
duplication of efforts)
1 2 3 4 5
17. Responsiveness (speed, easy to access) 1 2 3 4 5
18. Degree of flexibility of systems (ability to update, make
changes)
1 2 3 4 5
19. Timeliness of reports 1 2 3 4 5
20. Accuracy of reports 1 2 3 4 5
21. Readability of reports 1 2 3 4 5
22. Customizability of reports 1 2 3 4 5
23. Comprehensiveness of reports 1 2 3 4 5
25. Rate your computer proficiency skills that you use on the job:
□ No skill
□ Novice
skill
□ Moderate
skill
□ Highly
proficient
□ Mastery skill
26. How would you describe your computer systems’ fit to your needs as a manager?
□
Poor fit
□
Below average fit
□
Moderate fit
□
Good fit
□
Perfect fit
24. How would you describe your firm’s use of computer technology at your restaurant?
□ Laggard □ Somewhat
behind
□ Middle of the pack □ Close
follower
□ Industry
leader
137
35. How would you rate the financial success of your restaurant?
□
Not successful
□
Minimally successful
(falls below expectations
and competitors)
□
Successful
(meets expectations)
□
Highly successful
(exceeds expectations
and competitors)
Please give us information about yourself
Average number of hours you work each week? __________ hours per week
Your highest level of education:
□High School □Some College □Associates □Bachelors □Graduate Degree
Total number of years worked in the foodservice industry: __________ years
Your age is: Your gender is: □Male □Female
Your job title is:
Our computer systems help me to:
Not
at all
To a
moderate
extent
To a
great
extent
27. Evaluate operational efficiency 1 2 3 4 5
28. Dig behind the numbers 1 2 3 4 5
29. Track progress toward goals 1 2 3 4 5
30. Anticipate problem areas 1 2 3 4 5
31. Takes the complexity out of my job 1 2 3 4 5
32. Keeps me close to “what is going on” in the
restaurant
1 2 3 4 5
33. Increases the speed at which I make decisions 1 2 3 4 5
34. Improves the quality of my decisions 1 2 3 4 5
138
Please give us information about your restaurant
Ownership: □Independent □Multi-unit (more then two units)
# of units operated by your company: _______ units
Are you a franchisee or franchisor? □Franchisor □Franchisee □Neither
Segment: □Full-service □Quick service □ Other: _______________
Average guest check: $
Average annual sales volume: □Under $100,000 □$100 – $250,000
□$250 – $500,000 □$500 – $1 million □$1m – $2m □$2m– $5m □over $5m
Number of full and part-time employees currently employed: _______ employees
Please add your comments about computer use at your restaurant (“wish list”,
strengths, shortcomings, barriers to improvement, etc.):
139
Thank you for your support!
If you would like a copy of the results of this study,
please enclose your card
Code ______
Please return the questionnaire by September 30, 2002
Please return in the enclosed envelope or mail to:
Dr. Thomas George / Marsha Huber
Hospitality Management Program
The Ohio State University
1787 Neil Avenue, Room 315F
Columbus, OH 43210-1295
or
FAX to (614) 823-1014
140
APPENDIX B
Cover Letters
141
Date
Dear General Manager,
We are supporting a study being conducted by the Ohio State University (OSU) on the use of
information technology by restaurant managers. During the first week of August, you will be
receiving a survey and/or phone call from a research associate from OSU asking you to
participate in the study. We will call you during the afternoon hours of 2-4pm.
This study is designed specifically for restaurant managers. The survey takes less than 5 minutes
for managers to complete. Results from this study will enable restaurant owners, like yourself, or
technology directors to better select/design information technology for your restaurants.
Participating firms will be sent an Executive Summary at the completion of the project. You
will be richly rewarded by access to information that is up-to-date and extremely relevant to the
work that you do. In addition, the results will be presented at one of our CORA meetings.
Your managers’ answers are completely confidential, and your company will not be identified.
Once the completed questionnaire is returned, your company’s name will be deleted from the
mailing list, and never connected to the survey answers in any way. Your name will not be sold or
placed on any other mailing list.
If you have any questions or comments about this study, our researchers would be happy to speak
with you. They can be contacted at 740-965-8787 (local for Columbus and southern Delaware
County), or you can e-mail us at [email protected].
Thank you very much for helping us with this important study.
R. Thomas George, MBA, Ed. D. Marsha Huber, CPA, MBA
OSU Associate Professor of Hospitality OSU Research Associate
Kim Bartley
CENTRAL OHIO
RESTAURANT
ASSOCIATION
259 Garfield Avenue
London, OH 43140
Phone (877) 274-CORA
Fax (740) 852-5399
E-mail [email protected]
Web Site City Guides by Citysearch
142
President, Central Ohio Restaurant Association
TO: General Manager
FROM: The Ohio State University (OSU) and the
Central Ohio Restaurant Association (CORA)
DATE: August 6, 2002
RE: Technology research
Please help us complete this important and timely research by filling out the enclosed survey. The
details of this research are:
• The research project is sponsored by the Hospitality Program at the Ohio State University
and CORA.
• We are investigating the utilization of computer systems in the foodservice industry by
general managers.
• Results from this study will enable restaurant owners and technology directors to better
select and design information technology for restaurants.
• The survey takes less than 5 minutes to fill out.
• If you enclose your business card, we will send you a copy of the results of this study.
You can use the results to compare your restaurant to the other Ohio restaurants that
participated in this survey. Results will also be presented at a CORA meeting.
• Your answers are completely confidential and will be released only in summary form in
which no one individual answer can be identified. Your name will not be placed on any
other mailing list or sold.
• The survey is voluntary and for academic research. The research director is a doctoral
student at Ohio State.
• If you have any questions or comments, please contact the research director by calling
740-965-8787 (local to Columbus) or e-mail her me at [email protected].
Thank you very much for helping us with this important study. Please return the survey in the
enclosed return reply envelope or fax the survey to 614-823-1014.
Dr. R. Thomas George Marsha Huber Kim Bartley
OSU Associate Professor OSU Research Director CORA President
143
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