international chrie 2003 proceedings manualm3.ithq.qc.ca/collection/00000144.pdf · preface this...

Preface

This Proceedings features 81 refereed papers of the 2003 Annual International CHRIE Conference held in PalmSprings, California, August 6-10, 2003. The papers were selected from a pool of 136 papers submitted from 15countries/regions. Every paper was double blind reviewed by at least two reviewers who are specialized in thecontent area.

I am honored to be the Editor of this Proceedings and the Chairman of the Refereed Paper Review Committee forthis year’s I-CHRIE Conference. I would like to take this opportunity to express my heartfelt thanks to all theAssociate Editors of the Proceedings. They have worked tirelessly to make the Proceedings academicallyexcellent. Without their support and excellent job, the Proceedings would not be possible. I also would like tothank all the paper reviewers for their time in reviewing these papers. Without their effort, support, and carefulcritique of the researchers’ work, we would not be able to advance this discipline through research.

I would like to thank the School of Hotel and Restaurant Administration and the College of Human EnvironmentalSciences at Oklahoma State University for the support I received to take this responsibility as the Chairman andEditor of this year’s Refereed Paper Review Committee and the conference Proceedings. Without the support, Iwould not be able to provide the services to the I-CHRIE.

In addition, I would like to thank my graduate assistant, Holly Im, who contributed many hours of hard work toformat the Proceedings.

Last, but not least, my thanks to all the authors who worked so hard on your research and have come so far to betogether and to share your research findings with each other.

Congratulations and best wishes!

Hailin Qu, Ph.D.

Professor & William E. Davis Distinguished ChairEditor of I-CHRIE ProceedingsChairman of I-CHIRE Refereed Paper Review CommitteeSchool of Hotel and Restaurant AdministrationOklahoma State University

Please Note: Each paper’s authorship has been cross checked and /or may have beenchanged by I-CHRIE according to the lead authors’ original submissions.

Editor

Hailin QuOklahoma State University

Associate Editors

Bart BartlettThe Pennsylvania Sate University

Liping CaiPurdue University

Evangelos ChristouTechnological Educational Institute of

Thessaloniki

Raymond FerreiraGeorge State University

Richard GhiselliPurdue University

Shirley GilmoreIowa State University

Kathryn HashimotoUniversity of New Orleans

Joseph HollandUniversity of Wisconsin, Stout

Cathy H.C. HsuThe Hong Kong Polytechnic University

Curtis LoveUniversity of Nevada, Las Vegas

Dan MountThe Pennsylvania Sate University

Warren SacklerRochester Institute of Technology

Jack SamuelsMontclair State University

Linda SheaUniversity of Massachusetts, Amherst

Atul SheelUniversity of Massachusetts, Amherst

Reviewers

Anthony AgbehFerris State University

Cihan CobanogluUniversity of Delaware

Claudia GreenPace University

Stanley AtkinsonUniversity of Central Florida

Evangelos ChristouTechnological Educational Institute of

Thessaloniki

Tom GeorgeThe Ohio State University

Billy BaiUniversity of Nevada, Las Vegas

Shu T. ColeUniversity of Missouri

Rich GhiselliPurdue University

Martha BarclayWestern Illinois University

Chris CooperThe University of Queensland

Shirley GilmoreIowa State University

Bart BartlettThe Pennsylvania State University

Dave CranagePennsylvania State University

Nancy GravesUniversity of Houston

Thomas BauerHong Kong Polytechnic University

Darla DamonteCoastal Carolina University

Robert GriffinUniversity of Massachusetts, Amherst

Tom BaumUniversity of Strathclyde

Fred DeMiccoUniversity of Delaware

James L. GrovesUniversity of Missouri – Columbia

Patrick BeachWilliam Rainey Harper College

Jeff ElsworthMichigan State University

Zheng GuUniversity of Nevada, Las Vegas

Jeff BeckMichigan State University

Donna FariaJohnson & Wales University

Cathy GustafsonUniversity of South Carolina

William BeckerChristopher Newport University

Angela FarrarUniversity of Nevada, Las Vegas

Sunny HamUniversity of Kentucky

Shane BlumTexas Tech University

Ruomei FengPurdue University

Erna HarrisJohnson & Wales University

Carl BorchgrevinkPurdue University

Jeffrey FernstenUniversity of Massachusetts, Amherst

Kathy HashimotoUniversity of New Orleans

Kathleen Pearl BrewerUniversity of Nevada, Las Vegas

Raymond FerreiraGeorge State University

Brett HortonJames Madison University

Liping CaiPurdue University

Reed FisherJohnson State College

Cathy H.C. HsuThe Hong Kong Polytechnic

University

Debby CannonGeorgia State University

Judy K. FlohrUniversity of Massachusetts, Amherst

Bo HuOklahoma State University

Rachel ChenThe University of Tennessee

Andrew J. FrewQueen Margaret University College

Clark HuTemple University

Po-Ju ChenUniversity of Central Florida

William FryeNiagara University

Elizabeth M. InesonManchester Metropolitan University

Patricia JanesCentral Michigan University

Lynda MartinOklahoma State University

Richard PattersonWestern Kentucky University

Shawn JangKansas State University

Anna MattilaThe Pennsylvania State University

Carl PfaffenbergUniversity of Tennessee

Peter JonesUniversity of Surrey

Marvel MaunderSouthwest Missouri State University

Ali PooraniUniversity of Delaware

Jay KandampullyOhio State University

Audrey C. McCoolUniversity of Nevada, Las Vegas

Arun Jai PrakashFlorida International University

Soo KangMorehead State University

Brian MillerUniversity of Delaware

Allen ReichNorth Arizona University

Lisa KennonUniversity of North Texas

Juline MillsUniversity of Delaware

Dennis ReynoldsCornell University

Hyunjoon KimUniversity of Hawaii at Manoa

Patrick MoreoOklahoma State University

Deborah RobbeGeorgia State University

Woo Gon KimOklahoma State University

Dan MountThe Pennsylvania State University

Chris RobertsUniversity of Massachusetts, Amherst

Sheryl KlinePurdue University

Ken MyersUniversity of Minnesota-Crookston

Warren SacklerRochester Institute of Technology

Khoon Y. KohCentral Connecticut State University

Karthik NamasivayamThe Pennsylvania State University

William SamenfinkEndicott College

Rose KrebsUniversity of Central Florida

Doug NelsonPurdue University

Gail SammonsUniversity of Nevada, Las Vegas

Francis A. KwansaUniversity of Delaware

Haemoon OhIowa State University

Jack SamuelsMontclair State University

Joe LaLopaPurdue University

Robert O'HalloranUniversity of Memphis

Barbara ScheuleKent State University

Terry LamThe Hong Kong Polytechnic

University

John O'NeillThe Pennsylvania State University

Raymond S. SchmidgallMichigan State University

Kyung LeeKansas State University

Radesh PalakurthiSan Jose State University

Linda SheaUniversity of Massachusetts, Amherst

Li-Chun LinMontclair State University

Ravi PanditSouthern New Hampshire University

Atul SheelUniversity of Massachusetts, Amherst

Curtis LoveUniversity of Nevada, Las Vegas

Alex ParaskevasOxford Brookes University

Patti ShockUniversity of Nevada, Las Vegas

Keith MandabachNew Mexico State University

H.G. ParsaOhio State University

Stowe ShoemakerUniversity of Nevada, Las Vegas

Marianna SigalaUniversity of Strathclyde

Amy TanThe Hong Kong Polytechnic

University

Arjan van't VeerDutch National (State) Lottery

A.J. SinghMichigan State University

Mustafa TepeciMersin University

Sandra WatsonNapier University

Amy (Siu-Jan) SoPurdue University

Mark TestaSan Diego State University

John WilliamsKansas State University

Larry StalcupGeorgia Southern University

Emery TrowbridgeUniversity of New Hampshire

Kara WolfeNorth Dakota State University

Sandy StrickUniversity of South Carolina

Paris TsartasUniversity of the Aegean

Ronnie YehOklahoma State University

Siriporn SujithamrakBlack Hills State University

Ted TsukaharaSt. Mary's College

Alex SusskindCornell University

Thomas VandykeUniversity of North Carolina,

Greensboro

2003 Annual International CHRIE Conference and Exposition Proceedings

i

Table of Contents

A COMPARATIVE STUDY OF RESIDENTS’ PRE- AND POST-PERCEPTION TOWARD CASINODEVELOPMENT: A STRUCTURAL EQUATION MODELING APPROACH ....................................................................1By Ki-Joon Back and Choong-Ki Lee

E-RELATIONSHIP MARKETING: AN APPLICATION TO HOTEL WEBSITE DEVELOPMENT..................................8By Billy Bai, SooCheong (Shawn) Jang, and Clark Hu

FUTURE EVENTS AND THEIR EMPACT ON THE U.S. HOSPITALITY INDUSTRY A DELPHI STUDY TOPREDICT THE ROLE OF HUMAN RESOURCES, OPERATIONS, INFORMATION TECHNOLOGY,MARKETING, AND FINANCIAL MANAGEMENT IN THE YEAR 2007.........................................................................13By Jeffrey A. Beck, Jeffrey D. Elsworth, Arjun Singh, and Bonnie J. Knutson

LONGITUDINAL ANALYSIS OF ONLINE TRAVEL INFORMATION SEARCH BEHAVIOR: 1995-2000 ................20By Srikanth Beldona, Sheryl Kline and Alastair Morrison

PERSONALITY AND JOB PERFORMANCE IN FOOD AND BEVERAGE SERVICE PROVISION.............................27By Elizabeth Ineson and Androniki Bayliss

WORK MOTIVATION CORRELATES OF PERSONALITY IN A HOSPITALITY CONTEXT......................................32By Elizabeth Ineson and Androniki Bayliss

A COMPARISON OF EDUCATIONAL DELIVERY TECHNIQUES IN A FOODSERVICE TRAININGENVIRONMENT .......................................................................................................................................................................37By Richard Ghiselli and Carl Behnke

FOREIGN LANGUAGES AND THE HOSPITALITY CURRICULUM...............................................................................42By Matt A. Casado

IMPORTANT ELEMENTS OF HOTEL-MEETING PLANNER CONTRACTS: A PILOT STUDY................................47By Harsha Chacko and George Fenich

SIX SIGMA: HERE TO STAY OR JUST ANOTHER FAD?.................................................................................................52By Harsha E. Chacko

TESTING AND DEVELOPING THE ENVIRONMENT RISK CONSTRUCTIN HOSPITALITY STRATEGY RESEARCH.........................................................................................................................57By Prakash K. Chathoth and Michael D. Olsen

CO-ALIGNMENT BETWEEN ENVIRONMENT RISK, CORPORATE STRATEGY, CAPITAL STRUCTURE,AND FIRM PERFORMANCE: AN EMPIRICAL INVESTIGATION OF RESTAURANT FIRMS...................................63By Prakash K. Chathoth and Michael D. Olsen

INTEGRATING THE DISABLED INTO THE WORK FORCE: a STUDY OF EMPLOYING PEOPLE WITHDISABILITIES IN FOODSERVICE INDUSTRY...................................................................................................................69By Christina Geng-qing Chi and Hailin Qu

EXAMINING THE ROLE OF PRIOR KNOWLEDGE IN TOURISTS’ INFORMATION SEARCHBEHAVIOR ................................................................................................................................................................................75By Mi-Hea Cho

MEASURING THE IMPACT OF HUMAN RESOURCE MANAGEMENT PRACTICESONORGANIZATIONAL PERFORMANCE..................................................................................................................................80By SeongHee Cho and Karl J. Mayer


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INTERNET TOOLS AND APPLICATIONS IN TOURISM AND HOSPITALITY EDUCATION:A REALITY CHECK OF EDUCATORS IN EUROPE...........................................................................................................85By Evangelos Christou and Marianna Sigala

ADOPTION OF E-SHOPPING FOR AIR TRAVEL SERVICES: AN INVESTIGATION OFCONSUMERS’ PERCEPTIONS...............................................................................................................................................92By Evangelos Christou and Panagiotis Kasianidis

EMPLOYEE SATISFACTION: IMPACT ON ORGANIZATIONAL SUCCESS ..............................................................100By Joan Maie Clay and Radesh Palakurthi

INFORMED CHOICE IMPROVES CUSTOMER LOYALTY THROUGH EMPOWERMENTAND RESPECT FOR OPENNESS .........................................................................................................................................105By David Cranage

AN INITIAL INVESTIGATION OF FIRM SIZE AND DEBT USE BY SMALL RESTAURANT FIRMS ....................111By Michael C. Dalbor and Arun Upneja

TECH TRENDS: AN EXPLORATION OF THE FUTURE OF HOSPITALITY AND TOURISMEDUCATION............................................................................................................................................................................116By Juline E. Mills and Alecia Douglas

ATTITUDE TO LONG-HAUL PLEASURE TRAVEL NEED RECOGNITION:TRAVELERS, INTENDERS, OR NONINTENDERS...........................................................................................................121By Ruomei Feng and Liping Cai

INVESTIGATION OF THE CHANGING PARADIGM IN CASINO DEVELOPMENT..................................................126By George G. Fenich and Kathryn Hashimoto

NEW COURSE DEVELOPMENT: FROM IDEA TO SYLLABUS THE CASE OFA CROSS-DISCIPLINARY UNIVERSITY COURSE..........................................................................................................128By Gabor Forgacs

LEADER MEMBER EXCHANGE QUALITY: AN EMPIRICAL INVESTIGATION IN RESTAURANTS..................133By R. Thomas George and Murat Hancer

ABORIGINAL EMPLOYMENT PRACTICES IN CANADA’S GAMING INDUSTRY ..................................................139By Stefan Groschl

TRAVELERS’ PRIOR KNOWLEDGE AND THEIR INFORMATION SEARCH BEHAVIOR......................................143By Dogan Gursoy and Ken W. McCleary

ASSESSMENT OF ADMISSION CRITERIA FOR PREDICTING HOTEL MANAGEMENT STUDENTS’FIRST – YEAR ACADEMIC PERFORAMCNE – A CASE STUDY..................................................................................149By Yu-Chin (Jerrie) Hsieh and Bo Hu

THE IMPACT OF DESTINATION INVOLVEMENT ON TRAVELERS’ REVISIT INTENTIONS ..............................153By Bo Hu

RESTAURANT MANAGEMENT INFORMATION SYSTEMS: ASSESSMENT OF THE CONTINGENTUSE OF SOFTWARE APPLICATIONS IN THE RESTAURANT INDUSTRY................................................................158By Marsha M. Huber and R. Thomas George

A CONSUMER DEMAND MODEL FOR TRAVEL DESTINATION................................................................................163By SooCheong (Shawn) Jang and Liping A. Cai


iii

APPLYING EXPECTANCY THEORY TO STUDENT EVALUATIONS OF A COURSE ANDINSTRUCTOR..........................................................................................................................................................................168By Miyoung Jeong, Haemoon Oh, and Brett W. Horton

ENTREPRENEURIAL MANAGEMENT STYLE AND ORGANIZATION STRUCTURE:PRELIMINARY EVIDENCE FROM THE ASIA-PACIFIC CONTEXT.............................................................................173By Giri Jogaratnam and Eliza Ching-Yick Tse

THE IMPLEMENTATION OF QUALITY INITIATIVES IN THE IRISH HOTEL INDUSTRY.....................................178By Mary Keating and Denis Harrington

THE LODGING INDUSTRY MOVING FORWARD IN THE POST SEPTEMBER 11 ERA:A CASE STUDY ......................................................................................................................................................................185By Maryam Khan

THE EFFECTS OF MARKET-BASED RESOURCES AND EXTERNAL MARKET FORCES ON STRATEGICORIENTATION IMPLEMATATION AND FIRM PERFORMANCE IN THE LODGING INDUSTRY ........................190By Byeong Yong Kim, Haemoon Oh, and Cheryl O. Hausafus

A PERCEPTUAL MAPPING OF ONLINE TRAVEL AGENCIES AND PREFERENCE ATTRIBUTES ......................194By Dong Jin Kim and Woo Gon Kim

FINANCIAL VARIABLES FOR PREDICTING BOND RATINGS OF HOTEL AND CASINO FIRMS .......................201By Hyunjoon Kim and Zheng Gu

ECONOMIC VALUE ADDED APPLICATION IN THE HOSPITALITY INDUSTRY....................................................206By Woo Gon Kim and Donald F. Wood

THE CUSTOMER-BASED BRAND EQUITY AND FINANCIAL PERFORMANCE IN THE HOSPITALITYINDUSTRY...............................................................................................................................................................................211By Woo Gon Kim and Hong-bumm Kim

ECONOMIC HOTEL ROOM PRICING: A MULTI-STAGE SYNTHETIC APPROACH...............................................216By Woo Gon Kim and Stephen J. Hiemstra

ANALYSIS OF TAIWANESE HOSPITALITY STUDENTS’ LEARNING STYLES AND PERSONALITYTYPES.......................................................................................................................................................................................222By Hung-Sheng “Herman” Lai, C. Kenny Wu, Betty L. Stout, and Ben K. Goh

REDUCING DYSFUNCTIONAL TURNOVER AMONG HIGH CALIBER FOODSERVICE MANAGERS................227By Joseph “Mick” La Lopa and Richard F. Ghiselli

INFORMATION CONTENT OF LODGING WEB SITES: DOES IT MATCH THE EXPECTATIONS?.......................232By Woojin Lee and Mehment Erdem

WHY DO HOSPITALITY STOCKS EXHIBIT PRICE REVERSAL TRENDS IN THE 1990’S?....................................238By W. K. Leung and Lan Li

CHALLENGES AND STRATEGIC ISSUES IN ADAPTING UNCERTIN ENVIRONMENTS INONTARIO HOTELS ................................................................................................................................................................251By Zhen Lu

SERVICE QUALITY MANAGEMENT IN THE MEETINGS AND CONVENTION INDUSTRY.................................255By Donald J. MacLaurin


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MARKET PROFILING OF U.S. INBOUND GAMING VISITORS TO CANADA...........................................................260By Tanya MacLaurin

A COMPARISON OF DINING HABIT PERCEPTIONS OF TRAVEL CENTER MANAGERS AND TRUCKDRIVERS..................................................................................................................................................................................265By Keith H. Mandabach and Ronald P. Cole

MEASURING FOOD SAFETY KNOWLEDGE AND ATTITUDES OF RESTAURANT EMPLOYEES ......................270By Karl J. Mayer, Lesley Johnson, Andrew Hale Feinstein, and JeeHye Shin

CUSTOMER LOYALTY IN THE LAS VEGAS CASINO INDUSTRY.............................................................................276By Shiang-Lih Chen McCain

THE NEW WORLD OF HOSPITALITY SALES: A TEACHING GUIDE FOR THE PERPLEXED...............................281By Richard G. McNeill

THE EFFECT OF SELF-MANAGING TEAMS ON MANAGER COMMITMENT ANDORGANIZATIONAL TENURE IN PRIVATE CLUBS........................................................................................................287By Edward A. Merritt and Dennis E. Reynolds

AN EXPLORATORY STUDY OF SERVICE LEARNING IN HOSPITALITY EDUCATION:STUDENTS’ PERSPECTIVES OF THE EXPERIENCE......................................................................................................292By Brian Miller, Pam Cummings, and Shan Jiang

EXAMINING CUSTOMER SATISFACTION WITH TRAVEL AGENT WEBSITES:A QUALITATIVE NEURAL NETWORK ANALYSIS APPROACH.................................................................................296By Juline Mill and Alastair M. Morrison

PERCEIVED JUSTICE AND SERVICE RECOVERY STRATEGIES FOR CALL-CENTERSATISFACTION ......................................................................................................................................................................297By Dan Mount and Anna Mattila

A PROPOSED STRUCTURE FOR OBTAINING HUMAN RESOURCE INTANGIBLE VALUE INRESTAURANT ORGANIZATIONS USING ECONOMIC VALUE ADDED ...................................................................301By Kevin S. Murphy

THE IMPACT OF COMPENSATION ON THE TURNOVER INTENTIONS OFOUTBACK STEAKHOUSE MANAGERS............................................................................................................................306By Kevin S. Murphy

THE RELATIONSHIP OF WORK-FAMILY CONFLICT AND FAMILY-WORK CONFLICT TO JOBSATISFACTION ......................................................................................................................................................................312By Karthik Namasivayam and Dan Mount

THEORETICAL EXPLANATION OF CONSUMER PRICE SENSITIVITY AND ACCEPTABILITY:EMPIRICAL SUPPORT FROM A STUDY...........................................................................................................................318By David Njite and H.G. Parsa

PRICE RECOGNITION AND RECALL: DO THEY INFLUENCE CONSUMER PURCHASEDECISIONS? ............................................................................................................................................................................323By David Njite and H.G. Parsa

ROLE OF WEBSITE QUALITY IN ONLINE BOOKING DECISIONS.............................................................................327By Haemmon Oh, Miyoung Jeong, and Mary Gregoire


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THE STRATEGIC EVOLUTION OF LODGING COMPANIES TO BRAND MANAGEMENTORGANIZATIONS: A LONGITUDINAL STUDY ..............................................................................................................332By John W. O’Neil and Anna S. Mattila

THE RELATIONSHIP OF HOTEL MANAGEMENT PRACTICES TO EMPLOYEE LEARNING ANDPERFORMANCE ORIENTATIONS: THE MODERATING EFFECT OF SELF EFFICACY..........................................338By Ravi Pandit

WHY RESTAURANTS FAIL? RESULTS FROM A LONGITUDINAL STUDY AND QUALITATIVEINVESTIGATIONS..................................................................................................................................................................343By H.G. Parsa, Tiffany King, and David Njite

IMAGE COMMUNICATION, CULTURAL DIFFERENCES AND PRICE ENDING PRACTICES: ANANALYSIS OF RESTAURANT MENUS FROM TAIWAN ...............................................................................................349By H.G. Parsa and Hsin-Hui “Sunny” Hu

COMPARISON OF NORTH DAKOTA SENIOR CITIZENS’ GENERAL GENDERPERCEPTION TOWARD HOSPITALITY SERVICE..........................................................................................................353By Mort Sarabakhsh, Robert A. Walther, Greg Sanders, and Susan Ray-Degges

THE IMPACT OF THE RECREATIONAL FEE DEMONSTRATION PROGRAM ON VISITATIONOF HIGH VOLUME NATIONAL PARKS............................................................................................................................357By Zvi Schwartz and Li-Chun Lin.

HOTEL REVENUE MANAGEMENT FORECASTING: EVIDENCE OF EXPERT JUDGMENT BIAS ......................362By Zvi Schwartz and Eli Cohen

EVALUATING COLLABORATIVE LEARNING IN AN ONLINE ENVIRONMENT:LEARNING OR LURKING?...................................................................................................................................................367By Marianna Sigala

INFORMATION & COMMUNICATION TECHNOLOGIES (ICT) AND HOTEL PRODUCTIVITY: DOHOTEL CHARACTERISTICS MATTER?............................................................................................................................374By Marianna Sigala

ARE REPEAT TRAVELERS HOMOGENEOUS?................................................................................................................381By Siu-Ian (Amy) So, Liping A. Cai, and Ruomei Feng

THE MISSION IN ACTION WORKFORCE – INNOVATIONS IN THEHOSPITALAITY AND OTURISM CURRICULUM ............................................................................................................386By David Solnet

TRAVELER GEOGRAPHIC ORIGIN AND MARKET SEGMENTATION:CLASSIFICATION MODELS FORINTERNATIONAL VISITORS TO HONG KONG...............................................................................................................393By Heidi H. Sung, Kaye Chon, and Jenny Ji-Yeon Lee

A THREE-PHASE STUDY OF CULTURAL CONGRUENCE AND LEADERSHIP:IMPLICATIONS FORHOSPITALITY MANAGEMENT ..........................................................................................................................................398By Mark R. Testa

IMPACT ON THE LOCAL ECONOMY OF A APECIAL EXHIBIT AND A UNIVERSITYAFFILIATED MUSEUM.........................................................................................................................................................399By Frank C. Tsai, Ben K. Goh, Betty L. Stout, and Kenny C. Wu


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STRATEGIC IMPLICATIONS OF SERVICE ENCOUNTER IN THE HONG KONG LODGINGINDUSTRY: AN EMPLOYEE PERSPECTIVE ...................................................................................................................403By Eliza Ching–Yick Tse

ANALYSIS OF DESTINATION ATTRACTIONS AND OVERALL IMAGE DETERMINANTS OFOKLAHOMA AND AN INTERNATIONAL TRAVEL DESTINATION...........................................................................408By Hailin Qu and Suosheng Wang..

MANAGING AT AN ATTRACTION LEVEL: FAILURE TO SEE THE STRATEGIC WOOD FOR THEOPERATIONAL TREES .........................................................................................................................................................413By Sandra Watson and Martin McCracken

A STUDY OF CRISIS MANAGEMENT BY HOTEL MANAGERS IN THE WASHINGTON, D.C.METRO AREA.........................................................................................................................................................................422By Larry Yu, Greg Stafford, and Alexander Kobina Armoo


1

A COMPARATIVE STUDY OF RESIDENTS’ PRE- AND POST-PERCEPTION TOWARD CASINODEVELOPMENT: A STRUCTURAL EQUATION MODELING APPROACH

Ki-Joon Back*Kansas State University

and

Choong-Ki Lee*Kyung Hee University

ABSTRACT

While most of the previous research on residents’ perceptions was conducted in the form of snapshots taken at a particulartime, or after tourism development, this study aims to explore any significant differences in residents’ perceptions betweenpre-and post-development of casinos by taking a structural equation modeling approach. Specifically, this study wasconducted to investigate the effects of social, economic, and environmental issues on residents’ perceived benefit and supportvariables. The results show that positive economic and social impact was most significant in determining the benefit andsupport level, respectively, both before and after casino development.

Key Words: Casino, Residents, Perception, Structural Equation Modeling

INTRODUCTION

Several recent studies on the impacts of gaming legalization have been reported in the literature (Hsu, 2000; Long,1996; Perdue, Long, and Kang, 1999). The foundation of these gaming impact studies stems mainly from the tourism impactstudies of the 1970’s (Hsu, 2000). Most of the research has been focused on residents’ perceptions of the social, economic,and environmental impacts of gaming on their community. According to Carmichael, Peppard and Boudrea (1996),residents’ attitudes are important because they are rarely expressed in the political and development decision-making process.Their study results indicated that there was a growing awareness by local residents of both the negative impacts of rapiddevelopment and the positive employment benefits of casino development. Also, attention gaming development has evolvedinto the exploration of host community residents’ quality of life issues (Perdue et al. 1999). Giacopassi, Nicholes and Stitt(1999) worked with policy makers in seven communities that were new riverboat casino jurisdictions. Results showed thatthe majority of respondents favored the casino in the community and believed that it enhanced the quality of life in thecommunity by providing positive impacts on the economy.

The perceptions or behaviors of residents can be explained by applying the social exchange theory, which attemptsto understand and predict the behavior of individuals in an interactive situation (Ap, 1990). Based on the tourism literature,residents who perceive personal benefit from casino development will support and express positive attitudes toward casinodevelopment. Perdue et al. (1995) supported the social exchange theory in that residents who perceived benefits fromgaming were more likely to be positive in assessing the quality of life. They also found that personal benefits were stronglycorrelated with support for gambling and its positive impacts, such as jobs and recreation opportunities. The results indicatedthat resident support for gambling was a function of personal benefits, future of the community, positive and negativeimpacts of gambling, and quality of contact with gamblers.

Gaming impact studies in Korea are rare due to the short history of the casino industry. Although thirteen casinoshave been in operation since the late 1960’s and early 1970’s, only foreigners are allowed to enter those casinos. Afterobserving many positive economic impacts of gaming in land-based casinos in the U.S., such as in Colorado, the Koreangovernment legalized the gaming in the run-down former coal-mining center of Chongsun, Kwangwon province for domesticcustomers in December 1995. The small casino, so called “Kangwon Land Casino,” was the first casino for domesticcustomers opened in October 2000. This casino has thirty table games and 480 slot games and is attached to a deluxe hotelwith 199 guestrooms.

* Both authors have equal contributions for this study


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Prior to opening the casino, the government expected to experience numerous positive economic impacts includingincreased employment rate, disposable income, sales revenue in local businesses, and so on. Despite the many positiveimpacts of the casino, a considerable number of residents have expressed concerned about its negative impacts, namely socialand environmental problems. Like casinos in the U.S. market or in other nations, the residents of Chungsun are experiencingproblems with gambling addiction, crime rate, prostitution, drugs, traffic congestion and air pollution. The number ofpawnshops rapidly increased just after the casino’s opening and financial crisis and problem gamblers were frequentlyreported by mass media. However, much of the qualitative data including residents’ attitude toward the casino and socialperspectives have not been well reported.

Therefore, the primary purpose of this study aims to explore the changing attitudes of residents towards the pre- andpost-development of casinos, using a structural equation model. Specifically, this study was conducted to explore theunderlying factors affecting residents’ perceptions of casino development in terms of social, economic, and environmentalimpacts by developing measurement scales. Second, this study was developed to examine the underlying relationshipsamong impact, benefit, and support variables based on the social exchange theory.

METHODOLOGY

Characteristics of Respondents

Two casino communities, designated by a special law as run-down mining areas, were chosen for survey research.The data for this study were collected in two different time frames, through pre- and post-surveys. The pre-survey wasconducted six months prior to the casino opened. A self-administered questionnaire and personal interview were conducted.Respondents of at least 18 years of age were asked to participate in the survey. A total of 517 usable questionnaires werefinally collected during the pre-survey.

The post-survey was administered to those who had responded to the pre-survey after the Kangwon Land Casinoopened six months later. Each researcher was posted to the same survey site as in the pre-survey and was given informationon respondent’s name, phone number, and working place as collected in the pre-survey so that they could be easily identified.First, the surveyors asked respondents whether they had participated in the pre-survey, if so, and then they proceeded with thepost-survey. During the post-survey, a total of 404 usable questionnaire were finally collected fewer than those collected inthe pre-survey. Among the 404 post-survey respondents, the proportion of male respondents (53.7%) was slightly higherthan that of the female (46.3%). The majority of respondents were married (72.8%), aged 30 to 49 (63.6%). The majority ofrespondents earned less than 2 million won (approximately US$1,667) each month. Of the respondents, 52.2% stated thatthey were born in the casino community.

Measurement of Constructs

A preliminary list of measurement items was initially generated from a review of tourism literature pertaining toresidents' perceptions toward tourism and casino impacts (Carmichael et al., 1996; Jurowski et al., 1997; King et al. 1993;Lindberg and Johnson, 1997; Liu and Var, 1986; Long, 1996; Pizam and Pokela, 1985; Perdue et al., 1995, 1999). Then,these items were screened by tourism scholars in the field of tourism impacts and community leaders of the casino town. Apretest was conducted and validity of dimensionality and inter correlation was examined by factor analysis.

The theoretical model was developed based on the social exchange theory. As mentioned earlier, many researchers haveutilized the social exchange theory to integrate factors influencing residents’ reactions to casino development. The socialexchange theory assumes that residents’ perceptions are affected by the perceptions of the exchange people believe they aremaking (Gursoy et al., 2002). Jurowski et al. (1997) stated that residents’ support for tourism development should beconsidered as their willingness to take an exchange based on the social exchange theory.

The model postulates that exogenous variables have both direct and indirect effects on benefit and support. Thetheoretical model tested, as shown in Figure 1, involved six constructs of exogenous variables (negative social, negativeenvironmental, negative economic, positive social, positive environmental, positive economic factors) and two constructs ofendogenous variables: benefit, and support. Every construct found to be reliable by having Cronbach’s alpha ranged from.68 to .96 as a result of confirmatory factor analysis. Table 1 presents explication of each construct which were measured ona 5-point Likert-type scale: 1=strongly disagree, 3=neutral, and 5=strongly agree.


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Table 1: Results of Confirmatory Factor Analysis – Pre-Data and Post-Data

Construct Internal Item Factor LoadingsConsistency a Pre Post

Negative Social 0.96 Gambling addicts 0.85 0.78Destruction of family 0.87 0.82Prostitution 0.87 0.85Divorce 0.83 0.86Alcoholism 0.91 0.81Crime 0.93 0.85

Negative Environmental 0.92 Traffic congestion 0.77 0.70Quantity of litter 0.91 0.84Noise level 0.92 0.85Water pollution 0.84 0.84Destruction of natural environ. 0.82 0.75

Negative Economic 0.68 Costs of living 0.80 0.78Tax burden 0.72 0.74Leakage of casino revenue 0.38 0.34

Positive Social 0.87 Quality of life 0.53 0.65Community spirit 0.64 0.73Educational environment 0.80 0.85

Positive Environmental 0.68 Preservation of historic sites 0.73 0.74Preservation of natural beauty 0.56 0.50

Positive Economic 0.72 Investment and business 0.73 0.70Employment opportunity 0.68 0.65Tourist spending 0.66 0.67Tax revenue 0.61 0.70Public utilities/infrastructure 0.67 0.70

Benefits 0.75 Personal benefit 0.67 0.71Community benefit 0.88 0.85

Supports 0.82 Bright future 0.81 0.83Pride 0.77 0.80Support 0.82 0.85Right choice for the city 0.72 0.81

a Fit indices: c2(597)=1684.83; p=0.00, c2/ df = 2.82, RMSEA = .06, CFI = .91, NNFI = .90.

LISREL Methodology and Results

By using the LISREL program, the goodness of fit of the various models was testable and the relative fit ofparticular pairs of models could be assessed (Joreskog, Sorbom, du Toit, and du Toit, 2001). Three approaches to ascertainconsistency of factors were available using this methodology. The first approach answers the question, “Are there significantrelationship among the variables? ” The second approach answers the question, “Is there a significant difference between thepre and post residents perceptions toward the Kangwon Land casino development?” The third approach answers thequestion, “If there is a significant difference between two groups, what kind of differences occurred in supporting the casinodevelopment after the casino was opened?” These approaches were used: the first in models 1A and 1B, the second and thirdin models 2 and 3, described below.


4

Model 1A, shown in Figure 1, specified the eight factors described above. In this model, direct and indirect pathsfrom negative social, negative environmental, negative economic, positive social, positive environmental, positive economicfactors, and benefit to support were specified as well as direct paths from all those exogenous variables to benefit. Toidentify the best model for testing, a contrasting model (Model 1B) was created. This model was the same as Model 1A withthe exception that the direct paths from each exogenous variable to support were eliminated. A well-fitting Model 1A or 1Bprovides a degree of support for the general theoretical model used as a basis for the present test for the effects of exogenousvariables (impact factors) on endogenous variables (benefit and support) and group analysis between pre and post data. Thesignificance of the path coefficients would provide a test for various aspects of the general theoretical model.

Table 2 summarizes the goodness-of-fit results for models 1A, 1B, 2, and 3. The overall fit of Model 1A was good.The chi-square was significant (p


5

benefit were most significant among other exogenous variables. Furthermore, positive economic factors had the strongesteffect on residents’ support level (b=0.33, t=5.87).

Figure 1: Standardized Parameter Estimates for the Model (Model 2): Pre-Data Vs. Post-Data

-0.16 (3.01) a

- 0.13 (2.67) a

0.25 (5.06) a

0.66 (8.87) a

0.23 (3.22) a

0.08 (2.53) b

0.29 (4.46) 0.26 (3.88) a

0.26 (3.76) a 0.14 (1.45)

0.33 (5.87) a 0.04 (0.57)

Significant both pre and post Significant in only pre Not significant

a, b indicate statistical significance at p≤.001, p≤.01, respectively; t-values are in parentheses; italic figure denotes pre-date.

After the casino development, post data showed some changes in residents’ support level. Figure 1 exhibits thesignificant effect of residents’ perceptions about benefit on their attitude toward support for casino development. Benefitsshowed the strongest effect on support (b=0.66, t=8.87), whereas positive environmental and economic factors had nosignificant direct effects on support. Furthermore, positive social factors had less effect on support for post data (b=0.26,t=2.76) as compared to pre data (b=0.29, t=4.46). It is interesting to note that positive economic factors had significanteffects on support only when mediated by benefit. This result can be interpreted as residents exposure to the casino industry,and that their perceptions of positive environmental and economic factors did not cause them to support the casino industry.Rather, they tend to become more supportive when they actually received benefit, specifically economic benefit. Also, theresults showed that those residents’ perceptions about negative social factors had less effect on their level of support for thecasino after its opening.

CONCLUSION

The primary intention of this study aimed to exploring the residents’ perceptions of changes between pre- and post-development of casinos by measuring the levels of economic, social, and environmental impact factors. This study alsosought to ascertain how each impact factor determined residents’ benefit and support levels based on the social exchangetheory by applying structural equation modeling.

Benefit

0.26

(4.

04)

a

PositiveSocial

PositiveEnviron.

NegativeSocial

NegativeEviron.

NegativeEcon.

PositiveEcon.

Support


6

Confirmatory factor analysis confirmed six underlying dimensions measuring residents’ perceived impacts of casinodevelopment as well as benefit and support constructs. Results of the structural equation modeling indicated thatrespondents’ support level was directly influenced by benefit. Although the mean values of benefit and support decreasedsignificantly upon the casino’s operation, the causal relationship became more significant between those two variables. Bothpositive and negative economic factors showed significant impacts on support when they actually received benefits. Thesefindings confirmed the study by Perdue et al. (1995) that results for residents showed a positive correlation between theirsupport level for casino development and job opportunities and other types of economic impacts. The effects of the positiveand negative social factors on support level were slightly decreased. These significant economic and social impacts onbenefit and support level confirm the social exchange theory. However, environmental impact did not significantly predicteither the benefit or support level in post data.

In sum, the results showed that positive economic impact was most significant in determining the benefit level,which was further enhanced after the casino opened. Also, respondents perceived positive social impacts to be mostsignificant in affecting the support level both before and after casino development. The results of the structural equationmodeling approach suggest following implications: (1) the social exchange model fits very well in explaining residents’attitude toward casino operation both pre and post-survey data; (2) policy-makers should identify how to provide benefits tothose local residents so that they can support casino development further; and (3) casino operators and policy makers shouldmake efforts to minimize the negative social impacts, because increase in the level of quality of life or standard of living wasnot only due to the positive economic impact but also was significantly affected by negative social factors, such as gamblingaddiction problems.

One limitation of the study was that the post data were collected only six months after collection of the pre-surveydata. Respondents could still remember their responses causing history bias, or could not assess the actual impacts of thecasino on their personal life due to the short collection period. A longitudinal study is strongly recommended to investigateresidents’ perceptions of casino development over time, preferably examined on an annual basis. The results of thelongitudinal study continuing for several years after the opening of a casino should be able to be used in evaluating changesto residents’ attitudes and identifying new positive or negative impacts of the casino. Thus, policy-makers can takeappropriate actions to make the community a pleasant place to live and to improve the quality of life for residents.

REFERENCES

Ap, J. (1990). Residents’ Perceptions Research on the Social Impacts of Tourism. Annals of Tourism Research 17:610-616.

Bollen, K. A. (1989). Structural Equations with Latent Variables. New York: Wiley.Carmichael, B. A., D. D. Peppard, and F. A. Boudreau (1996). Mega-Resort on My Doorstep: Local Resident Attitudes

toward Foxwood Casino and Casino Gambling on nearby Indian Reservation Land. Journal of Travel Research24:9-16.

Giacopassi, D., and B.G. Stitt (1999). Assessing the Impacts of Casino Gambling on Crime in Mississippi. AmericanJournal of Criminal Justice 18: 117-131.

Gursoy, D., C. Jurowski, and M. Uysal (2002). Resident Attitude: A Structural Modeling Approach. Annals of TourismResearch 29(1):79-105.

Hsu, C. H. C. (2000). Residents’ Support for Legalized Gaming and Perceived Impacts of Riverboat Casinos: Changesin Five Years. Journal of Travel Research 38:390-395.

Joreskog, K., D. Sorbom, S. du Toit, and M. du Toit (2001). LISREL 8: New Statistical Features. Chicago: ScientificSoftware International.

Jurowski, C., M. Uysal, M., and D. R. Williams (1997). A Theoretical Analysis of Host Community Resident Reactionsto Tourism. Journal of Travel Research 36(2):3-11.

Kangwon Land Casino (2000). 2001 Business Operation plan. Kangwon Land Casino: Kangwon Province.King, B., A. Pizam, and A. Milman (1993). Social Impacts of Tourism: Host Perceptions. Annals of Tourism Research

20(4): 650-665.Liu , J. C, and T. Var (1986). Resident Attitudes toward Tourism Impacts in Hawaii. Annals of Tourism Research

13(2):193-214.Lindberg, K., and R. L. Johnson (1997). Modeling Resident Attitudes toward Tourism. Annals of Tourism Research

24(2): 402-424.Long, P. T. (1996). Early Impacts of Limited Stakes Casino Gambling on Rural Community Life. Tourism

Management 17:341-355.Perdue, R., T. Long, and Y. S. Kang (1995). Resident Support for Gambling as a Development Strategy. Journal of

Travel Research 34:3 –11.


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Perdue, R., T. Long, and Y. S. Kang (1999). Boomtown Tourism and Resident Quality of Life: The Marketing ofGaming to Host Community Residents. Journal of Business Research 44:165-177.

Pizam, A. (1978). Tourism's Impacts: The Social Costs to the Destination Community as Perceived by its Residents.Journal of Travel Research 16(4):8-12.

Pizam, A., and J. Pokela (1985). The Perceived Impacts of Casino Gambling on a Community. Annals of TourismResearch 12:147-165.


8

E-RELATIONSHIP MARKETING: AN APPLICATION TO HOTEL WEBSITE DEVELOPMENT

Billy BaiUniversity of Nevada at Las Vegas

SooCheong (Shawn) JangKansas State University

and

Clark HuTemple University

ABSTRACT

The purpose of the study was to examine online customer relationship building through hotel company web sites. Using thetop 150 hotel companies as the sample, the study found that hotel companies perform relatively well in establishing the lowerlevels of customer relationships. The size of the company was found to be positively related to the overall customerrelationship building; the number of brands held by the hotel company was found to have a negative impact on theperformance of customer relationship building. Marketing implications were discussed.

Key Words: e-Relationship marketing; customer relationship management; Website development; Hotel industry

INTRODUCTION

As competition in the lodging industry is increasingly severe, mass marketing and advertising have become lesseffective. In recent years, relationship marketing has been drawing marketers’ attention as an alternative to deal withcustomers of diversified needs. Relationship marketing represents a paradigm shift in marketing and orientation (Grönroos,1996). The ultimate goal of relationship marketing is to make customers loyal through tailor-made marketing andconsequently to achieve financial success.

As an electronic means to implement relationship marketing, the Internet is considered revolutionary in helpingbuild personal relationships between hotels and customers. Since the Internet is a strategic mechanism facilitating the practiceof relationship marketing, hotel companies should put the relationship-marketing concept into work as a strategy directing thedevelopment of their websites (Gilbert, Powell-Perry, & Widijoso, 1999). However, these efforts still fall short of a full-swing implementation of relationship marketing. Furthermore, little attention of research regarding relationship marketinghas been devoted to the lodging industry. More importantly, since the combination of relationship marketing and the Internetmay offer a powerful competitive advantage for hotel companies (Gilbert et al., 1999), research efforts as to how hotelcompanies promote customer relationships on the Internet have become greatly needed. Therefore, the objectives of thisstudy were to 1) examine the extent to which hotel companies use their websites to build customer relationships, 2) toevaluate the performance of online relationship building by various hotel companies’ attributes such as the type of company,the type of website, and the company size measured with the number of rooms, and 3) to identify the attributes that have asignificant impact on the level of website development in relation to customer relationship building. The results of the studyis expected to provide valuable recommendations on long-term relationship building with online customers in the lodgingindustry.

LITERATURE REVIEW

The heart of marketing efforts is shifting from immediate transaction-based marketing with its emphasis on winningnew customers, to customer retention through effective management of customer relationships (Martin, 1998). According toKotler, Bowen, and Makens (2003), relationship marketing centers on creating, maintaining, and enhancing strongrelationships with customers. The goal of relationship marketing is to deliver long-term value to customers, and the measureof success is long-term customer satisfaction and loyalty. In relation to the operational perspective of relationship marketing,five progressive levels of relationships were proposed by Kotler, et al. (2003, p. 391): basic, reactive, accountable, proactive,and partnership. The underlying rationale of relationship marketing is that acquiring customers is much more expensive than


9

keeping them (Stone, Woodcock, & Wilson, 1996). Strong partnership with loyal customers can result in a major increase incompany profitability.

As a main technological driver based on relationship marketing, customer relationship management (CRM) providesmanagement with the opportunity to implement relationship marketing on a company-wide basis (Ryals & Knox, 2001). Theinformation technology works as the ‘bridge’ that connects a company to customers in the relationship marketing system.Thus, the Internet is an effective marketing instrument that allows marketers to bring relationship marketing into practice bygenerating market knowledge and facilitating customer transactions. Web-based relationship marketing also offers distinctadvantages: low-costs and high level of interactivity and one-to-one, personalized relationships with customers.Concentrating on the forty-one features of electronic relationship management, Feinberg, Kadam, Hokama, and Kim (2002)analyzed the top 100 specialty store, standard retail store, and Internet retailer web sites. The researchers found that Internetretailers were significantly more likely to have e-customer relationship attributes on their websites and argued that somefeatures such as company profile, mailing address, links, search engine, spare parts availability, gift certificate purchase, andchat were highly associated with customer satisfaction. Gilbert et al., (1999) analyzed 143 hotel websites provided by theYahoo Web Site to examine the management attitudes to the adoption of Internet as a relationship-marketing tool. Theauthors reported that the majority of the hotels have used the Web as an information center and a reservation channel, butonly one-fifth of them carry real-time processing function for online transactions. They also noted that only a small portion ofhotel chains have seen the Internet as a relationship marketing medium.

METHODOLOGY

The operational framework used for this study was based on the studies by Kotler, et al (2002) and Feinberg, et al(2002). In lieu of the nature of lodging business, some website features were modified to reflect the unique characteristics ofhotel websites. Due to the page limitation, the framework is not showed in this conference paper and it can be requestedthrough authors. The sample for this study was chosen from a list of top hotel companies published by the Hotel & MotelManagement (2001). There were 281 hotel companies ranked by the number of guestrooms. For this study, the top 150 hotelcompanies were selected from the list. Out of 150 hotel companies, twenty-three hotel company websites were found to bebad links. In the end, the study sample ended up with 127 hotel companies. There were 10 different types of hotel companiesfound in the study sample, namely, ownership (n=17), management (n=21), franchise (n=5), ownership and management(n=59), ownership and franchise (n=1), management and franchise (n=1), O/M/F (ownership/management/franchise) (n=16),membership (n=3), consortium (n=3), and association (n=1). The size of hotel companies ranged from 2,229 to 552,879guestrooms that they are affiliated with. Data for this study were collected from the websites of 127 sample hotel companiesin November and December 2002. With the identified framework for online customer relationship building through hotelcompany websites, researchers visited the randomly assigned hotel company websites to collect relevant information. Whenthe hotel company website contained the item on the pre-identified framework, the collected information was coded as “1”and “0” otherwise. Additional information including the type of website, namely, a single brand versus multiple brands, andthe number of brands that hotel companies are affiliated with, were also collected. In this sample, more hotel companywebsites were designed for multiple brands (n=94) than a single brand (n=33). In terms of the number of brands serviced bythese hotel companies, 56% of the hotel companies (70 out of 125 with two missing values) were affiliated with up to 6brands.

Descriptive statistics, analysis of variance (ANOVA), and regression model were employed to achieve the studyobjectives. There are 5 levels of relationships with 29 attributes in the identified framework. A subtotal was calculated foreach of the 5 levels, that is, the sum of responses at each level divided by the total number of attributes for that level. Thesesubtotal percentages were thus utilized as an indication of how well hotel companies built customer relationships with theirwebsites. For the type of companies, the ANOVA test retained 4 types of companies that had more than 5 observations,namely, ownership companies, management companies, ownership/management companies, and O/M/F companies asdefined by the data source. The number of guestrooms that the hotel company was affiliated with was used as a proxy for thecompany size. Because of the wide range of guestrooms reported in the study sample (minimum 2,229 and maximum552,879), this variable was collapsed into three categories based on the distribution of guestrooms. Therefore, hotelcompanies with the top 25% guestrooms were named large companies, the middle 50% named medium companies, and thelowest 25% named small companies. For the regression model, the overall average of the responses from the 5 levels ofrelationships was calculated by dividing the sum of subtotals from all 5 levels by 5. The overall average was used as thedependent variable in the regression equation. The independent variables included the size of company, the number ofbrands, the type of website, and the type of company. The type of company variable was dummy coded and “managementcompanies” were used as the reference group because all other types of companies had an ownership component.


10

RESULTS

In comparison, hotel companies were much better in maintaining customer relationships at the basic (74.2%) andreactive (76%) levels by providing necessary product information online and contact information on their websites (Table 1).As the degree of customer relationship progresses, however, this study found that hotel companies had not utilized theirwebsites to establish effective company-customer relationships. As Table 2 indicates, hotel companies provided less websitefeatures that are meant to hold customers accountable and build deeper customer relationships. At the accountable level(37.1%), problems fell on such areas as complaining ability (4%), annual report (20%), customer service pages (25%), andmost recent financial information (27%). At the proactive level (32.6%), hotel companies completely failed to use electronicbulletin board that allows customers to share information publicly (0%) and chat that allows customers to interact with eachother and the site (1%). The partnership level of relationship is the highest level of customer relationship that companies arecommitted to establish. At this level, companies consider customers as partners. It was found in this study that hotelcompanies provided least website features (21.9%) that are aimed at maintaining partner relationship with their customers. Aclose look at the performance of each item at this level showed that the dominant problems included differential membershipstructure (6%) and hotline exclusively for members (6%), although loyalty programs were mentioned for relatively higheroccurrences (45%).

Table 1: Customer Relationship Building through Hotel Company Websites

Five Levels of Relationships Mean (Standard Deviation)Level 1: Basic .742 (.256)Level 2: Reactive .760 (.271)Level 3: Accountable .371 (.299)Level 4: Proactive .326 (.178)Level 5: Partnership .219 (.267)

For online customer relationship building in terms of company characteristics, the study found significantdifferences by the type of company, the type of website, and the company size (Table 2). It seems that hotel companies werenot consistent in their efforts to hold customers accountable, take the initiative to build deeper customer relationships, andadd value to the company-customer relationship. Regarding the type of website, hotel companies with a single brandoutperformed those with multiple brands. The former was found to provide more online information than the latter in termsof product highlights, online reservation, and free telephone for making reservations. Company size was also found to havean impact on the customer relationship building. At the basic level, the large hotel companies provided product preview morethan the medium and small companies. At the accountable level, the large companies seemed to outperform the othercompanies in very single aspect of the web features including customer service pages, complaining ability, Internet privacypolicy, change or cancel a reservation, retrieve a reservation, security alert, annual report, and most recent financialinformation. At the proactive level, the large companies were found to excel over the other companies in providing free signups for product information and promotion packages, and local search engines.

Table 2: Online Customer Relationship Building by Type of Company, Type of Website, and Company Size

Five Levels of Relationships Type of Company Type of Website Company SizeLevel 1: Basic .223 .696 .014*Level 2: Reactive .110 .005* .069Level 3: Accountable .001* .107 .001*Level 4: Proactive .012* .088 .001*Level 5: Partnership .004* .904 .137

Note: 1. The numbers present the p-value of the ANOVA tests; 2. * significant at .05 level.

The regression model was conducted to identify the influencing factors upon the level of hotel website developmentin relation to online customer relationship building. Table 3 presents the results and shows that the two of the independentvariables tested were important in accounting for the website development of the top hotel companies. The coefficient ofdetermination (R2) indicates that 42.9 percent of the variation in the website level was explained by the variables in themodel. The regression model was statistically significant at an alpha level of .01. Since correlations among the independent


11

variables were presumed, multicollinearity was checked with VIF (Variation Inflation Factor) and their values were foundwell below the problematic level of 10. The beta coefficient indicates that the number of rooms, as a proxy of the size ofcompany, was found to be significant at the 0.01 level, signifying that the larger the company is, the more sophisticatedwebsite it does have. The number of brands was found to have a significantly negative relationship in the process ofdeveloping customer relationships on the company’s website. The inverse relationship suggests that the more brands acompany holds, the less progress in the website development toward customer relationship building. The result may besomewhat contradictory to the general expectation. However, it shows that the reality may be opposite and indicates that thecompanies carrying fewer numbers of brands surpassed their counterparts in focusing their resources on the web developmentand that they reached up to the more advanced levels of customer relationship building accordingly.

Table 3: Effect of Company Size, Number of Brands, Type of Website, and Type of Company

Variables Standardized Beta Coefficients tNumber of Rooms 0.270 3.48**

Number of Brands -0.564 -6.94***Type of Web Site Designed for Multiple Brands (Designed for Single Brand)a

0.150 1.77

Type of Company Ownership Ownership & Management Ownership, Management, & Franchise (Management)a

0.1030.0050.138

1.270.061.69

Model F value R-square Adjusted R-square

14.8**0.4290.400

Note: 1. a Parenthesis refers to a variable used as a reference group; 2. * p


12

companies should also design and provide extra value to “hook” customers including differential membership structure, e.g.Gold or Platinum Guest, and hotline exclusively reserved for such a guest. One major advantage of the Internet is itsconvenience. The website is accessible twenty-four hours a day and seven days a week. Hotel companies need to providemore interactive means to communicate with their customers. Apparently, hotel companies have not seen the growingpotential of electronic bulletin board or instant chat functions that allow customers to share information publicly and interactwith each other and the website. It is imperative that hotel companies foresee these opportunities for everlasting customerrelationship building on the Internet. Customers have a certain level of perceived anxiety before the purchase of a hotelproduct. Testimonials or instant online conversations may help eliminate this anxiety and turn the prospective customer intoan actual buyer. Web features as such may become a competitive advantage over the hotel company’s competitors in the longrun.

CONCLUSION

This study examined the use of hotel company websites for customer relationship building. While hotel companiesperform relatively well at the basic and reactive levels of customer relationships, it is obvious that more commitment andexpertise should be devoted to building more advanced levels. To establish the partnerships with customers should be soughtafter by every hotel company. As an effort of achieving this level of relationship, customer loyalty programs should bedeveloped and implemented wisely on the Internet. Hotel companies must continue to strive for innovative approachestoward effective implementation of e-customer relationship marketing.

This study was exploratory in nature and should call for more research endeavors of investigating customerrelationship building on the e-business environment. Future research should examine additional hotel website features thatare appropriate for online customer relationship building. This study only chose hotel companies as a study sample. Hotelswebsites at the property level should be inspected as well. Customer satisfaction and hotel management perceptions must beexplored for a better understanding of e-customer relationship building. This study provided a well-defined approach of e-relationship marketing in the lodging industry. Other segments of the hospitality and tourism industry may apply and refinethe approach for more insightful discoveries in e-relationship marketing.

REFERENCES

Arnott, D. C., & Bridgewater, S. (2002). Internet, interaction and implications for marketing. Marketing Intelligence &Planning, 20(2), 86-95.

Feinberg, R. A., Kadam, R., Hokama, L., & Kim, I. (2002). The state of electronic customer relationship management inretailing. International Journal of Retail & Distribution Management, 30(10), 470-481.

Gilbert, D. C., Powell-Perry, J. A., & Widijoso, S. (1999). Approaches by hotels to the use of the Internet as relationshipmarketing tool. Journal of Marketing Practice - Applied Marketing Science, 5(1), 21-38.

Grönroos, C. (1996). Relationship marketing: Strategic and tactical implications. Management Decision, 34(3), 5-14.Hotel & Motel Management. (2001, September 17). H&MM's top hotel companies listings. Retrieved October 8, 2002, from

http://www.hive4hospitality.com/hotelmotel/data/articlestandard/hotelmotel/092002/10850/article.pdfKotler, P., Bowen, J. T., & Makens, J. C. (2003). Chapter 11: Building customer loyalty through quality. In Marketing for

Hospitality and Tourism (3rd ed., pp. 380-440). Upper Saddle River, NJ: Prentice Hall, Inc.Martin, C. L. (1998). Relationship marketing: A high-involvement product attribute approach. Journal of Product & Brand

Management, 7(1), 6-26.Reichheld, F. F., & Sasser, W. E., Jr. (1990). Zero defections: Quality comes to services. Harvard Business Review, 68(5),

105-111.Stone, M., Woodcock, N., & Wilson, M. (1996). Managing the change from marketing planning to customer relationship

management. Long Range Planning, 29(5), 675-683.


13

FUTURE EVENTS AND THEIR IMPACT ON THE U.S. HOSPITALITY INDUSTRY A DELPHI STUDY TOPREDICT THE ROLE OF HUMAN RESOURCES, OPERATIONS, INFORMATION TECHNOLOGY,

MARKETING, AND FINANCIAL MANAGEMENT IN THE YEAR 2007

Jeffrey A. BeckJeffrey D. Elsworth

Arjun Singh

and

Bonnie J. KnutsonMichigan State University

ABSTRACT

The purpose of this Delphi study was to make predictions in five areas of concern to hospitality industry managers, investors,lenders, educators, and others associated with the U.S. hospitality industry. These five prediction categories consist of over100 events, which ultimately will define the future hospitality industry. An analysis of the results will provide initial cluesabout the direction of the industry with important implications for the management of hospitality enterprises. The fiveprediction categories for the purpose of the study include the industry structure, management of human resources,operations/information technology, marketing, and finance.

Key Words: Delphi Study, forecasting, lodging, food Service, and clubs

INTRODUCTION

Hospitality managers, investors, lenders, and other decision makers in the hospitality industry are not comfortablewith uncertainties and the associated risks that they imply. Most decision-makers have some feeling about the occurrence offuture events. These may range from a high degree of confidence to a vague and ill-defined feeling of discomfort (Pyhrr, S.A, Cooper, J. R., Wofford, L.E., Kapplin, S. D., & Lapides, P.D., 1989). Nevertheless, decision makers in the hospitalityindustry are concerned about the direction of actual change in any future event. Sinkley states that actual change, which isafter-the-fact future change, can be broken down into an anticipated component and an unanticipated component. If changeconsisted of only an anticipated component, then there would be little or no risk involved. The unanticipated component isclearly the source of risk (Sinkley, 1992).

As part of the celebration of the 75th anniversary of The School of Hospitality Business at Michigan StateUniversity, the faculty of the school wished to conduct a predictive research study for a time capsule to be opened in 25years. The study was to identify any areas of uncertainty and concern for individuals and organizations with ties to thehospitality industry. Through a study of the literature and discussions with hospitality faculty and industry executives, fivecategories of uncertainties were identified as primary concerns for the hospitality industry:

1. The size and structure of the hospitality industry.2. Management of human resources in the hospitality industry.3. Management of operations and information technology.4. Marketing and/or Brand management.5. Financial management.

While the time capsule study included both 2007 and 2027, the purpose of this paper is to identify andcomprehensively predict the key events and issues that will define each of the five categories for the year 2007. Bysuccessfully predicting the likelihood with which these key future events will occur, we hope to mitigate some of theuncertainty associated with the management of the hospitality organizations in the future.

OVERVIEW OF THE DELPHI STUDY AND METHODOLOGY

There are primarily two types of forecasting methods: quantitative and qualitative. Quantitative approaches may befurther divided into causal and time series (Schmidgall, 2002). Causal methods such as regression analysis and econometrics“assume that the value of a certain variable is a function of other variables” (Schmidgall, 2002). Time series analysis, on theother hand, uses the occurrence of a past pattern to predict future events. Naïve methods, moving averages, and exponential


14

smoothing are some examples of time series analyses. Certain forecasting problems are not readily handled using aquantitative approach. In such cases, one has to rely on qualitative forecasting methods, which are holistic in nature and relymore on human judgment when they are used to make predictions. One such qualitative (long-term) forecasting technique isthe Delphi technique.

“The Delphi technique is a method used to systematically combine expert knowledge and opinion to arrive at aninformed group consensus about the likely occurrence of future events”. According to Helmer and Rescher (1960), theoriginal proponents of the Delphi technique, “the technique derives its importance from the realization that projections offuture events, on which decisions must often be based, are formed largely through the insight of informed individuals, ratherthan through predictions derived from well-established theory”.

According to Linstone and Turoff, the Delphi technique should be used when one or more of the followingproperties exist in the problem:

1. The problem does not lend itself to precise analytical techniques but can benefit from subjective judgment on acollective basis.

2. The individuals needed to contribute to the examination of a broad or complex problem have no history of adequatecommunication and may represent diverse backgrounds with respect to experience or expertise.

3. More individuals are needed than can effectively interact in a face-to-face exchange.4. Time and cost make frequent group meetings infeasible.5. The heterogeneity of the participants must be preserved to assure the validity of the results; i.e., the participants must

not be dominated by quantity or by strength of personality (called the “bandwagon effect” and the “halo effect,”respectively).

6. When the anonymity of the participants is important. (p. 96)

In addition to the above factors, Paliwoda states that when studies involve “multiple dimensions,” a Delphi studymay be the preferred choice (Paliwoda, 1983). Johnson states that the Delphi technique is especially suited for long-termforecasts—more than five years out in rapidly changing, volatile fields (Johnson, 1976).

All of these reasons had some impact on why the Delphi technique was selected as the method of choice for thepresent forecasting problem. Most important, however, was that the problem was multi-dimensional in nature and involvedexpertise, such that no individual had sufficient knowledge to affect a solution. Therefore, the problem required a groupdecision-making method. The traditional, face-to-face group decision-making processes would have inserted a study bias(particularly due to the “bandwagon effect” and the “halo effect”).

The steps in conducting a Delphi study listed below are combined from various sources in the Delphi literature(Moeller and Shafer, 1984; Martino, 1983; Deveau, 1994; Linstone and Turoff, 1979; Tersine and Riggs, 1976):

Step 1: Identify the basic issues, problems, and events to be predicted.Step 2: Select a panel of experts.Step 3: Explore, discuss, and finalize the basic issues and events to be predicted.Step 4: Design a draft questionnaire.Step 5: Pilot-test the draft questionnaire.Step 6: Mail round 1 of the Delphi questionnaire.Step 7: Summarize the statistical results of round 1 and include these results with the round 2 mailing of the questionnaire.Step 8: Continue future rounds similar to steps 6 and 7.Step 9: Analyze the data to show consensus of participants over progressive rounds.

The combined functional and industry specialization expertise of the faculty at The School of Hospitality Business atMichigan State University, was used to develop the prediction issues and event statements. Based on brainstorming sessionsand faculty input, three separate questionnaires were developed representing the three major sectors of the hospitalityindustry: Lodging, Food Service, and Clubs. Each questionnaire was divided into five categories, to coincide with theobjectives of the study:

1. The size and structure of the hospitality industry.2. Management of human resources in the hospitality industry.3. Management of operations and information technology.


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4. Marketing and / or Brand management.5. Financial management.

Using faculty and industry expertise, over 100 future event statements were developed, for the categories in thequestionnaire and assigned a 5-point likelihood of occurrence scale with 5 indicating “very likely to occur” and 1 indicating“not likely to occur.” The questionnaire format was divided into two prediction periods: 2007 and 2027. It also providedspace at the end of each category where respondents could write opened ended predictions about future events. For thepurposes of this study, only the results from the year 2007 responses are being presented.

An expert for the purposes of the study was expected to have a broad view understanding of their industry sector(lodging, food service and clubs), with specific expertise in at least one functional area. The expert was to be either in a topmanagement or ownership rank and directly involved in making strategic decisions for their organization. Finally, we felt thatan effective panel of experts should not only be accessible but also interested in the results of the research.

The School of Hospitality Business at Michigan State University’s alumni database and the Club Manager’s ofAmerica (CMAA) provided a rich list for the selection of an expert panel. We selected 74 lodging, 47 food service, and 41club industry executives as experts using the above criteria. Due to a limited number of MSU alumni in the Club Industry, theCMAA list was used to augment the list of experts in that sector of the hospitality industry. Table 1 illustrates the profile ofthe three expert panels. The three separate Delphi questionnaires, with a self addressed envelope, were mailed as Round 1 tothe appropriate hospitality sector experts. Each panelist received two phone calls, first to verify the receipt of thequestionnaire and the second to encourage them to complete and mail the questionnaire.

Table 1: Profile of Delphi Expert Panel

INDUSTRYSECTOR

PANEL EXPERTISE ROUND 1RESPONDENTS

Lodging Lodging panel consists of Presidents, CEOs, Vice Presidents,Owners, General Managers and Senior Hotel Developmentexecutives with major U.S. and international hotel companies

38

Food Service Food service panel consists of Presidents, CEOs, VicePresidents, Owners, General Managers of Food servicecompanies to include full service and fast food restaurants,contract food service firms, independent restaurants,institutional food services, vending companies, specialty foodservice, food service company vendors, and food serviceconsulting firms.

21

Clubs Club panel consists of CMAA members and members of theboard. Most panelists are club general managers, with a fewvice presidents and CEOs.

21

FINDINGS

Eighty completed questionnaires were received, for an overall response rate of 49.4%. Thirty-eight responses werefrom the lodging panel, 21 from the food service panel, and 21 from club panelists. This study presents Round 1 results fromthe panelists occurring for the year 2007. The items with a mean score of 4.0 or higher have been included in this study.Tables 2, 3, and 4 present the results in descending order of the probability of occurrence.

The Lodging Industry

Table two presents the results of the lodging industry Delphi panelists. The panelists foresee a greater number ofwomen in management positions in 2007. Technology is an area of concern: our panel of experts believes that wirelesstechnology will play an increasing role in operations. The widespread use of cellular phones suggests that thetelecommunications department will become a cost center. Furthermore, consumer databases will be used greater targetmarkets of interest.


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Table 2: Events most likely to Impact the Lodging Sector of the Hospitality Industry: 2007

Lodging MEANThe cost of payroll taxes and employee benefits will be higher. 4.5

The industry will see substantially more women in management positions. 4.3

The workforce will be more culturally diverse. 4.3

Consumers will be more sophisticated and knowledgeable. 4.3

Pervasive use of cell phones will render the telecommunications department into a cost center rather thana profit center.

4.3

Marketing to an aging population will become increasingly important. 4.2

Wireless technology will play an increasing role in various aspects of hotel operations. 4.2

Convenience will increasingly drive consumer choices. 4.2

Cost of real estate will be higher as prime locations become less available. 4.2

Consumers will be more value-driven. 4.2

Consumer databases will lead to more target marketing. 4.1

Because of mergers and acquisitions, the industry will be more consolidated, with fewer companies 4.1

Labor will continue to remain one of the highest cost factors. 4.1

Product differentiation will become increasingly important in growing the business. 4.1

Personalization/customization will be a driving force in marketing. 4.0

Lending terms required by lenders will be more rigorous. 4.0Note: Scale: 5= Very likely to occur; 1 = Not at all likely to occur

The Foodservice Industry

A 1998 National Restaurant Association (NRA) Delphi panel looked at the future industry structure in 2010 andreported some the most likely developments to be a continued intensity in terms of competition, an increase in chainoperations and an increase in multiple concepts within the chains. Our results for 2007, presented in Table 3, echo findingsfrom the NRA Delphi study.


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Table 3: Events most likely to Impact the Foodservice Sector of the Hospitality Industry: 2007

Foodservice MEAN The cost of payroll taxes and employee benefits will be higher. 4.6

Labor will continue to remain one of the highest cost factors. 4.4

Word-of-mouth will still be the most influential form of advertising. 4.4


The industry will develop more concepts that include multi-unit brands under one roof. 4.2

Consumers will be more value driven. 4.2

Marketing to an aging population will become increasingly important. 4.2

Consumer databases will lead to more target marketing. 4.1


In general, managing employees will be more challenging 4.0

The proportion of chains versus independents will increase. 4.0

Convenience will increasingly drive consumer choices. 4.0

Changing demographics will lead to more ethnic driven marketing. 4.0

Foodservice companies will offer improved compensation and other incentives to increaseemployee retention.

4.0

Note: Scale: 5= Very likely to occur; 1 = Not at all likely to occur

The Club Industry

As with the lodging experts, the club Delphi panelists believe that more women will be placed in managementpositions. A continuing trend of the hospitality industry in general is the challenge is the management of employees. Specificto the club industry, the Delphi panelists believe that the trend of the club manager functioning as the chief operating officerand that the largest source of revenue for clubs will be membership dues will continue. These results are listed in Table 4.

Table 4: Events most likely to impact the Club Sector of the Hospitality Industry: 2007

Club MEAN Country (golf) clubs will remain the largest segment of the club industry. 4.6

The cost of payroll taxes and employee benefits will be higher. 4.5

Labor will continue to be the highest cost category for clubs. 4.4

In general, managing employees will be more challenging. 4.4



The management of employees will become more challenging. 4.2

The concept of the manager functioning as the chief operating officer will be become morecommonplace.

4.2

The largest revenue source for clubs will continue to be member dues. 4.2

Convenience will increasingly drive member choices. 4.0

Members will be more sophisticated and knowledgeable. 4.0

Members will want unique hospitality experiences. 4.0

Member health/wellness will be an integral part of a club product offering. 4.0Note: Scale: 5= Very likely to occur; 1 = Not at all likely to occur


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CONCLUSIONS AND IMPLICATIONS

It comes as no surprise that the panelists believe that managing employees will be more challenging by 2007.Cultural diversity in the workforce will continue to rise. Women will hold more management positions. In general, as onemight expect, human resource issues are, and will continue to be, an area of concern for the panelists from all three sectors.

The experts in all three sectors also believe that greater emphasis must be placed on marketing and service quality tothe customer. Targeting the marketing effort will become more commonplace as databases are used. Aging customers, with agreater demand for value increase the importance of marketing strategy. Convenience is recognized to be a driving force inconsumer choices.

Financially related events are on the minds of all the experts, as one might imagine. Labor costs and related benefitsare expected to be higher; the foodservice panelists believe that improved compensation will be required for employeeretention. Real estate costs will be higher for lodging companies, while dues will continue to rise as clubs look to members asthe largest source of revenue. It is interesting to note that only the lodging panel believed technology related events would beprominent in 2007. The use of cellular telephones by guests and wireless technology will play a greater role in lodgingoperations. Branding and concept offerings are predicted to play a greater role in foodservice. The club panelists believe thatmembers will require unique hospitality experiences, something not considered likely by the lodging and foodservice experts.

From a lodging standpoint, when factoring the cost of real estate and the rigor with which lenders evaluate capitalrequests, the trend for continued building in the lodging industry is not as likely through 2007. With the predicted increase inchains and multiple concepts under one roof, independent restaurant operators will face competition like never before. Theclub sector panelists predict more women in management positions, possibly to address the membership issues facing privateclubs recently.

In general, across the three sectors, our panel of experts seem most sure about three events:

1. The workforce will be more diverse: both ethnically and by gender.2. Labor costs will take an increasing chuck of organizational resources: payroll taxes, benefits, and incentives to

retain employees.3. Consumers will continue to demand more: value, convenience, personalization, and unique experiences.

SUMMARY

The purpose of this Delphi study was to make predictions in five areas of concern to hospitality industry managers,investors, lenders, and others associated with the U.S. hospitality industry. The analysis of the results provided initial cluesabout the direction of the industry with important implications for the management of hospitality enterprises in the next 5years. The five prediction categories for the purpose of the study included the industry structure, management of humanresources, operations/information technology, marketing, and finance. The experts who participated in the first round of thisDelphi study believed that escalating costs, human resource issues, differentiated marketing strategies and responding togreater demands by customers were the events most likely to occu

international chrie 2003 proceedings manualm3.ithq.qc.ca/collection/00000144.pdf · preface this...

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