|ognizant Communication Corporation|
VOLUME 6, NUMBER 2
Tourism Analysis, Vol. 6, pp. 81-97
1083-5423/02 $20.00 + .00
Copyright © 2002 Cognizant Comm. Corp.
Printed in the USA. All rights reserved.
Jay Beaman,1 Tzung-Cheng (T. C.) Huan,2 and Metin Kozak3
1Auctor Consulting Associates, Ltd., 465 Andra Court, Cheyenne,
2Graduate Institute of Management, National Chia-yi University, 151 Lin-Sen East Road, Chia-yi, Taiwan, R. O. C. 600
3School of Tourism and Hospitality Management, Mugla University, 48000 Turkey
Recent articles have demonstrated the value of approaching the analysis of repeat travel using Markov models. The tourism literature identifies behavior that can be introduced into Markov models by expanding the state space. The authors have formulated such models. This article addresses the estimation of parameters of a model for first-time and repeat visitors; if the visitors are repeat then being a frequent or less frequent repeater is addressed. Estimation is for 1991 Travel Industries "In-flight Inbound" data for 1991 Japanese pleasure visitors to the US. Because the estimation is not possible using a standard program, a special estimation program was written. The logic and results of the estimation program, including criteria for determining a reasonable fit, are discussed. It is found that parameters of the model can be estimated and that the fit to the data is possibly good enough that residual variance is random. Unfortunately, other models involving different behavior can be expected to fit the data equally well. So, while a practical implication for researchers is that expanded state space models can be estimated, the challenge raised is that research must establish the actual structure of behavior. Only with that structure defined can valid information be produced for managers.
Key words: Markov model; Repeat travel; Segments
Address correspondence to Jay Beaman, Auctor Consulting Associates, Ltd., 465 Andra Court, Cheyenne, WY 82009. Tel: (307) 635-2114; E-mail: firstname.lastname@example.org
Evaluating Customer Satisfaction: A Contingency Model Approach
Dan Toy,1 Deborah Kerstetter,2 and Robin Rager3
1Department of Marketing, California State University, Chico,
307 Tehama Hall - 051, Chico, CA 95929-0051
2Department of Leisure Studies, The Pennsylvania State University, 201 Mateer, University Park, PA 16802
3Department of Health Studies, Texas Woman's University, P.O. Box 425499, Denton, TX 76204
Increased competition between leisure service businesses has forced managers to place greater emphasis on understanding and satisfying their customers' needs. Traditionally a modified version of the Parasuraman, Zeithaml, and Berry service quality model has been used to assess how well businesses are meeting their customers' needs. This approach is flawed, however, because it cannot explain the variability of outcomes. This study demonstrates the considerable flexibility and power of using a contingency approach to leisure service performance/satisfaction modeling. The results indicate that it is an effective research methodology for understanding the determinants of customer satisfaction for targeted clientele and a basis for positioning or repositioning the service mix to meet customers' needs.
Key words: Contingency modeling; Customer satisfaction; Leisure services
Address correspondence to Deborah Kerstetter, Associate Professor
of Leisure Studies, The Pennsylvania State University, 201 Mateer, University
Park, PA 16802. Tel: (814) 863-8988; E-mail: email@example.com
Lori Pennington-Gray1 and Richard A. Spreng2
1Department of Recreation, Parks and Tourism, University
of Florida, PO Box 118209, Gainesville, FL
2Marketing and Supply Chain Management, Michigan State University, East Lansing, MI
This study was designed to perform a cohort analysis of travel preferences. Data from two Canadian studies were used to examine the effects of age, cohort, and time period effects on travel preferences of Canadians. The results indicated that five of the nine travel preference variables were statistically significant. The adjusted R2s for each of the models were examined and the model with the highest adjusted R2 was chosen as the best model. The period model was the best predictor for "the importance of shopping" in pleasure travel. The age-period model explained the importance of "high quality restaurants" preference better than any other model. The age-generation model explained the importance of "national/provincial" parks more than any other model. The period-generation model explained the most variation in responses to the importance of "first-class accommodations." Finally, the age-period-generation model fit the data for the importance of "museums and art galleries" the best. Results showed that the best fitting model was different for each of the travel preference variables. Implications include that the effects of age, cohort, and period have varied effects on preference and are therefore not easily predicted. Recommendations for future studies include developing a set of "core" questions to be used over time at the national or state level, striving to collect panel data, and trying to develop variables that are more generational in nature.
Key words: Cohort analysis; Generation; Tourism; Marketing
Address correspondence to Lori Pennington-Gray, Department of Recreation, Parks and Tourism, University of Florida, PO Box 118209, Gainesville, FL 32611-8209. Tel: (352) 392-4042, ext. 318; Fax: (352) 392-7588; E-mail: firstname.lastname@example.org
Organizational Climate, Perceived Customer Satisfaction, and Revenue per Available Room in Four- and Five-Star Australian Hotels
Michael Davidson,1 Mark L. Manning,2 Peter Brosnan,3 and Nils Timo3
1The Lester E. Kabacoff School of Hotel, Restaurant &
Tourism Administration, University of New Orleans, New Orleans, LA
2School of Tourism and Hotel Management and 3Faculty of Commerce and Management, Griffith University, Australia
Organizational climate, customer satisfaction (as rated by employees), and revenue per available room (REVPAR) were investigated using 1401 employees of 14 hotels. Twenty-two percent of the variance in customer satisfaction between hotels was explained by differences in global organizational climate. Strong support was found for a model proposing seven dimensions of organizational climate to significantly affect customer satisfaction that would, in turn, significantly affect REVPAR. The dimensions of organizational climate explained 30% of the variance in customer satisfaction between hotels, and 23% of the variance in REVPAR between hotels was explained by customer satisfaction. It is argued that should employee perceptions not provide a valid measure of customer satisfaction, and are simply the result of employees in better organizational climates rating customer satisfaction more highly, one must still conclude 23% of the variation in REVPAR between hotels to be the result of variation in organizational climate.
Key words: Organizational climate; Hotels; Customer satisfaction; Revenue; REVPAR
Address correspondence to Michael Davidson, Professor & Director, The Lester E. Kabacoff School of Hotel, Restaurant & Tourism Administration, University of New Orleans, 200 Business Bldg., Lakefront, New Orleans, LA 70148. Tel: (504) 280-7192; Fax: (504) 280-3189; E-mail: email@example.com
Tourism Analysis, Vol. 6, pp. 139-147
1083-5423/02 $20.00 + .00
Copyright © 2002 Cognizant Comm. Corp.
Printed in the USA. All rights reserved.
H. Leslie Furr,1 Mark A. Bonn,2 and Angela Hausman3
1Georgia Southern University, Hotel & Restaurant Management,
Statesboro, GA 30460
2Dedman School of Hospitality, College of Business, Florida State University, Tallahassee, FL
3University of Texas-Pan American, Department of Management, Marketing, and International Business
An increasing number of consumers from every generation are purchasing more goods and services over the Internet each year. Responses obtained from personal interviews with over 13,000 individuals visiting a major Florida destination during 1997, 1998, and 1999 provided the sample for this study of generational consumption and Internet use behavioral patterns. A review of the study population responses to the interview suggested the number of travelers consulting the Internet and booking travel over the Internet increased significantly each year over the last 3 years (1997-1999). In addition, a geographical analysis of Internet users suggested that US residents who traveled more than 100 miles to visit the destination area (Tampa, FL, USA) and used the Internet were likely to be a member of the Generation X'ers or Baby Boomers age group, rather than the Mature Traveler age group. The Internet users who traveled longer distances also had a tendency to be more affluent and better educated than the non-Internet user group who traveled shorter distances to the same destination area. The consumer patterns of individuals who would purchase travel services on the Internet represent an exceptional target market for travel-related products because they reportedly spent significantly more for travel services than their Internet nonuser counterparts. Finally, the article outlines significant differences between information-seeking and purchase behavior across various demographic groups.
Key words: Internet; Purchase behavior; Travel patterns; Consumers; E-commerce; Generations
Address correspondence to H. Leslie Furr, Georgia Southern University, Hotel & Restaurant Management, Statesboro, GA 30460. Tel: (912) 681-5617; E-mail: firstname.lastname@example.org
Apostolos Kobotis and Chris A. Vassiliadis
Department of Business Administration, University of Macedonia, 540 06 Thessaloniki, Greece
This article presents a mathematical model for analyzing the degree of attractiveness of tourist attractions displayed on a tourist brochure. The sites are related to several competitive tourist destinations and represent natural, cultural, and other artificial resources. Then, a mathematical formula is proposed for the evaluation of a tourist site's utility. Finally, the mathematical model is applied to real data.
Key words: Expectancy value model; Tourist attractions; Consumer behavior; Promotion strategies
Address correspondence to Chris A. Vassiliadis, Department of Business Administration, University of Macedonia, 156 Egnatia Str., P.O. Box 1591, 540 06 Thessaloniki, Greece. Tel: #30-31-891581 or 891584; Fax: #30-31-891544; E-mail: email@example.com
Henry Iroegbu1 and Joseph S. Chen2
1Department of Hospitality and Tourism Management, Virginia
Polytechnic Institute and State University, 360 Wallace Hall, Blacksburg,
2Hotel and Restaurant Management Program, Eastern Michigan University, 206B Roosevelt Hall, Ypsilanti, MI 48197
Past tourism impact research has focused on the perceptions of tourism development in rural communities. Only a few studies have assessed the perceived tourism impacts surrounding urban community. This research attempts to delineate the homogeneity of urban residents' reaction toward tourism development. Data collected from a statewide mailing survey on urban tourism impacts in 1997 were analyzed in the study. The resulting data revealed two distinct groups of residents that were labeled as "Tourism Skeptics" and "Tourism Supporters."
Key words: Urban tourism; Tourism skeptics; Tourism supporters; Segmentation
Address correspondence to Joseph S. Chen, Ph.D., CHA, Director, Hotel
and Restaurant Management Program, Eastern Michigan University, 206B Roosevelt
Hall, Ypsilanti, MI 48197. Tel: (734) 487-0845; Fax: (734) 487-0787; E-mail: