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The use of trade-off analysis for the design of holiday travel packages
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The Use of Trade-Off Analysis for the Design of Holiday Travel Packages Hans Miihlbacher Giinther Botschen University
of Innsbruck,
Austria
A model of consumer holiday travel package awareness, preference, and choice presented in the first part is applied to learn the destinations competing with Tirol, Austria, for 1987 summer holiday visitors. Open-ended unaided awareness questions were used to measure holiday destination competitors and the relative topof-mind-shares of Tirol among different target markets. A constant-sum question was used to measure preferences for each respondent toward the destinations he or she was most aware of. Conjoint analysis was used to measure the relative importance of five relevant holiday package features: choice of destination, type of accommodations, number of nights at the destination, total cost per day of the trip, and kind of vacation considered. Cluster analysis based on these relative importances and on the relative utilities of feature categories/levels revealed different consumer segments to be addressed differently with summer holiday package offers.
Introduction Rising consumer incomes and the internationalization of service industries have resulted in increasing globalization of competition among different offers of holiday destinations and trip packages in the last decade. Local and regional tourist agencies, travel operators, and hotel chains, as well as other members of the industry, therefore have felt the strong necessity of heavily promoting their offers. But in real terms, the battlefield among competing services is within the mind of the customer. Given that the consumer has identified some set of holiday trips as worthy of possible choice, and assuming that he or she will not buy all the alternatives in this consideration set at the same time, what affects the consumer’s final choice? Both marketing (e.g., price, destination, or accommodation) as well as consumer (e.g., demographics and life style) variables may exert certain effects. A marketer for a given holiday service needs to learn if her or his offer is a contender: Is the
Address correspondence to Professor keting, University of Innsbruck, Innrain Journal of Business Research 17, 117-131 (1988) 0 1988 Elsevier Science Publishing Co., Inc. 1988 52 Vanderbilt Ave., New York, NY 10017
Dr. Hans Muhlbacher, 52, A-6020 Innsbruck,
Institut fiir Handel, Austria.
Absatz
und Mar-
0148-2963/88/$3.50
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firm’s (station’s, region’s) service present on the battlefield? If yes, how should the offer be designed and positioned to win the battle? Studies using unaided awareness, conjoint analysis, and cluster analysis may be a useful tool to answer the questions. Building on the findings of a few other studies in this field, therefore, this paper will try to further clarify the applicability of conjoint measurement to the explanation and forecasting of customer preferences and choice between competing holiday trip packages. Recommendations for extended research will be given.
Theoretical
Considerations
This study for one part was a replication of a study by Mtihlbacher and Woodside (1987). The purpose was to retest the top-of-mind-awareness (TOMA) hypothesis under differing conditions using a very similar research instrument. On the other hand, it was designed to test the capability of conjoint analysis in combination with cluster analysis to detect and describe differing segments of consumers in the summer holiday travel market. The TOMA holiday destination is an essential part of the consumer’s evoked set of holiday trips. The evoked set includes all the alternatives that a consumer actively considers in purchase deliberation for a specific purchase task (Narayana and Markin, 1975). Previous related travel research by Bronner and de Hoog (1985) found that only a few (four plus or minus two) holiday destinations were considered actively in making vacation choices. By definition, the TOMA destination is the alternative most available to the consumer, i.e., it is the holiday destination the consumer recalls first from long-term memory when asked to think about a specific holiday trip purchase task. Significant positive relationships have been reported between share of TOMA brand and final brand choice (Wilson, 1985; Woodside and Wilson, 1985; Spiggle and Sewall, 1987). Mtihlbacher and Woodside (1987) have found the same relationship between TOMA winter holiday destination and preference for winter vacation packages. The first two hypotheses tested in this study, therefore, are: H,: The order of mentioning of holiday trip destinations in unaided recall is equal to the rank order of preference for these destinations measured by a constant-sum question. Hz: The three holiday trip destinations mentioned in unaided recall have the highest probability of being effectively chosen for a summer holiday trip.
Thus, increasing a given destination’s “share-of-mind” of first mentions among competing holiday destinations in a relevant segment of customers seems to be a useful objective in tourism marketing. Such an objective indicates the need for tracking studies to learn changes to the unaided awareness shares among competing destinations, changes in the holiday destinations included in consumer’s evoked sets, and the conversion rate of first mention to purchase for each holiday trip. Conjoint analysis introduced to marketing research some 10 years ago (Green and Srinivasan, 1978) has proved to be a relevant tool for investigating optimal product design (Cattin and Wittink, 1982; Kucher and Simon, 1987). In studies so far on holiday trip decision behavior and design of touristic services, conjoint analysis was rarely used (Miihlbacher and Woodside, 1987). It allows the detection
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of individual partworths for each product feature (relative importances) and for different levels of each characteristic (relative utilities). Therefore, hypothesis 3 could be tested. H,: The average utilities of holiday trip destinations (mentioned in unaided recall questions) measured by conjoint analysis vary substantially; the average utility for the TOMA destination being significantly higher than for the destinations mentioned second and third.
Another interesting property of conjoint analysis is the fact that it allows a degree of modelling: The deduction of attribute utilities permits the predection of likely preference between product profiles, even though those profiles may not specifically have been included in the original study (Morgan, 1987). Because of its micro level measurement approach aggregation of relative importances/utilities seldom makes much sense (Schmidt, 1987). Therefore, cluster analysis was applied in this study to detect relevant consumer segments with different holiday trip preferences and choice behavior. A summary of how consumer and marketing variables are hypothesized to influence holiday trip awareness, preference, intention, and choice is presented in Figure 1. Nine arrows are included in the figure to indicate substantial relationships. Arrows 1 and 2 summarize the influences of consumer and marketing variables on consumer holiday destination awareness and evoked set membership, respectively. Arrow 4 depicts the hypothesis that awareness and order of mention in the evoked set of holiday destinations is related positively to trip preference. Arrow 3 depicts the moderating influence of marketing variables on the awareness-preference relationship, e.g., price increase or change in the package design associated with the TOMA holiday destination may lower the consumer’s preference toward the trip relative to competing destinations in the consumer’s evoked set. Consumer variables, such as previous vacation purchase experience, household income, or lifestyle, are shown in Figure 1 to influence consumer holiday destination awareness and trip preference but not intention to purchase or vacation choice. A consumer’s decision process for a summer holiday trip in many cases should be rather involving. Consumer variables, however, have not been found to directly increase the explanation of consumer brand intention and choice for highly involving purchase processes (Spiggle and Sewall, 1987). Following the results of the study by Muhlbacher and Woodside (1987) in the present study household income was hypothesized to be an important modifier of the role of prices of vacation offers. Subjects with higher-versus-lower household incomes should be found to have less sensitivities to changes in cost of destination choices. H4: The higher the level of a respondent’s annual household income, the smaller the loss of utility of holiday trip packages with higher cost per day. Similarly, the home country of consumers was hypothesized to have a significant impact on consumer preferences toward competing holiday trip packages (arrow 4 in Fig. 1). Hs: The relative importance of holiday trip features English-speaking respondents.
differs among German-
and
CONSUMER VARIABLES Previous purchase experience l Life cycle, Income, Age l Home country 0 Lifestyle l
1
MARKETING VARIABLES l Package Design 0 Promotion 0 Pricing l Distribution <
/
<
\
f
2
f
\ CONSUMER HOLIDAY DESTINATION AWARENESS
/
Evoked Set
6
/
\ 4
.
CONSUMER HOLIDAY TRIP INTENTIONS AND PLANS
SITUATIONAL VARIAGLES l Benefits sought l Company expected
CONSUMER HOLIDAY TRIP CHOICE / PURCHASE
Figure 1. General
Model
of holiday
trip awareness
and choice
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Situational variables have been found to influence the specific set of brands evoked in a purchase decision process (Chandon and Strazzieri, 1986) and to moderate the relationship between consumer brand intention and brand choice (Bearden and Woodside, 1976; Belk, 1974) as well as purchase intention and store choice (Filser, 1986). The direct influence of consumer holiday trip intention on choice (arrow 9) and the moderating influences of situational variables (arrows 6 and 8) are depicted in Figure 1.
The
Study In March-April 1987, an exploratory study was designed and conducted. Subjects in the study were winter holiday travel parties from the United States, Australia, and all parts of Europe to the State of Tirol, Austria. A total of 460 subjects participated in the study. The biggest group was German-speaking (229 Germans and seven Swiss), followed by native English-speaking subjects (110). Each subject participated in a personal interview lasting 20 minutes. For comparative reasons, the survey form was structured following the one used by Mtihlbacher and Woodside (1987). It included unaided questions on summer holiday destinations that first came to mind; a constant-sum question to measure preference toward the destinations in the consumers’ evoked sets; and a series of trade-off questions related to the destinations that first came to mind-costs of the holiday per day, type of accommodation used, length of stay, and preferred activities during the holiday trip. Demographic and travel behavior questions were also asked.
The Sample A convenience sample of 480 subjects participating in winter holiday activities in the State of Tirol, Austria, was used in the study. A quota sample of respondents in three age groups: 18-35, 36-55, and 56+ was specified before the data were collected; the sample was planned to include approximately equal proportions of Germanand English-speaking respondents, males and females, and the three age groups in a proportion of 6:4:2 respectively. This unequal proportion of age groups was chosen because the summer holiday season in Tirol is characterized by a lack of tourists of age 18-35. Therefore, there is some special interest in learning more details about holiday destination choice factors and behavior in that age group. Persons selected for inclusion in the sample were approached while they were seated in the outside areas of restaurants located next to ski slopes in 28 different winter holiday resorts spread all over Tirol or in coffee shops in Innsbruck, the capital of Tirol. Forty postgraduate students from the University of Innsbruck conducted the personal interviews. The interviewers were fluent in both German and English. Each interviewer was trained and instructed to complete a minimum of twelve interviews following the quota allocated. While the respondents participating in the study were about evenly divided among males and females as well as Germanand English-speaking respondents, the planned proportions of age groups got distorted by the fact that 15 respondents had to be eliminated from the final sample because of their age (below 18). Five
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other questionnaires could not be included in data processing because of errors in data collection. The final sample of 460 participants was composed of 46% of respondents between 18-35, 39% between 36-55, and 15% of respondents older than 55.
Operationalizations and Survey Instrument A 7-page questionnaire was designed, pretested, revised, and used in the study. It was prepared in both German and American/English versions. Data on the respondents’ TOMA destination were collected using the following question: Please think about a summer holiday trip in 1987. Among all the summer holiday trips you might actually take, what destination first comes to mind? Following this question, the respondents were requested to describe what made them think about this destination first and if they thought of this destination as being the best place for rest and relaxation, active experience, or education and culture. Then, the respondents were requested to name two additional summer holiday vacations using the following questions: When you think about other summer holiday destinations, what destination comes to your mind? Beside the two destinations you just named, please mention just one more destination that comes to mind.
Between the two questions and following the third unaided awareness question, again, the respondents were requested to describe what made them think about the respective destination and what kind of holiday trip they associated with this destination. This procedure is similar to the one applied by Woodside (1986) as well as Miihlbacher and Woodside (1987). It was advocated by Wilson (1981) to learn the content and order of availability of brands in consumers’ evoked sets. Even though Wilson had studied frequently purchased product categories where product as well as purchase involvement may be significantly lower than for summer holiday destination choices, Woodside and Miihlbacher in their studies have found nomological validity using this operationalization of TOMA destinations and destination preference, i.e., the prediction that order of availability was associated positively to destination preference was supported. Two methods were used to measure preference toward the destinations mentioned in response to the unaided awareness questions: a constant-sum measure and conjoint analysis. The constant-sum scale is an effective measure of consumer preferences toward competing services because of two properties (Hughes, 1971; Wilson, 1981): 1. It is a relative measure, less sensitive to individual response styles and interscale. pretation problems of adjectives than, e.g., a semantic differential 2. The constant-sum measures the psychological differences between stimuli, and the data have properties of interval measurements.
J BUSN RES 1988:17:117-131
Trade-Off Analysis For Holiday Packages For the constant-sum
measure,
the respondents
123
were asked:
Please think about the three summer holiday destinations you mentioned for 1987. If you had a total of 20 days to spend on summer holidays, how many of these days would you spend at each of the three destinations? The respondents were able to answer this question with ease. Conjoint analysis has the same measurement properties that apply to constantsum scales. It provides useful answers to the question of what features increase the consumer’s perceived utility toward a summer holiday trip (package), and what specific actions are likely to be most effective against competing offers (packages) to increase preference and the likelihood of choice of destination X. For the present study, an incomplete fractional factorial design (Banks, 1965) was used to present the stimuli to the respondents in a “full profile approach” (Schmidt, 1987). As research by Alpert et al. (1978), Jain et al. (1978), Montgomery et al. (1977), and Oppedijk et al. (1977) has shown, this approach roughly yields the same utilities than the “two factors at a time approach,” when the number of features and levels are limited to up to five attributes and three levels per attribute. The respondents were asked to rank order 16 feature combinations-each one describing a summer holiday trip package-with three levels for each of the five following attributes: destination, accommodation, number of nights, cost per day, and type of vacation. This method kept the task relatively simple and was less fatiguing for the respondents compared to rank-ordering all possible combinations of holiday trip features. For the actual conjoint analysis task, the interviewer requested the respondent to wait a moment. The interviewer than printed the names of the three destinations the respondent had mentioned in the unaided recall in the appropriate locations of the questionnaire. The respondents were then shown the 16 personalized alternative holidays and asked to rank-order them from most to least preferred. To increase realism, a modification to the complete ranking task was used in the way suggested by Miihlbacher and Woodside (1987): The respondents were instructed to “Strike out all the choices that you would not consider.” Pretests had shown that several respondents, after having ranked some of the presented alternatives from most to least preferred, reported that the remaining alternatives were all equally unattractive or rank-ordering the remaining alternatives seemed unrealistic because each had one or more feature descriptions that would eliminate the alternative from consideration. The additional instruction could be used without difficulty because conjoint analysis permits the handling of ties in rankings. Data on age and income were collected by asking the respondents to refer to two cards listing five income levels and seven age levels. The respondents were asked to mention a letter code that best described their annual household income level and age level. Information on monthly income was collected among Germanspeaking respondents based on the belief that those respondents would be more likely to relate to monthly and not annual income levels. Finally, all respondents who had not mentioned Tirol as one of the summer holiday trip destinations that comes to their mind in unaided recall were asked why they did not choose Tirol as a destination for summer holidays.
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Analysis An individual level analysis was used to estimate each subject’s preferences towards the different feature level categories. Analysis of variance was used to test the hypotheses. The first step in utility score calculations for one respondent is to sum the ranks for each feature level category. The lower the rank sum, the higher the preference. The raw rank sums for the feature level categories are then resealed as estimated utilities. The level category obtaining the best score is resealed to be 1.00. The worst level category is restored to 0.00. All other scores are repositioned proportionally within that range. In the second step, the relative importance score for each feature is determined. The relative importance scores are the relative weight a respondent places on each feature. This weight is derived from the range of raw rank sums between the level categories of each feature. The greater the range, the higher the importance of the feature for the respondent. Each feature range is transformed to a percentage of the sum of all ranges. Therefore the relative importances of features sum up to 100%. Other algorithms for conjoint analysis are available (see Green and Srinivasan, 1978; Solgaard, 1987). The approach described here is one of the simplest, and, unlike other methods, it provides utilities ranging from 0 to 1.00 for feature level categories and importances between 0 and 100% for each feature; thus, the conjoint analysis detailed here is intuitively appealing for marketing strategists. Results
Hypotheses 1 to 3 Were Supported by the Results of the Study The order of the three 1987 summer holiday destinations were mentioned in unaided recall was associated positively with the responses to the constant-sum question, thereby reinstating the results of Bronner and de Hoog (1985) as well as of Muhlbather and Woodside (1987). The first-mentioned destination received an average of 53% of the 20 days among the 460 respondents; the second mentioned destination received an average time of 27%; the third mentioned destination received an average time of 20%. The ANOVA results were statistically significant (p
(p
Trade-Off
Analysis
For Holiday
Table 1. Average
J BUSN RES 1988:17:117-131
Packages
Relative
Utilities
of Cost-per-Day
for Different
125
Household
Income Groups Cost per Day Cases”
$50
$100
$150
Under $30,000 $30,000-$70,000
208 201
.75 .64
.43 .50
.20 .22
Above $70.000 F-Value
41 450
.S8 7.53 .OOO
.43 2.67 .07
1.15
Household Income
P< “10 respondents
refused to indicate
.27 .32
their income.
day of the holiday, whereas for respondents with annual household incomes under $30,000 (U.S.), the average utilities declined significantly with increases in total cost per day. In the present study, the average utilities declined for all household income groups with increasing total cost per day. As Table 1 shows, on the one hand, the relative utility of low cost per day declined significantly with increasing annual household income; on the other hand, the decline of average relative utilities with increasing cost per day was significantly smaller for higher income groups in comparison to the group with the lowest annual household income levels (see Figure 2). Most interestingly, however, the group of respondents reporting annual household incomes between $30,000 and $70,000 (U.S.) showed the lowest relative importance of that feature for their holiday trip decision-making process (ANOVA pCO.01). The relative importance of the five summer holiday package features tested did significantly differ among the respondents (ANOVA p
Between$30.000.and$ 70.000.-
Above$ 70.000.-
75 .64 .58 SO 43
43
I
20
\
i $50
$100
$150
Figure 2. Relative
utilities
$50
$100
$
of cost per day in different
15ii annual
27
\
22
\
L household
income
groups.
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H. Muhlbacher and G. Botschen
two studies. Whereas for winter holiday trips the destination seems to play a major role in the decision process, the summer holiday choice is strongly based on cost per day (31%). The destination even has less relative importance (14%) than the three other features-type of vacation (19%), number of nights (18%), and accommodations (18%)-tested. The destinations first mentioned in unaided recall differed significantly by country of origin of the respondents (Table 2): U.S. citizens significantly first mentioned the United States as the country for a summer holiday trip more often (22%) than other destinations. The reason for that fact, which was also detected for winter holiday trips of U.S. citizens in the study of Miihlbacher and Woodside (1987), may be that most Americans spending their holidays in Tirol are only one-time visitors. Germans mentioned Italy most often in as first place (18%) they would vacation, followed by Tirol (14%) and the rest of Austria (ll%), as well as Greece (11%). Of the 68 respondents from Great Britain, Ireland, and the Scandinavian countries, 65% mentioned Mediterranean countries as their first 1987 summer holiday destination. Only 6% mentioned Tirol first. The winter holiday guests from France and Belgium (49) together with the tourists from other Mediterranean countries (37) were the only ones to name Tirol in the first place as a summer holiday trip destination for 1987. These results indicate a strong tendency of people from northern Europe to go south for summer vacation, whereas people living in the southern parts of Europe express an interest in spending their summer holidays in the Alps. Cluster analysis of the relative importances the individual respondents put on the features tested revealed the four clusters shown in Table 3. The clusters did not significantly differ in average annual household income, members’ age, sex, or home country. The preferred type of vacation was no valid discriminator either. Significant differences appeared in the cluster members’ holiday trip booking behavior (p
Total Rank
25 (11) 2 (4) 1 (5) 17 (3.7) 8
3 (15) 35 (7.5) 5
2 (10)
7 (3) 0 (-)
4 (6) 2 (4)
3 (8)
Turkey
52 (11.3) 3
2 (10)
4 (8)
64 (13.9) 1
0 (-1
3 (6)
43 (18)
38 (8.3) 4
0 (6_)
20 (8) 3 (6)
7 (10) 6 (12)
8 (12) 6 (12)
11 (16) 8 (16)
2 (5)
Spain
4 (11)
Italy
First Mentioned
2 (5)
Greece
Destination
(%)
From Different Home Countries
25 (11)
by Respondents
56 (12.2) 2
5 (10)
32 (14)
3 (4) 2 (4)
5 (14)
Mediterranean Britain, Ireland, Scandinavia France, Belgium Germany, Switzerland U.S.A. Rest of the world
Rest of Austria
4 (6) 8 (16)
Tirol
First Mentioned
Origin
Table 2. Destinations
30 (6.5) 7
0 (-_)
10 (4) 2 (4)
12 (18) 2 (4)
4 (11)
France
10 (50) 135 (29.4)
2 (10) 33 (7.2) 6
67 (28) 20 (40)
13 (19) 11 (22)
14 (38)
Others
7 (3) 11 (22)
6 (9) 4 (8)
3 (8)
North America
460 (100)
20
236 50
68 49
37
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Table 3. Clusters of Respondents
with Different Relative Importances Clusters
Features
of Features
(No. of Cases)
Cl (68)
c2 (17)
C3 (67)
c4 (308)
31 16 29 14 10
10 11 12 55 12
10 61 10 10 9
17 29 19 19 16
Number of Days Cost per day Type of vacation Accommodation Destination ANOVA,
and G. Botschen
between groups p < 0.00.
small-but-decided group of people that chooses the same location at the same place for summer vacation every year. A total of 308 respondents (66% of the sample) formed cluster 4. As for the respondents in cluster 3, for them, too, cost per day was the most important feature in summer holiday trip choice. But additionally, all the other features were also strongly considered in the decision process. It seems that this group of consumers, nearly evenly split between early (41%) and late (59%) bookers and without a special profile concerning the people accompanying them on their holiday trip, evaluated the offered alternative trips using all the features with approximately the same weight. Cost per day may have served as a kind of decisive criterion for the final choice. Cluster analysis based on individual relative utilities of feature level categories produced the results exhibited in Table 4. Clusters differed significantly by age (~~0.02) and income of respondents (p
J BUSN RES 1988:17:117-131
Trade-Off Analysis For Holiday Packages Table 4. Results
of Cluster Analysis Based on Individual Relative Utilities of Feature
Level-Categories
Variable
Cluster 1 (n = 24)
Cluster 2 (n = 303)
Cluster 3 (n =77)
Cluster 4 (n = 56)
Mean of Total Sample (n = 460)
.7233 .3460 5654
.5373 .4601 .3844
.4650 .4223 .4013
.4961 .3916 .3262
.5299 .4395 .3896
.8998 .8467 .2519
.6211 .6102 .4668
.5382 .6250 .5156
.7777 .4088 .2094
.6408
.2575 .6664 .7108
.3597 .5994 .4227
.5008 .3112 .4766
.5687 .2546 .3904
.4035 .5127 .4428
.7403 .6731 .2213
.7979 .4203
.3072 .7596 .2218
.5551 .1890 .4698
.6832 .4621 .2137
.3596 .3098 .5354
.4082 .2513 .5588
.4314 .4727 .3840
Destination First mentioned Second mentioned Third mentioned Accommodations Bed and breakfast Apartment Hotel Number of days 1 Week 2 Weeks 3 Weeks Cost per Day $50 $100 $150 Type of Vacation Rest and relaxation Active experience Education and culture ANOVA,
.1696 .4997 .6753
.1637 .4747 .5529
.6005 .4324
between groups p < 0.00
The fourth cluster detected is rather small (n = 24). It consists of respondents mainly 3.5S-years old with an annual household income between $30,000 and $70,000, preferring longer summer vacations alone or with their family at low to middle cost per day for the purpose of education and culture. Bed and breakfast places and apartments have nearly the same utility for this group, but dominate the choice of hotel by far. The TOMA destination is strongly preferred. It is interesting to note that when they were asked directly, “would you say a holiday/vacation at this destination is mainly for rest and relaxation, for active experience, or for education and culture?” more than half of all respondents answered “for rest and relaxation.” Conjoint analysis, however, revealed that there is not one cluster where this level/category of type of vacation has earned the highest relative utility. Most respondents seek active experience during their summer holidays (cluster 2). All three other clusters are special cases of people going for education and culture: one group seeking a short trip at average cost per day in the accommodation available, the other group preferring long vacations at low cost in bed and breakfast places, and the third group with a strong preference for bed and breakfast places during short and cheap trips. Comparison of results of the two cluster analyses based on feature importances and on relative utilities of level categories, respectively, leads to the conclusion that there exists strong congruence between clusters 4 (relative importance) and 2 (relative utilities) as well as clusters 1 (relative importance) and 3 (relative utilities). For the other four clusters, the similarity is less evident.
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Limitations The findings reported here are exploratory. They are based on a convenience sample. The features of holiday travel packages tested were chosen by the researchers on an ad hoc basis. The findings, therefore, should be viewed only to be illustrative as to how consumer holiday trip awareness, preference, feature importances, and utilities of feature level categories can be measured and related to a general model of holiday trip awareness and choice. A qualitative study to clarify the question of what features are relevant for the consumers in what holiday trip decision situation and a large-scale, representative sample of visitors to Tirol is needed before any conclusions on TOMA shares of Tirol and its competitors can be reached, and how the holiday travel packages might be designed to increase their perceived utility among different target markets.
Recommendations Tracking unaided awareness among competing holiday destinations for different potential target markets is recommended based on the results of the present study and similar findings reported elsewhere, e.g., Bronner and de Hoog (1985) and Miihlbacher/Woodside (1987). Such tracking research provides useful information on what specific destinations are actually competing on the battlefield within the consumer’s mind. Research using conjoint analysis offers useful information on what might be done to strengthen preference of holiday destination X or, for competing holiday travel packages, to overcome the consumers’ preference of package Y. The use of conjoint analysis forces the marketing strategist to learn what features and feature level categories are most relevant for influencing the utilities of competing holiday destinations for different target markets. Consequently, the specific features and level categories can be incorporated in experimental designs that permit conjoint analysis to estimate the consumers’ acceptance of different designs of holiday travel packages. References Alpert,
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Montgomery, D. B., Wittink, D., Glaze, Th., A Predictive Test of Individual Level Concept Evaluation and Trade-off Analysis, Working Paper, College of Business Administration, Stanford University, Cal., 1977. Morgan, R. P., Brand/Price Trade-Off-Where we stand now, in Micro and Macro Market Modelling: Research on Prices, Consumer Behaviour and Forecasting, EMACi ESOMAR, Tutzing, Germany, October, 1987, pp. 263-275. Muhlbacher, H., and Woodside, A. G., Conjoint Analysis of Consumer Preferences Toward Purchasing Competing Services in Micro and Macro Market Modelling: Research on Prices, Consumer Behaviour and Forecasting, EMAC/ESOMAR, Tutzing, Germany, October, 1987, pp. 299-319. Narayana, M., and Markin, R. J., Consumer Behavior and Product Performance: Alternative Conceptualization, Journal of Marketing 39 (October 197.5): 1-6.
An
Oppedijk v. V., Walle, M., and Beazley, D., An Investigation of Alternative Methods of Applying the Trade-off Model, Journal of The Marketing Research Society 19 (1977): 2-9. Schmidt, M., An Empirical Evaluation of Some Aggregation Techniques and Estimation Algorithms in Conjoint Analysis in Micro and Macro Marketing Modelling: Research on Prices, Consumer Behaviour and Forecasting, EMACIESOMAR, Tutzing, Germany, October, 1987, pp. 135-156. Solgaard, H. S., A Comparison of Conjoint and Logit Modelling of a Single Consumer’s Evaluation of a Choice Set, in Micro and Macro Marketing Mode&g: Research on Prices, Consumer Behaviour and Forecasting, EMAC/ESOMAR, Tutzing, Germany, October, 1987, pp. 97-111. Spiggle, S., and Sewall, M. A. A Choice Marketing, 51 (April 1987): 97-111. Wilson, E. J., A Procedure for the Analysis Advertising Research 21 (1981): 31-38.
Sets
Model
of Consumer
of Retail Decision
Selection, Making,
Journal
of
Journal of
Woodside, A. G., Measuring Customer Awareness and Share-of-Requirements Awarded to Competing Industrial Distributors, in Proceedings, Division 23. J. Saegert and J. C. Anderson, eds., American Psychology Association, Washington, D. C., 1986. Woodside, A. G., and Wilson, E. J. Effects of Consumer Awareness of Brand Advertising on Preference, Journal of Advertising Research 25 (1986): 41-48.
117
The Use of Trade-Off Analysis for the Design of Holiday Travel Packages Hans Miihlbacher Giinther Botschen University
of Innsbruck,
Austria
A model of consumer holiday travel package awareness, preference, and choice presented in the first part is applied to learn the destinations competing with Tirol, Austria, for 1987 summer holiday visitors. Open-ended unaided awareness questions were used to measure holiday destination competitors and the relative topof-mind-shares of Tirol among different target markets. A constant-sum question was used to measure preferences for each respondent toward the destinations he or she was most aware of. Conjoint analysis was used to measure the relative importance of five relevant holiday package features: choice of destination, type of accommodations, number of nights at the destination, total cost per day of the trip, and kind of vacation considered. Cluster analysis based on these relative importances and on the relative utilities of feature categories/levels revealed different consumer segments to be addressed differently with summer holiday package offers.
Introduction Rising consumer incomes and the internationalization of service industries have resulted in increasing globalization of competition among different offers of holiday destinations and trip packages in the last decade. Local and regional tourist agencies, travel operators, and hotel chains, as well as other members of the industry, therefore have felt the strong necessity of heavily promoting their offers. But in real terms, the battlefield among competing services is within the mind of the customer. Given that the consumer has identified some set of holiday trips as worthy of possible choice, and assuming that he or she will not buy all the alternatives in this consideration set at the same time, what affects the consumer’s final choice? Both marketing (e.g., price, destination, or accommodation) as well as consumer (e.g., demographics and life style) variables may exert certain effects. A marketer for a given holiday service needs to learn if her or his offer is a contender: Is the
Address correspondence to Professor keting, University of Innsbruck, Innrain Journal of Business Research 17, 117-131 (1988) 0 1988 Elsevier Science Publishing Co., Inc. 1988 52 Vanderbilt Ave., New York, NY 10017
Dr. Hans Muhlbacher, 52, A-6020 Innsbruck,
Institut fiir Handel, Austria.
Absatz
und Mar-
0148-2963/88/$3.50
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firm’s (station’s, region’s) service present on the battlefield? If yes, how should the offer be designed and positioned to win the battle? Studies using unaided awareness, conjoint analysis, and cluster analysis may be a useful tool to answer the questions. Building on the findings of a few other studies in this field, therefore, this paper will try to further clarify the applicability of conjoint measurement to the explanation and forecasting of customer preferences and choice between competing holiday trip packages. Recommendations for extended research will be given.
Theoretical
Considerations
This study for one part was a replication of a study by Mtihlbacher and Woodside (1987). The purpose was to retest the top-of-mind-awareness (TOMA) hypothesis under differing conditions using a very similar research instrument. On the other hand, it was designed to test the capability of conjoint analysis in combination with cluster analysis to detect and describe differing segments of consumers in the summer holiday travel market. The TOMA holiday destination is an essential part of the consumer’s evoked set of holiday trips. The evoked set includes all the alternatives that a consumer actively considers in purchase deliberation for a specific purchase task (Narayana and Markin, 1975). Previous related travel research by Bronner and de Hoog (1985) found that only a few (four plus or minus two) holiday destinations were considered actively in making vacation choices. By definition, the TOMA destination is the alternative most available to the consumer, i.e., it is the holiday destination the consumer recalls first from long-term memory when asked to think about a specific holiday trip purchase task. Significant positive relationships have been reported between share of TOMA brand and final brand choice (Wilson, 1985; Woodside and Wilson, 1985; Spiggle and Sewall, 1987). Mtihlbacher and Woodside (1987) have found the same relationship between TOMA winter holiday destination and preference for winter vacation packages. The first two hypotheses tested in this study, therefore, are: H,: The order of mentioning of holiday trip destinations in unaided recall is equal to the rank order of preference for these destinations measured by a constant-sum question. Hz: The three holiday trip destinations mentioned in unaided recall have the highest probability of being effectively chosen for a summer holiday trip.
Thus, increasing a given destination’s “share-of-mind” of first mentions among competing holiday destinations in a relevant segment of customers seems to be a useful objective in tourism marketing. Such an objective indicates the need for tracking studies to learn changes to the unaided awareness shares among competing destinations, changes in the holiday destinations included in consumer’s evoked sets, and the conversion rate of first mention to purchase for each holiday trip. Conjoint analysis introduced to marketing research some 10 years ago (Green and Srinivasan, 1978) has proved to be a relevant tool for investigating optimal product design (Cattin and Wittink, 1982; Kucher and Simon, 1987). In studies so far on holiday trip decision behavior and design of touristic services, conjoint analysis was rarely used (Miihlbacher and Woodside, 1987). It allows the detection
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119
of individual partworths for each product feature (relative importances) and for different levels of each characteristic (relative utilities). Therefore, hypothesis 3 could be tested. H,: The average utilities of holiday trip destinations (mentioned in unaided recall questions) measured by conjoint analysis vary substantially; the average utility for the TOMA destination being significantly higher than for the destinations mentioned second and third.
Another interesting property of conjoint analysis is the fact that it allows a degree of modelling: The deduction of attribute utilities permits the predection of likely preference between product profiles, even though those profiles may not specifically have been included in the original study (Morgan, 1987). Because of its micro level measurement approach aggregation of relative importances/utilities seldom makes much sense (Schmidt, 1987). Therefore, cluster analysis was applied in this study to detect relevant consumer segments with different holiday trip preferences and choice behavior. A summary of how consumer and marketing variables are hypothesized to influence holiday trip awareness, preference, intention, and choice is presented in Figure 1. Nine arrows are included in the figure to indicate substantial relationships. Arrows 1 and 2 summarize the influences of consumer and marketing variables on consumer holiday destination awareness and evoked set membership, respectively. Arrow 4 depicts the hypothesis that awareness and order of mention in the evoked set of holiday destinations is related positively to trip preference. Arrow 3 depicts the moderating influence of marketing variables on the awareness-preference relationship, e.g., price increase or change in the package design associated with the TOMA holiday destination may lower the consumer’s preference toward the trip relative to competing destinations in the consumer’s evoked set. Consumer variables, such as previous vacation purchase experience, household income, or lifestyle, are shown in Figure 1 to influence consumer holiday destination awareness and trip preference but not intention to purchase or vacation choice. A consumer’s decision process for a summer holiday trip in many cases should be rather involving. Consumer variables, however, have not been found to directly increase the explanation of consumer brand intention and choice for highly involving purchase processes (Spiggle and Sewall, 1987). Following the results of the study by Muhlbacher and Woodside (1987) in the present study household income was hypothesized to be an important modifier of the role of prices of vacation offers. Subjects with higher-versus-lower household incomes should be found to have less sensitivities to changes in cost of destination choices. H4: The higher the level of a respondent’s annual household income, the smaller the loss of utility of holiday trip packages with higher cost per day. Similarly, the home country of consumers was hypothesized to have a significant impact on consumer preferences toward competing holiday trip packages (arrow 4 in Fig. 1). Hs: The relative importance of holiday trip features English-speaking respondents.
differs among German-
and
CONSUMER VARIABLES Previous purchase experience l Life cycle, Income, Age l Home country 0 Lifestyle l
1
MARKETING VARIABLES l Package Design 0 Promotion 0 Pricing l Distribution <
/
<
\
f
2
f
\ CONSUMER HOLIDAY DESTINATION AWARENESS
/
Evoked Set
6
/
\ 4
.
CONSUMER HOLIDAY TRIP INTENTIONS AND PLANS
SITUATIONAL VARIAGLES l Benefits sought l Company expected
CONSUMER HOLIDAY TRIP CHOICE / PURCHASE
Figure 1. General
Model
of holiday
trip awareness
and choice
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Situational variables have been found to influence the specific set of brands evoked in a purchase decision process (Chandon and Strazzieri, 1986) and to moderate the relationship between consumer brand intention and brand choice (Bearden and Woodside, 1976; Belk, 1974) as well as purchase intention and store choice (Filser, 1986). The direct influence of consumer holiday trip intention on choice (arrow 9) and the moderating influences of situational variables (arrows 6 and 8) are depicted in Figure 1.
The
Study In March-April 1987, an exploratory study was designed and conducted. Subjects in the study were winter holiday travel parties from the United States, Australia, and all parts of Europe to the State of Tirol, Austria. A total of 460 subjects participated in the study. The biggest group was German-speaking (229 Germans and seven Swiss), followed by native English-speaking subjects (110). Each subject participated in a personal interview lasting 20 minutes. For comparative reasons, the survey form was structured following the one used by Mtihlbacher and Woodside (1987). It included unaided questions on summer holiday destinations that first came to mind; a constant-sum question to measure preference toward the destinations in the consumers’ evoked sets; and a series of trade-off questions related to the destinations that first came to mind-costs of the holiday per day, type of accommodation used, length of stay, and preferred activities during the holiday trip. Demographic and travel behavior questions were also asked.
The Sample A convenience sample of 480 subjects participating in winter holiday activities in the State of Tirol, Austria, was used in the study. A quota sample of respondents in three age groups: 18-35, 36-55, and 56+ was specified before the data were collected; the sample was planned to include approximately equal proportions of Germanand English-speaking respondents, males and females, and the three age groups in a proportion of 6:4:2 respectively. This unequal proportion of age groups was chosen because the summer holiday season in Tirol is characterized by a lack of tourists of age 18-35. Therefore, there is some special interest in learning more details about holiday destination choice factors and behavior in that age group. Persons selected for inclusion in the sample were approached while they were seated in the outside areas of restaurants located next to ski slopes in 28 different winter holiday resorts spread all over Tirol or in coffee shops in Innsbruck, the capital of Tirol. Forty postgraduate students from the University of Innsbruck conducted the personal interviews. The interviewers were fluent in both German and English. Each interviewer was trained and instructed to complete a minimum of twelve interviews following the quota allocated. While the respondents participating in the study were about evenly divided among males and females as well as Germanand English-speaking respondents, the planned proportions of age groups got distorted by the fact that 15 respondents had to be eliminated from the final sample because of their age (below 18). Five
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other questionnaires could not be included in data processing because of errors in data collection. The final sample of 460 participants was composed of 46% of respondents between 18-35, 39% between 36-55, and 15% of respondents older than 55.
Operationalizations and Survey Instrument A 7-page questionnaire was designed, pretested, revised, and used in the study. It was prepared in both German and American/English versions. Data on the respondents’ TOMA destination were collected using the following question: Please think about a summer holiday trip in 1987. Among all the summer holiday trips you might actually take, what destination first comes to mind? Following this question, the respondents were requested to describe what made them think about this destination first and if they thought of this destination as being the best place for rest and relaxation, active experience, or education and culture. Then, the respondents were requested to name two additional summer holiday vacations using the following questions: When you think about other summer holiday destinations, what destination comes to your mind? Beside the two destinations you just named, please mention just one more destination that comes to mind.
Between the two questions and following the third unaided awareness question, again, the respondents were requested to describe what made them think about the respective destination and what kind of holiday trip they associated with this destination. This procedure is similar to the one applied by Woodside (1986) as well as Miihlbacher and Woodside (1987). It was advocated by Wilson (1981) to learn the content and order of availability of brands in consumers’ evoked sets. Even though Wilson had studied frequently purchased product categories where product as well as purchase involvement may be significantly lower than for summer holiday destination choices, Woodside and Miihlbacher in their studies have found nomological validity using this operationalization of TOMA destinations and destination preference, i.e., the prediction that order of availability was associated positively to destination preference was supported. Two methods were used to measure preference toward the destinations mentioned in response to the unaided awareness questions: a constant-sum measure and conjoint analysis. The constant-sum scale is an effective measure of consumer preferences toward competing services because of two properties (Hughes, 1971; Wilson, 1981): 1. It is a relative measure, less sensitive to individual response styles and interscale. pretation problems of adjectives than, e.g., a semantic differential 2. The constant-sum measures the psychological differences between stimuli, and the data have properties of interval measurements.
J BUSN RES 1988:17:117-131
Trade-Off Analysis For Holiday Packages For the constant-sum
measure,
the respondents
123
were asked:
Please think about the three summer holiday destinations you mentioned for 1987. If you had a total of 20 days to spend on summer holidays, how many of these days would you spend at each of the three destinations? The respondents were able to answer this question with ease. Conjoint analysis has the same measurement properties that apply to constantsum scales. It provides useful answers to the question of what features increase the consumer’s perceived utility toward a summer holiday trip (package), and what specific actions are likely to be most effective against competing offers (packages) to increase preference and the likelihood of choice of destination X. For the present study, an incomplete fractional factorial design (Banks, 1965) was used to present the stimuli to the respondents in a “full profile approach” (Schmidt, 1987). As research by Alpert et al. (1978), Jain et al. (1978), Montgomery et al. (1977), and Oppedijk et al. (1977) has shown, this approach roughly yields the same utilities than the “two factors at a time approach,” when the number of features and levels are limited to up to five attributes and three levels per attribute. The respondents were asked to rank order 16 feature combinations-each one describing a summer holiday trip package-with three levels for each of the five following attributes: destination, accommodation, number of nights, cost per day, and type of vacation. This method kept the task relatively simple and was less fatiguing for the respondents compared to rank-ordering all possible combinations of holiday trip features. For the actual conjoint analysis task, the interviewer requested the respondent to wait a moment. The interviewer than printed the names of the three destinations the respondent had mentioned in the unaided recall in the appropriate locations of the questionnaire. The respondents were then shown the 16 personalized alternative holidays and asked to rank-order them from most to least preferred. To increase realism, a modification to the complete ranking task was used in the way suggested by Miihlbacher and Woodside (1987): The respondents were instructed to “Strike out all the choices that you would not consider.” Pretests had shown that several respondents, after having ranked some of the presented alternatives from most to least preferred, reported that the remaining alternatives were all equally unattractive or rank-ordering the remaining alternatives seemed unrealistic because each had one or more feature descriptions that would eliminate the alternative from consideration. The additional instruction could be used without difficulty because conjoint analysis permits the handling of ties in rankings. Data on age and income were collected by asking the respondents to refer to two cards listing five income levels and seven age levels. The respondents were asked to mention a letter code that best described their annual household income level and age level. Information on monthly income was collected among Germanspeaking respondents based on the belief that those respondents would be more likely to relate to monthly and not annual income levels. Finally, all respondents who had not mentioned Tirol as one of the summer holiday trip destinations that comes to their mind in unaided recall were asked why they did not choose Tirol as a destination for summer holidays.
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Analysis An individual level analysis was used to estimate each subject’s preferences towards the different feature level categories. Analysis of variance was used to test the hypotheses. The first step in utility score calculations for one respondent is to sum the ranks for each feature level category. The lower the rank sum, the higher the preference. The raw rank sums for the feature level categories are then resealed as estimated utilities. The level category obtaining the best score is resealed to be 1.00. The worst level category is restored to 0.00. All other scores are repositioned proportionally within that range. In the second step, the relative importance score for each feature is determined. The relative importance scores are the relative weight a respondent places on each feature. This weight is derived from the range of raw rank sums between the level categories of each feature. The greater the range, the higher the importance of the feature for the respondent. Each feature range is transformed to a percentage of the sum of all ranges. Therefore the relative importances of features sum up to 100%. Other algorithms for conjoint analysis are available (see Green and Srinivasan, 1978; Solgaard, 1987). The approach described here is one of the simplest, and, unlike other methods, it provides utilities ranging from 0 to 1.00 for feature level categories and importances between 0 and 100% for each feature; thus, the conjoint analysis detailed here is intuitively appealing for marketing strategists. Results
Hypotheses 1 to 3 Were Supported by the Results of the Study The order of the three 1987 summer holiday destinations were mentioned in unaided recall was associated positively with the responses to the constant-sum question, thereby reinstating the results of Bronner and de Hoog (1985) as well as of Muhlbather and Woodside (1987). The first-mentioned destination received an average of 53% of the 20 days among the 460 respondents; the second mentioned destination received an average time of 27%; the third mentioned destination received an average time of 20%. The ANOVA results were statistically significant (p
(p
Trade-Off
Analysis
For Holiday
Table 1. Average
J BUSN RES 1988:17:117-131
Packages
Relative
Utilities
of Cost-per-Day
for Different
125
Household
Income Groups Cost per Day Cases”
$50
$100
$150
Under $30,000 $30,000-$70,000
208 201
.75 .64
.43 .50
.20 .22
Above $70.000 F-Value
41 450
.S8 7.53 .OOO
.43 2.67 .07
1.15
Household Income
P< “10 respondents
refused to indicate
.27 .32
their income.
day of the holiday, whereas for respondents with annual household incomes under $30,000 (U.S.), the average utilities declined significantly with increases in total cost per day. In the present study, the average utilities declined for all household income groups with increasing total cost per day. As Table 1 shows, on the one hand, the relative utility of low cost per day declined significantly with increasing annual household income; on the other hand, the decline of average relative utilities with increasing cost per day was significantly smaller for higher income groups in comparison to the group with the lowest annual household income levels (see Figure 2). Most interestingly, however, the group of respondents reporting annual household incomes between $30,000 and $70,000 (U.S.) showed the lowest relative importance of that feature for their holiday trip decision-making process (ANOVA pCO.01). The relative importance of the five summer holiday package features tested did significantly differ among the respondents (ANOVA p
Between$30.000.and$ 70.000.-
Above$ 70.000.-
75 .64 .58 SO 43
43
I
20
\
i $50
$100
$150
Figure 2. Relative
utilities
$50
$100
$
of cost per day in different
15ii annual
27
\
22
\
L household
income
groups.
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H. Muhlbacher and G. Botschen
two studies. Whereas for winter holiday trips the destination seems to play a major role in the decision process, the summer holiday choice is strongly based on cost per day (31%). The destination even has less relative importance (14%) than the three other features-type of vacation (19%), number of nights (18%), and accommodations (18%)-tested. The destinations first mentioned in unaided recall differed significantly by country of origin of the respondents (Table 2): U.S. citizens significantly first mentioned the United States as the country for a summer holiday trip more often (22%) than other destinations. The reason for that fact, which was also detected for winter holiday trips of U.S. citizens in the study of Miihlbacher and Woodside (1987), may be that most Americans spending their holidays in Tirol are only one-time visitors. Germans mentioned Italy most often in as first place (18%) they would vacation, followed by Tirol (14%) and the rest of Austria (ll%), as well as Greece (11%). Of the 68 respondents from Great Britain, Ireland, and the Scandinavian countries, 65% mentioned Mediterranean countries as their first 1987 summer holiday destination. Only 6% mentioned Tirol first. The winter holiday guests from France and Belgium (49) together with the tourists from other Mediterranean countries (37) were the only ones to name Tirol in the first place as a summer holiday trip destination for 1987. These results indicate a strong tendency of people from northern Europe to go south for summer vacation, whereas people living in the southern parts of Europe express an interest in spending their summer holidays in the Alps. Cluster analysis of the relative importances the individual respondents put on the features tested revealed the four clusters shown in Table 3. The clusters did not significantly differ in average annual household income, members’ age, sex, or home country. The preferred type of vacation was no valid discriminator either. Significant differences appeared in the cluster members’ holiday trip booking behavior (p
Total Rank
25 (11) 2 (4) 1 (5) 17 (3.7) 8
3 (15) 35 (7.5) 5
2 (10)
7 (3) 0 (-)
4 (6) 2 (4)
3 (8)
Turkey
52 (11.3) 3
2 (10)
4 (8)
64 (13.9) 1
0 (-1
3 (6)
43 (18)
38 (8.3) 4
0 (6_)
20 (8) 3 (6)
7 (10) 6 (12)
8 (12) 6 (12)
11 (16) 8 (16)
2 (5)
Spain
4 (11)
Italy
First Mentioned
2 (5)
Greece
Destination
(%)
From Different Home Countries
25 (11)
by Respondents
56 (12.2) 2
5 (10)
32 (14)
3 (4) 2 (4)
5 (14)
Mediterranean Britain, Ireland, Scandinavia France, Belgium Germany, Switzerland U.S.A. Rest of the world
Rest of Austria
4 (6) 8 (16)
Tirol
First Mentioned
Origin
Table 2. Destinations
30 (6.5) 7
0 (-_)
10 (4) 2 (4)
12 (18) 2 (4)
4 (11)
France
10 (50) 135 (29.4)
2 (10) 33 (7.2) 6
67 (28) 20 (40)
13 (19) 11 (22)
14 (38)
Others
7 (3) 11 (22)
6 (9) 4 (8)
3 (8)
North America
460 (100)
20
236 50
68 49
37
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H. Muhlbacher
Table 3. Clusters of Respondents
with Different Relative Importances Clusters
Features
of Features
(No. of Cases)
Cl (68)
c2 (17)
C3 (67)
c4 (308)
31 16 29 14 10
10 11 12 55 12
10 61 10 10 9
17 29 19 19 16
Number of Days Cost per day Type of vacation Accommodation Destination ANOVA,
and G. Botschen
between groups p < 0.00.
small-but-decided group of people that chooses the same location at the same place for summer vacation every year. A total of 308 respondents (66% of the sample) formed cluster 4. As for the respondents in cluster 3, for them, too, cost per day was the most important feature in summer holiday trip choice. But additionally, all the other features were also strongly considered in the decision process. It seems that this group of consumers, nearly evenly split between early (41%) and late (59%) bookers and without a special profile concerning the people accompanying them on their holiday trip, evaluated the offered alternative trips using all the features with approximately the same weight. Cost per day may have served as a kind of decisive criterion for the final choice. Cluster analysis based on individual relative utilities of feature level categories produced the results exhibited in Table 4. Clusters differed significantly by age (~~0.02) and income of respondents (p
J BUSN RES 1988:17:117-131
Trade-Off Analysis For Holiday Packages Table 4. Results
of Cluster Analysis Based on Individual Relative Utilities of Feature
Level-Categories
Variable
Cluster 1 (n = 24)
Cluster 2 (n = 303)
Cluster 3 (n =77)
Cluster 4 (n = 56)
Mean of Total Sample (n = 460)
.7233 .3460 5654
.5373 .4601 .3844
.4650 .4223 .4013
.4961 .3916 .3262
.5299 .4395 .3896
.8998 .8467 .2519
.6211 .6102 .4668
.5382 .6250 .5156
.7777 .4088 .2094
.6408
.2575 .6664 .7108
.3597 .5994 .4227
.5008 .3112 .4766
.5687 .2546 .3904
.4035 .5127 .4428
.7403 .6731 .2213
.7979 .4203
.3072 .7596 .2218
.5551 .1890 .4698
.6832 .4621 .2137
.3596 .3098 .5354
.4082 .2513 .5588
.4314 .4727 .3840
Destination First mentioned Second mentioned Third mentioned Accommodations Bed and breakfast Apartment Hotel Number of days 1 Week 2 Weeks 3 Weeks Cost per Day $50 $100 $150 Type of Vacation Rest and relaxation Active experience Education and culture ANOVA,
.1696 .4997 .6753
.1637 .4747 .5529
.6005 .4324
between groups p < 0.00
The fourth cluster detected is rather small (n = 24). It consists of respondents mainly 3.5S-years old with an annual household income between $30,000 and $70,000, preferring longer summer vacations alone or with their family at low to middle cost per day for the purpose of education and culture. Bed and breakfast places and apartments have nearly the same utility for this group, but dominate the choice of hotel by far. The TOMA destination is strongly preferred. It is interesting to note that when they were asked directly, “would you say a holiday/vacation at this destination is mainly for rest and relaxation, for active experience, or for education and culture?” more than half of all respondents answered “for rest and relaxation.” Conjoint analysis, however, revealed that there is not one cluster where this level/category of type of vacation has earned the highest relative utility. Most respondents seek active experience during their summer holidays (cluster 2). All three other clusters are special cases of people going for education and culture: one group seeking a short trip at average cost per day in the accommodation available, the other group preferring long vacations at low cost in bed and breakfast places, and the third group with a strong preference for bed and breakfast places during short and cheap trips. Comparison of results of the two cluster analyses based on feature importances and on relative utilities of level categories, respectively, leads to the conclusion that there exists strong congruence between clusters 4 (relative importance) and 2 (relative utilities) as well as clusters 1 (relative importance) and 3 (relative utilities). For the other four clusters, the similarity is less evident.
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Limitations The findings reported here are exploratory. They are based on a convenience sample. The features of holiday travel packages tested were chosen by the researchers on an ad hoc basis. The findings, therefore, should be viewed only to be illustrative as to how consumer holiday trip awareness, preference, feature importances, and utilities of feature level categories can be measured and related to a general model of holiday trip awareness and choice. A qualitative study to clarify the question of what features are relevant for the consumers in what holiday trip decision situation and a large-scale, representative sample of visitors to Tirol is needed before any conclusions on TOMA shares of Tirol and its competitors can be reached, and how the holiday travel packages might be designed to increase their perceived utility among different target markets.
Recommendations Tracking unaided awareness among competing holiday destinations for different potential target markets is recommended based on the results of the present study and similar findings reported elsewhere, e.g., Bronner and de Hoog (1985) and Miihlbacher/Woodside (1987). Such tracking research provides useful information on what specific destinations are actually competing on the battlefield within the consumer’s mind. Research using conjoint analysis offers useful information on what might be done to strengthen preference of holiday destination X or, for competing holiday travel packages, to overcome the consumers’ preference of package Y. The use of conjoint analysis forces the marketing strategist to learn what features and feature level categories are most relevant for influencing the utilities of competing holiday destinations for different target markets. Consequently, the specific features and level categories can be incorporated in experimental designs that permit conjoint analysis to estimate the consumers’ acceptance of different designs of holiday travel packages. References Alpert,
M. I., Betak,
Measurement, Austin, Tex., Banks,
J. F., and Golden,
Working 1978.
Paper,
Graduate
L. L., Data Gathering Issues in Conjoint School of Business, University of Texas,
S., Experimentation in Marketing, McGraw
Hill, New York:
N.Y.,
1965.
Bearden, W. 0.) and Woodside, A. G., Interactions of Consumption Situations Attitudes, Journal of Applied Psychology 61 (December 1976): 764-769. Belk, R. W., An Exploratory Assessment of Situational of Marketing Research 11 (May 1974): 156-163. Bronner, F., and de Hoog, R., A Recipe Research 3 (September 1985): 109-115.
for Mixing
Effects
and Brand
in Buyer Behavior,
Decision
Ingredients,
Journal European
Cattin, Ph., and Wittink, D. R., Further Beyond Conjoint Measurement: Toward a Comparison of Methods, in Advances in Consumer Research. W. D. Perreault, ed., AMA, 1977 pp. 41-45. Chandon, J.-L., and Strazzieri, A., Une analyse de structure de marche sur la base de la mesure de l’ensemble evoque, Recherche et Applications en Marketing 1 (1986): 17-39.
Trade-Off
Analysis
For Holiday
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Packages
131
Filser, M., Quelles formules de distribution pour demain? Les reponses de la segmentation par avantages recherches, Recherche et Applications en Marketing 1 (1986): 3-16. Green, P. E., and Srinivasan, V., Conjoint Analysis in Consumer Research: Outlook, Journal of Consumer Research 5 (April 1978): 102-123.
Issues
Hughes, D. G., Attitudes Measurementfor Ill., 1971.
Glenview,
Marketing Strategies, Scott Foresman,
and
Jain, A. K., Acito, F., Malhotra, N., and Mahajan, V., A Comparison of Predictive Validity of Alternative Methods for Estimating Parameters in Preference Models, Working Paper, School of Management, State University of New York at Buffalo, N.Y., 1978. Kucher, E., and Simon, H., Conjoint-Measurement Harvard Manager, 3, 1987.
- Durchbruch
bei der Preisentscheidung,
Montgomery, D. B., Wittink, D., Glaze, Th., A Predictive Test of Individual Level Concept Evaluation and Trade-off Analysis, Working Paper, College of Business Administration, Stanford University, Cal., 1977. Morgan, R. P., Brand/Price Trade-Off-Where we stand now, in Micro and Macro Market Modelling: Research on Prices, Consumer Behaviour and Forecasting, EMACi ESOMAR, Tutzing, Germany, October, 1987, pp. 263-275. Muhlbacher, H., and Woodside, A. G., Conjoint Analysis of Consumer Preferences Toward Purchasing Competing Services in Micro and Macro Market Modelling: Research on Prices, Consumer Behaviour and Forecasting, EMAC/ESOMAR, Tutzing, Germany, October, 1987, pp. 299-319. Narayana, M., and Markin, R. J., Consumer Behavior and Product Performance: Alternative Conceptualization, Journal of Marketing 39 (October 197.5): 1-6.
An
Oppedijk v. V., Walle, M., and Beazley, D., An Investigation of Alternative Methods of Applying the Trade-off Model, Journal of The Marketing Research Society 19 (1977): 2-9. Schmidt, M., An Empirical Evaluation of Some Aggregation Techniques and Estimation Algorithms in Conjoint Analysis in Micro and Macro Marketing Modelling: Research on Prices, Consumer Behaviour and Forecasting, EMACIESOMAR, Tutzing, Germany, October, 1987, pp. 135-156. Solgaard, H. S., A Comparison of Conjoint and Logit Modelling of a Single Consumer’s Evaluation of a Choice Set, in Micro and Macro Marketing Mode&g: Research on Prices, Consumer Behaviour and Forecasting, EMAC/ESOMAR, Tutzing, Germany, October, 1987, pp. 97-111. Spiggle, S., and Sewall, M. A. A Choice Marketing, 51 (April 1987): 97-111. Wilson, E. J., A Procedure for the Analysis Advertising Research 21 (1981): 31-38.
Sets
Model
of Consumer
of Retail Decision
Selection, Making,
Journal
of
Journal of
Woodside, A. G., Measuring Customer Awareness and Share-of-Requirements Awarded to Competing Industrial Distributors, in Proceedings, Division 23. J. Saegert and J. C. Anderson, eds., American Psychology Association, Washington, D. C., 1986. Woodside, A. G., and Wilson, E. J. Effects of Consumer Awareness of Brand Advertising on Preference, Journal of Advertising Research 25 (1986): 41-48.