Evaluating Destination Advertising

Abstract

Park, Nicolau, and Fesenmaier proposed the Destination Advertising Response (DAR) model as a means to more effectively evaluate destination advertising campaigns by incorporating the key decisions or components (i.e., facets) that comprise a trip. While this model appears to be an attractive alternative to traditional destination advertising evaluation, little research has been conducted to examine its validity. The goal of this study is to evaluate the potential usefulness of the DAR framework based upon current understanding of the travel decision-making process and industry practice. Additionally, the framework is evaluated based on a series of empirical analyses that consider the impact of destination advertising on the destination decision as well as on several trip-related decisions. The implications of this model for destination advertising are substantial in that it provides a much richer foundation for the development of destination marketing strategies.

Keywords

conversion studies destination advertising travel decision making facets-based advertising model advertising response

Introduction

Conversion studies have long been used by destination marketing organizations (DMOs) to evaluate the effectiveness of advertising campaigns and other marketing efforts such as destination websites (Woodside 2010). Traditional conversion studies yield a gross conversion ratio which is the percentage of travelers who visit a destination after requesting travel information. The conversion ratio is often used to gauge the efficiency and effectiveness of advertising campaigns, the economic impact of travelers to the destination, and the advertising campaign’s return on investment (Pratt et al. 2010). One of the major criticisms of traditional conversion studies is that many travelers have already decided to visit a destination before requesting information, which implies that the DMO’s advertising campaigns may have little influence on most travelers’ decisions to visit the destination (Woodside 1990; Burke and Gitelson 1990; Kim, Hwang, and Fesenmaier 2005). Another important criticism of traditional conversion studies is that the tourism destination is viewed as a single product when in fact a visit to a destination reflects a number of decisions or facets (i.e., aspects of the trip that must be planned) related to accommodations, attractions, dining, events, and shopping (Fesenmaier and Jeng 2000; Jeng and Fesenmaier 2002). Indeed, consideration of a trip as an amalgamation of several expenditure decisions together comprising a whole product or service is consistent with modern economic consumer choice theory as refashioned by Becker (1971) and Lancaster (1971).

Recently, Park, Nicolau, and Fesenmaier (2013) proposed the Destination Advertising Response (DAR) model as a means to more effectively evaluate DMO advertising campaigns by incorporating the role of each trip decision process (including destination choice as well as decisions to visit accommodations, attractions, and restaurants) in traveler response. However, while intuitively appealing, the work by Park, Nicolau, and Fesenmaier (2013) provides little evidence supporting the viability of the model beyond model fit. Thus, the goal of this study is to assess its adequacy and therefore provide a more robust foundation for evaluating destination marketing campaigns.

This article begins with a review of the conversion analysis methodology currently used to measure destination advertising response. The concept of a facets-based advertisement response model is first discussed; the proposed multifaceted framework is then considered in the context of tourism decision-making models, the destination value chain framework, and through an examination of current destination advertising practices. The proposed DAR model is then evaluated based on a survey of 17,785 U.S. travelers and the results of a series of analyses are presented. Last, the article concludes with a discussion of the implications for both researchers and destination marketers.

Literature Review

Many approaches to assessing tourism advertising have been developed, including true and quasi-experimental design, econometric modeling, aggregated buyer-purchaser modeling, and conversion analysis (McWilliams and Crompton 1997; Woodside 1990, 2010). While conversion analysis, which is an analysis of individuals’ responses to advertising campaigns in terms of destination awareness, visitation, and visitor expenditures, remains the most widely used technique for evaluating tourism advertising campaigns (Woodside 2010), it has several key limitations. First, an underlying assumption of traditional conversion studies is that individuals request information from a tourism marketing organization or similar entity in order to help them make a decision about whether or not to travel to the destination (Burke and Gitelson 1990). A number of studies examining this assumption have found that only a small portion of inquirers use the information to make a specific travel (or destination) decision and that the majority of travelers have decided to visit the destination prior to being exposed to the advertising (Woodside 1990; Burke and Gitelson 1990; Kim, Hwang, and Fesenmaier 2005). Further, these studies indicate that tourism advertising may not lead to destination visits in the short run, but rather it may expose an individual to the destination and/or create a positive image of the destination that results in an eventual visit occurring after the conversion study is completed (Kim, Hwang, and Fesenmaier 2005).

Finally, conversion studies along with most other approaches to evaluating the effectiveness of tourism advertising campaigns tend to view a travel destination as a single product and, therefore, focus solely on destination choice (Woodside 1990, 2010). As such, conversion studies fail to reflect the influence destination advertising may have on other aspects of travel beyond destination choice. Importantly, this consideration of the trip as a single choice contradicts the Becker/Lancaster economic theory of consumer behavior (Becker 1971; Lancaster 1971) whereby a vacation trip may be considered a bundle of interrelated time and goods (i.e., mode of transportation, accommodations, restaurants, etc.), and where choices are made separately in regard to each aspect of the trip and summed to the total trip expenditure. Therefore, each specific trip decision potentially alters the relative cost of travel and all other facets (i.e., each facet’s cost as a percentage of the total trip expenditures) and theoretically the change in relative prices alters consumer choices. Further, Alchian and Allen’s (1964) third law of demand posits that higher relative transport costs induce consumers to opt for the more expensive of two substitute goods because the relative cost of the higher-priced good is less. For example, New Yorkers incurring a price ratio of $3/$2 for high- to low-quality Washington apples will on average choose the higher-quality apples than Washingtonians, who sans transport costs, face a lower price but higher price ratio of $2/$1. Borcherding and Silberberg (1978) juxtaposed this observation and argued that transport costs apply equally to other goods such as the traveling consumer and concluded that those who spend more to travel to a destination will also choose more expensive options once there. Bertonazzi, Maloney, and McCormick’s (1993) analysis of Clemson University football fans empirically tested this assumption and found that those traveling far distances, indeed, opted for more expensive football tickets. Conversely, Cowen and Tabarrok (1995) argue that in the case of bundled goods such as the several goods that comprise a vacation, the third law of demand does not hold because the travel expenditure does not amount to a per-unit fixed cost. Indeed, Razzolini, Shughart, and Tollison (2003) argue that market structure determines if the third law of demand holds; further, they determined that the theory is most apt to stand up to empirical analysis in the case of highly competitive and constant cost markets.

The literature specific to tourism indicates that distance traveled and sometimes specific travel costs such as airfare are significant factors included in models estimating tourism expenditure. There are to our knowledge no studies that focus on this aspect of demand, but distance or travel costs have been included as an explanatory variable in econometric models estimating demand. Brida and Scuderi (2013) reviewed this literature and found that forty studies have been conducted that include an assessment of consumer spending relative to distance traveled. Of these studies, the majority (25) find a positive and significant effect of distance traveled on expenditures, supporting the third law of demand. From these results, it can be argued that if distance traveled and other consumer and trip characteristics influence on-destination spending choices, facets-based advertising is presumably more effective. Finally, this finding is supported in a recent study by Grigolon, Kemperman, and Timmermans (2013), who found that different travel facets are planned at different points in time and there are significant differences in trip planning depending on life cycle, levels of income, and travel experience.

Park, Nicolau, and Fesenmaier (2013) posited that a facets-based advertising response model is needed in order to explicitly model the degree to which each facet (i.e., trip decision) related to the destination (especially choice of overnight accommodations, attractions, restaurants, and events) can be influenced separately through advertisements and that visitor expenditures associated with these decisions may significantly contribute to expenditures within the destination. In particular, they propose that destination advertising response can be described as a four-stage hierarchical process (see Figure 1). The first two stages of the DAR model are drawn from traditional advertising response models whereby the potential visitor is first exposed to destination advertising, which results in the formation of an attitude toward the destination advertising (MacInnis and Jaworski 1989; Mehta 1994) and then in the second stage this attitude influences the individual’s attitude toward the destination. The proposed DAR model, however, differs from traditional advertising response models in the third stage of destination advertising response where the individual considers individual trip components. It is posited that these trip decisions typically follow a strong hierarchical structure whereby travel decisions of higher priority such as destination, budget, and accommodations are made in the earlier stages of travel, and past decisions influence future choices (Choi et al. 2011). This assumption is consistent with the research by Bertonazzi, Maloney, and McCormick (1993), and more recently Hwang and Fesenmaier (2011) and Nicolau and Más (2005, 2006), which indicates that the traveler’s utility is derived from aspects related to each of the decisions that comprise the trip.

Figure 1.

The destination advertising response (DAR) model.

In the final stage of the destination advertising response model, each travel-related facet decision is evaluated in terms of its contribution to overall value (e.g., defined in this study as total trip expenditures). Importantly, the model also considers the role of traveler characteristics such as travel party size and previous experience at the destination, and trip characteristics including trip purpose and length in moderating the destination advertising response process (Moutinho 1987). Finally, the information ecosystem (i.e., the various channels in which individuals seek and consume destination-related information) is also considered as this too has been shown to moderate the relationship between advertising and trip decisions (Grønflaten 2009).

Park, Nicolau, and Fesenmaier (2013) also argue that the focus on the destination decision as the basis for measuring tourism advertisement response is not consistent with theory describing how consumers plan their travel. In particular, studies show that travel planning is often a highly complex process that requires a number of decisions in addition to the destination choice including travel party, accommodations, length of trip, attractions, and activities. Fesenmaier and Jeng (2000) proposed a hierarchical multifaceted framework describing the travel planning process, which since has been empirically supported through numerous studies. Additionally, Dellaert, Ettema, and Lindh (1998) found that the different travel decisions are interrelated, and that the timing of these choices are distinct and influenced differently by a number of constraints, including budget and timing. More recently, Choi et al. (2011) identified several other key facets within the travel decision process including departure date, travel budget, length to trip, travel mode, accommodation, attractions, and activities and confirm that travel decision-making behavior is a multifaceted and hierarchical process and that information sources and decision-making heuristics vary across facets. These findings were also confirmed by Grigolon, Kemperman, and Timmermans (2013) where they found that the travel planning process is quite complex in that “at the stage of booking, preferred options may no longer be available, triggering a process of adaptation and perhaps another cycle in the planning process.” As such, Grigolon, Kemperman, and Timmermans (2013) conclude that each decision is influenced differentially by tourism destination promotion. Therefore, based on this understanding of the travel decision-making process, it is argued that the models used for destination advertising evaluation should reflect the influence it has on each of the facets that comprise the visit.

The argument for a facets-based advertisement response model can also be made through the examination of the destination value chain framework. That is, it is well understood within the tourism industry that the overall tourist experience can be described by a series of “touch points” including transportation, accommodation, and activities, and that each of these elements should be evaluated separately (Yilmaz and Bititci 2006; UNWTO 2007). It is also recognized that the evaluation of the performance of each part of the tourism value chain is needed in order to maximize traveler value to the destination and, in turn, to develop marketing (and development) strategies that can make the destination more competitive (Ritchie and Crouch 2003; Ritchie and Ritchie 2002; Wöber 2002). Thus, it is posited that the value chain approach to benchmarking and evaluating destination performance provides further support for a facets-based approach to measuring destination advertisement response.

Finally, the complexity of the travel decision process is implicitly recognized by DMOs when one considers that the promotional materials and web sites of most DMOs include information on a wide range of activities related to the destination (Gretzel, Yuan, and Fesenmaier 2000) and that these materials explicitly support the information needs of travelers. As illustrated in Figure 2, major U.S. destinations such as New York, Orlando, Chicago, and Los Angeles provide content on their official web sites intended to influence the decision choices of facets beyond destination choice including attractions, events, shopping, restaurants, and accommodations (Park and Gretzel 2007; Han and Mills 2006; Choi, Lehto, and Morrison 2007). Unfortunately, it also appears that the approaches used by DMOs in measuring the effectiveness of their advertising do not reflect this multifaceted perspective; instead, industry guidelines focus exclusively on destination choice when measuring the effectiveness of their advertisements (Destination Marketing Association International 2011).

Figure 2.

Examples of destination management organization websites advertising multiple destination facets.

Research Design

Three research questions guide this study: (1) To what degree are various travel decisions (i.e., destination choice, attractions, restaurants, accommodations, events, and shopping) of the overall trip influenced by destination advertising? (2) To what degree does the destination advertising response for these travel decisions affect the amount of money spent during the visit to the destination? and, (3) Can consumer groups with different sensitivities to destination advertising be identified? A series of analyses were conducted in order to answer these questions. Specifically, frequency analysis was first conducted to examine the extent to which key decisions are influenced by the tourism advertising: the destination decision, attractions, restaurants, events, shopping, and accommodations. To answer the remaining questions, two analyses were conducted whereby the mean visitor expenditures of those visitors influenced or not for each of the key facets were first compared through independent sample t-tests. Then, the DAR model was evaluated using multivariate regression analysis to assess the marginal impact of facet-based promotional material on visitor expenditures. In this analysis, the overall visitor spending at the destination was the dependent variable and the decisions to attend or purchase the separate facets of the trip and traveler characteristics were included as independent variables.

Traveler responses to destination advertising were obtained using an online survey of American travelers who had requested travel-related information as part of 40 separate destination marketing programs that were conducted by 20 different state and regional tourism offices located throughout the United States between March 2010 and September 2011. The web-based travel survey was distributed to all inquirers based on the date of contact (within three months of the request for travel information) and the destination from which information was requested. The questions used in this study (see appendix) were developed based on previous studies that evaluated the effectiveness of tourism advertisements (e.g., Burke and Gitelson 1990; Kim, Hwang, and Fesenmaier 2005; McWilliams and Crompton 1997; Woodside 1990, 1996, 2010). The complete survey instrument was 39 questions in length, though not all respondents were presented with all questions based on their responses. The average time taken by survey respondents to complete the survey was 13.4 minutes.

Respondent email addresses were obtained separately for each of the 40 destination advertising campaigns and, in total, 264,317 online surveys were successfully delivered to American travelers 18 years and older. This aspect of the methodology is important in that it avoids selection bias based on destination which leads to a more precise analysis of tourist demand as it includes not only those people who travel and purchase but also those who do not (Park and Fesenmaier 2012). In order to increase the response rate, the following three-step process was followed: (1) an initial invitation was sent out along with the URL of the survey; (2) four days later, a reminder was delivered to those who had not completed the survey; and (3) the final request for participation was sent out to those who had not completed the survey one week later. A $100 Amazon.com gift card was provided to one randomly selected respondent for each destination as an incentive to participate in the study. These efforts resulted in 18,602 responses. However after controlling for missing values the final data included 17,785 usable responses, which represents a 6.7 percent response rate. It is important to note that the advantages of online surveys (e.g., low cost, fast response, and wide accessibility of the Internet) enable tourism advertising researchers to contact the population of people who requested travel information, which largely eliminates the need for complex structured sampling procedures (Hwang and Fesenmaier 2004). Importantly, it is also argued that this approach enables us to obtain a sizeable sample that ensures robustness of the parameter estimates (i.e., underlying behavioral response), which in turn enables us to evaluate the relative impact of the hypothesized variables on advertising response.

Construct Measurement

With few exceptions, the constructs of interest to this study were measured using single items. In general, single-measure items can be grouped into two broad categories: (1) those measuring self-reported facts such as years of education, age, and number of previous jobs and (2) those measuring psychological constructs such as job satisfaction or quality of life (Wanous and Hudy 2001). The use of the first group of single-item measures is a commonly accepted practice; however, the use of the second group of single-item measures in academic research is often considered a “fatal flaw” in that that their reliability cannot be estimated (Warren and Landis 2007). However, Sackett and Larson (1990), Rossiter (2011), and others (e.g., Wanous and Hudy 2001; Warren and Landis 2007) have argued very forcefully that well-specified single-item scales should be used when possible in order to simplify the survey and to communicate better with the respondent.

The variables used in this study to understand destination advertising response correspond to the constructs identified in the proposed DAR model are presented in Table 1 and are described below. Specifically, the variables gender, age, income, travel party size, trip purpose, trip length, trip planning timing, and the number of previous visits made to the destination were measured using single items in the questionnaire and that are coded into dummy (0/1) variables reflecting the “status” of the respondent. Advertising exposure was measured across four separate channels: TV/Radio, Magazine/Newspaper, Internet, and Other. Response options included “yes,” “no,” and “not sure.” This choice of response format follows the recommendations of Dolnicar and Grün (2007) and Dolnicar, Grün, and Leisch (2011), where they show that this shortened format may be preferred in that it is reliable, quicker, perceived as less complex, and offers similar managerial implications. The responses for each channel were postcoded so that the “no” and “not sure” responses were combined and assigned a value of 0, while the “yes” response was given a value of 1.

Table 1.

Variables Used in Study.

Control variables Gender Age Income Travel party size Trip purpose Trip length Distance from destination	Number of previous destination visits Time of travel planning Exposure to destination websites Attitude toward destination advertisements	Research variables Destination ad response Attractions ad response Restaurants ad response Events ad response Shopping ad response Accommodation ad response
		Dependent variable Total trip expenditures

Visitor expenditure was measured using nominal spending categories and was then “transformed” into a discrete variable for visitor expenditure by taking the midpoint value for each spending category following Park, Nicolau, and Fesenmaier (2013). Last, distance from the destination used a series of three dichotomous (0/1) variables (e.g., lives in destination state, lives in state adjacent to destination state, and lives in state nonadjacent to destination) following Wöber and Fesenmaier (2004), who argued that these “class” variables better capture the general responses to travel distance when the specific destination is not known and when there is not a single destination.

Respondent attitude toward the advertisements was measured using seven items following Dolnicar and Grün (2007) and Dolnicar, Grün, and Leisch (2011) where response options for each aspect of the advertising was “yes,” “no,” and “not sure.” Responses were postcoded so that the “no” and “not sure” categories were combined and given a value of 0, while the “yes” response was given a value of 1. The seven advertisement attitude items were then summed resulting in a total advertisement attitude score ranging from 0 to 7, with 0 representing the lowest opinion toward the advertising and 7 representing the highest opinion. The reliability of the advertisement attitude scale was calculated using the seven items, and the resulting Cronbach’s coefficient alpha = 0.946 indicates a sufficient degree of reliability (Straub, Boudreau, and Gefen 2004). Basic descriptive statistics for the seven items for advertisement attitude are found in Table 2 and show that the various destination advertising materials were considered moderately attractive and interesting but were not rated highly in terms of trust. Also, the table shows that there was substantial variability in attitudes toward the advertising as reflected by the relatively high standard deviations. Last, the relatively high correlations among the seven items are consistent with the single dimensionality of the overall attitude scale.

Table 2.

Summary Statistics for the Attitude Toward Advertisements Scale.

Item	M	SD	1	2	3	4	5	6
1. Attractive	.66	.475
2. Features	.65	.476	.897
3. Trust	.47	.499	.632	.631
4. Help	.60	.491	.795	.794	.600
5. Place	.62	.486	.812	.825	.591	.785
6. Knowledge	.56	.496	.734	.746	.572	.710	.770
7. Plan	.52	.499	.694	.697	.593	.677	.718	.733
Cronbach’s alpha = 0.946

Note: All correlations are significant at p <.001. Attractive = the travel advertisements about the destination were attractive; features = showing interesting and unique features of the destination; trust = accurate and trustworthy; help = helping you think about what it might be like to visit the destination; place = helping you think about different places to visit in the destination; knowledge = improving your knowledge about the destination; and plan = helpful in planning your trip to/through the destination.

Advertising response was measured using two different types of questions depending on the trip facet. For destination choice, the respondent was asked how likely they were to visit the destination even if they had not seen the advertising; a dichotomous (0/1) variable was created whereby those responding “definitely yes” or “probably yes” were given values of 1, and those responding “maybe no,” “definitely no,” or “not sure” were given values of 0. However, for the other trip decisions (i.e., decisions related to attractions, restaurants, events, shopping, and accommodations), respondents were asked: “Did any of the following events happen as the result of seeing or hearing travel advertising, visiting a website about the destination, calling a 1-800 telephone number, or receiving travel information about the destination?” For these responses, “yes” was coded a value of 1 and those responding “no” or “not sure” were coded values of 0. It is important to note that a possible limitation of this study relates to the degree to which individuals can accurately perceive or recall the influence that destination advertising on each travel-related facet (Nisbett and Wilson 1977). However, again following Dolnicar and Grün (2007), Dolnicar, Grün, and Leisch (2011), and Rossiter (2011), it is argued that the items used to measure advertisement response were sufficiently robust in that respondents simply had to indicate whether they were influenced by the materials they saw or read. Nevertheless, it is important to recognize that threats to the validity of this study include an individual’s capacity to recognize and recall tourism advertising, and if the advertising somehow influenced their travel behavior.

Response Bias Adjustment

Recent tourism literature indicates that nonresponse bias has become an important issue when using an online survey methodology because of the relatively low response rates that are typically achieved (Dolnicar, Laesser, and Matus 2009; Pan 2010). Nonresponse bias can be especially problematic for advertising conversion studies when the goal is to obtain an accurate estimation of visitors’ conversion ratios and expenditures. Therefore, a response bias analysis was conducted because of the relatively low response rate that was achieved in this study. This study adopts a weighting adjustment technique using inverse propensity scores to identify nonresponse error following Rosenbaum and Rubin (1984) and Park and Fesenmaier (2012) whereby the sample data collected were reweighted based on known characteristics of the study population, including PRIZM segment, market area, state of residence, and advertising campaign.

After inverse propensity scores were applied, the original sample and the weighted sample were compared to determine response bias using Chi-square analysis; the results of this analysis indicates that basic demographic characteristics of gender (chi-square = .001, df = 1, p = .975), age (chi-square = .028, df = 5, p = .999), and income (chi-square = .098, df = 8, p = .999) are statistically stable across the original sample and the weighted sample. Chi-square analysis was also conducted to compare the conversion ratios for each trip decision facet between the unweighted and weighted samples and, again, no statistically significant differences were found (chi-square = .020, df = 5, p = .999). However, a statistically significant (t = 5.818, df = 134,984, p = .000) difference of $42.85 was found between the unweighted sample ($598.74) and the weighted sample ($641.59) for mean total destination expenditure. This finding indicates that use of the unweighted sample results in an underestimation of approximately 6.7 percent and is consistent with the findings of Park and Fesenmaier (2012). Therefore, the results discussed in this paper are based on a weighted sample that corrects for response bias.

Results

Sample Characteristics

The characteristics of the respondents are summarized in Table 3. As can be seen, the majority (62%) of the respondents are female and 81 percent are 45 years old or older. Approximately two-thirds (69%) of the sample have an annual household income of at least $50,000, and a little more than one-third (37%) have an annual household income of $80,000 or more. About 10 percent of the sample traveled alone, and the most popular reasons for travel were vacations, weekend getaways, and visiting friends and relatives. Importantly, a cursory comparison to the characteristics of U.S. travelers (U.S. Travel Association 2010, 2012) indicates that they are very similar; the one exception being gender whereby the respondent sample is slightly skewed toward females. Additionally, only about 20 percent of travelers live in the same state as the destination. Repeat visits to the destinations were common, with only 35 percent of respondents having no previous experience with the destination. The most common trip length was three to five nights (31%) and the most common time to start planning a trip was between one and four weeks before traveling (33%). The Internet was by far the most common channel in which travelers were exposed to destination advertising (73%), with newspaper and magazine ads a close second (65%), followed by TV/radio ads and other ads (such as outdoor advertisements). Finally, the mean value of respondent attitudes toward the destination advertising was 3.91 (SD = 3.01), which indicates that the destination advertisements were viewed somewhat favorably.

Table 3.

Sample Characteristics.

	Percent of Sample		Percent of Sample
Gender		Distance from destination
Male	38.4	Lives within state	20.9
Female	61.6	Lives in next state	26.5
Age (years)		Previous visits to destination
18–24	1.1	Lives far from state	52.6
25–34	6.4	None	35.4
35–44	11.8	Once	18.1
45–54	27.1	2–5 Times	25.3
55–64	33.1	6–10 Times	9.2
≥65	20.5	≥11 times	12.1
Household income ($)		Trip length
<10k	1.6	Day trip	11.8
10k–19k	2.7	1 night	11.1
20k–29k	6.4	2 nights	25.0
30k–39k	9.6	3–5 nights	31.4
40k–49k	10.3	6–10 nights	14.3
50k–59k	11.7	≥11 nights	6.4
60k–69k	10.5	Time of planning	4.3
70k–79k		Never planned trip
≥80k	10.5	Day of trip	2.9
Travel party size (no. of persons)	36.6	1–6 days before trip	15.7
1	10.1	1–4 weeks before trip	32.5
2	49.6	5–8 weeks before trip	22.4
3–5	33.5	>2 months before trip	22.3
≥6	6.9	Ad channel exposure
Trip type		TV/radio ads	39.7
Vacation	37.1	Magazine/newspaper ads	64.7
Weekend getaway	31.9	Internet ads	72.4
Special/sporting event	8.3	Other ads	19.3
Visit friend or relative	37.1
Business	6.1
Other	7.0

Influence of Destination Advertising on Destination Choice

Analysis of the total responses indicate that 48 percent visited their targeted destination at least once during the time between initial inquiry and the follow-up survey; this value (48%), then, represents the aggregate gross conversion rate for all 40 destination marketing programs considered in this study. An alternative measure of advertising response is the net conversion ratio, which is the ratio of destination visits made after travelers are exposed to destination marketing. Consistent with tourism industry best practices (Destination Marketing Association International 2011), the overall net conversion rate for the 40 destination marketing programs was 12.9 percent, representing the ratio of respondents indicating that they made their decision to visit the destination after seeing or hearing a travel advertisement, visiting a website, calling a 1-800 telephone number, or receiving travel information about the destination.

Last, the level of influence that destination advertising had on the decision to visit the destination was measured by asking: “Would you have visited the destination even if you did not obtain the travel information?” with response options including “Definitely Yes,” “Probably Yes,” “Maybe No,” “Definitely No,” and “Not Sure.” Those responding “Maybe No” or “Definitely No” were judged to have been influenced by destination advertising to visit the destination. Using this method, the ratio of those actually influenced by destination advertising to visit the destination may be considered the “true” conversion rate for destination advertisement programs, and for this study, the overall “true” conversion rate for the 40 destination marketing programs was 7.2 percent, and suggests that destination advertising has little impact on the destination decision for most travelers.

Destination Advertisement Influence on Other Trip Decisions

The third step in the analysis examined the impact of destination advertising on the various decisions (i.e., facets) that comprise the trip planning process. In particular, this study compared the conversion rates for five different trip decisions (e.g., attractions, restaurants, events, shopping, and accommodations) to the “true” destination choice conversion rate; these travel facets have been demonstrated to be significant components of overall trip expenditures (Park, Nicolau, and Fesenmaier 2013). A conversion rate for each aspect of the trip was calculated as the ratio of those that were exposed to destination advertising and those that were influenced by the advertisements to visit specifically featured destination facets (i.e., attractions, restaurants, hotels, events, and shopping). Table 4 summarizes the results of this analysis and indicates that attractions and restaurants are the two facets that are most influenced by destination advertising while destination choice is the facet least influenced by destination advertising. These results clearly indicate that destination advertising influences each trip facet to varying degrees and, therefore, the DAR model seems to be an appropriate framework for measuring advertising response.

Table 4.

Ads’ Influence on Visitors’ Individual Trip Decision Facets.

Trip Decision	Conversion Rates (%)
Destination choice	14.7
Visiting a featured attraction	63.3
Visiting a featured restaurant	48.8
Visiting a featured event	34.9
Visiting a featured store or shop	38.9
Staying at featured accommodations	38.6

Visitor Spending and the Influence of Destination Advertisements on Facet Choices

Independent sample t-tests were then conducted to identify any differences in total destination spending between the subgroups of those visitors influenced for each trip facet (see Table 5). This analysis was based on the 8,382 travelers that were exposed to destination advertisements and visited the destination; the respondents who did not visit the destination or were not exposed to destination advertising were excluded from the study. The largest difference is found in accommodations, with those influenced by advertising to visit featured accommodations spending, on average, $238 more at the destination than those that are not influenced by advertisements (t = 52.374, df = 98,077, p = .000). The difference in spending is also notable for attractions ($223) and restaurants ($193). Importantly, no statistically significant difference was found between the total travel party expenditures of those that were influenced to visit a particular destination and those that were not (t = 0.118, df = 126,235 p = .906). These findings suggest that advertising is not necessarily relevant to spending choices when determining where to vacation, but does influence spending on specific aspects of the trip.

Table 5.

Total Trip Expenditure Based on Facet-Based Advertisement Response.

Trip Facet	Ad Influenced Facet Decision	No Influence on Facet Decision	Mean Difference	t	df	Significance
Destination	$640	$641	$ 1	0.118	126,235	.906
Accommodations	$817	$579	$238	52.374	98,077	.000
Attractions	$753	$530	$223	48.396	98,077	.000
Events	$778	$614	$166	35.327	98,077	.000
Restaurants	$770	$577	$193	43.382	98,077	.000
Shopping	$766	$611	$155	33.851	98,077	.000

Regression results

The results presented above, however, are not sufficient in demonstrating differential response since there may be underlying factors such as traveler and trip characteristics that confound spending. Therefore, further analysis is needed to assess the relationship between advertisement response and visitor spending where potential moderating factors are considered. This final stage of the study used multivariate regression analysis to assess the marginal impact of facet-level advertisement response on overall visitor expenditures where the dependent variable is the log total visitor expenditures and where the moderating variables shown in Table 1 are included in the model to explain visitor spending. The resulting multiple regression model is presented in Table 6 and explains 37.2% of the variance in total visitor expenditure (R² = .372, adjusted R² = .371, df = 49, f = 65.240, p = .000). To determine the amount of variance that the destination facets’ advertisement response explains in total visitor expenditure, a regression model that omits the advertisement response group of variables was also estimated. The model without the advertising response variables explains 33.2% of the variance in visitor expenditure (R² = .332, adjusted R² = .331, df = 43, f = 62.438, p = .000). Thus, the difference in R² values between the final model and the model that excludes advertisement response indicates that taken together advertising response related to the six core destination facets improves the model fit by a modest 4.0% of the variation in total visitor expenditure. This finding is illustrated in Table 6, where the regression coefficients are statistically significant for advertising response to visiting featured attractions (b = 0.215, p = .000), accommodations (b = 0.192, p = .000), restaurants (b = 0.153, p = .000), events (b = 0.099, p = .000), and shopping (b = 0.056, p = .000) but not significant for destination choice (b = 0.003, p = .934). The results also indicate that source of destination advertising (as defined by channel) also has significant impact on total trip expenditures. As can be seen, exposure to destination advertisements via the Internet (e.g., destination Web sites) has a moderate and significant impact (b = 0.065,p = .020) on visitor expenditures; travel planning one to two months before (b = 0.190, p = .008) or three months or more before (b = 0.223, p = .001) departure also results in significantly greater spending. Attitudes toward advertisements are also important to destination spending and suggest that having a positive attitude toward destination advertisements (b = 0.014, p = .055) can positively influence destination expenditures.

Table 6.

Results of Multiple Regression Analysis.

Category	Variable	b	SE	p Value	Marginal Change in Total Visitor Expenditure ($)	Category	Variable	b	SE	p Value	Marginal Change in Total Visitor Expenditure
Constant		3.664	0.197	.000***	39.03	Age (years)	25–34	−0.001	0.139	.992	1.00
Trip purpose	Vacation	0.115	0.030	.000***	1.12		35–44	0.109	0.136	.423	1.11
	Weekend getaway	0.194	0.031	.000**	1.21		45–54	0.032	0.133	.808	1.03
	Special/sporting event	0.085	0.045	.056*	1.09		55–64	0.033	0.133	.804	1.03
	Visit family/friends	−0.178	0.028	.000***	0.84		≥65	0.033	0.134	.808	1.03
	Business	0.217	0.050	.000***	1.24	Gender	Female = 1	−0.076	0.025	.003**	0.93
Trip length	1 night	0.458	0.052	.000***	1.58	Previous visits to destination	Once	0.065	0.098	.507	1.07
	2 nights	0.873	0.045	.000***	2.39		2–5 times	0.060	0.096	.533	1.06
	3–5 nights	1.166	0.046	.000***	3.21		6–10 times	0.072	0.099	.468	1.07
	6–10 nights	1.247	0.055	.000***	3.48		≥11 times	0.039	0.099	.693	1.04
	≥11 nights	1.319	0.067	.000***	3.74	Ad channel exposure	TV/radio ads	−0.013	0.025	.610	0.99
Distance	Lives adjacent to state	0.074	0.030	.014**	1.08		Magazine/newspaper ads	−0.022	0.026	.400	0.98
Distance	Lives nonadjacent to state	0.384	0.037	.000***	1.47		Internet ads	0.065	0.028	.020**	1.07
Trip Planning	Plan day of trip	−.162	0.102	.111	0.85		Other ads	0.051	0.029	.078*	1.05
	Plan 1–6 days before	0.038	0.072	.597	1.04	Attitude toward ads		0.014	0.007	.055*	1.01
	Plan 1–4 weeks before	0.128	0.069	.066*	1.14	Response to advertising	Destination choice	0.003	0.034	.932	1.00
	Plan 5–8 weeks before	0.190	0.072	.008**	1.21		Attractions	0.215	0.028	.000***	1.24
	Plan 3+ months Before	0.233	0.073	.001***	1.26		Restaurants	0.153	0.028	.000***	1.17
Travel party size (no. of people)	2	0.099	0.042	.020**	1.10		Events	0.099	0.028	.000***	1.10
	3–5	0.265	0.044	.000***	1.30		Shopping	0.056	0.0281	.048**	1.06
	≥6	0.625	0.061	.000***	1.87		Accommodations	0.192	0.027	.000***	1.21
Household income ($)	10k–19k	0.148	0.132	.261	1.16
	20k–29k	0.184	0.119	.122	1.20
	30k–39k	0.220	0.114	.054*	1.25
	40k–49k	0.245	0.114	.032**	1.28
	50k–59k	0.296	0.113	.009**	1.34
	60k–69k	0.308	0.114	.007**	1.36
	70k–79k	0.445	0.114	.000***	1.56
	≥80k	0.470	0.110	.000***	1.60

Note: dependent variable = LN total trip expenditures. R² = .372, adjusted R² = .371, df = 49, F = 65.240, p = .000.

p < .1, **p < .05, ***p < .001.

The results of the regression analysis also indicate that both trip characteristics and traveler characteristics have a significant impact on total visitor expenditure. Specifically, vacations, weekend getaways, special/sporting events, and business trips all increase visitor spending, while visits to family and friends decrease visitor spending. Among these different travel motivators, business travel (b = .217, p = .000) and weekend getaways (b = .194, p = .000) have the largest marginal increases on total expenditure. As could be expected, the length of stay at a destination, size of travel party, and annual household incomes above $40,000 also significantly impact visitor expenditure. Additionally, distance to the destination has a positive and statistically significant impact on total trip expenditures. These findings are consistent with Borcherding and Silberberg (1978) extension of the third law of demand to destination travelers, as well as the empirical results of Bertonazzi, Maloney, and McCormick (1993) and the majority of studies included by Brida and Scuderi (2013). Further, in regard to the distance traveled hypothesis that greater distance traveled encourages more spending (Razzolini, Shughart, and Tollison 2003), the implication can be drawn that the travel industry is reasonably competitive and that cost functions, though not necessarily constant, are stable. It can thus be inferred that destination advertisers, especially those representing higher-quality products, may be well served to target more distant markets.

Discussion

The goal of this study was to examine the validity of the facets-based destination advertising response (DAR) model proposed by Park, Nicolau, and Fesenmaier (2013) based on a review of the current literature, examination of current practices, and the results of 40 different U.S. destination advertising campaigns, which include the responses of more than 17,000 respondents. A review of the tourism literature suggests that traditional conversion studies based solely on destination choice are inconsistent with current tourism understanding and practice, and an examination of destination web sites suggests that DMOs implicitly use a facets-based approach in their advertisements. As such, there appears to be a disconnect between the one-dimensional approach to conversion studies that are currently the industry standard and the now well-established, multidimensional paradigms used to understand travel decision making and the destination value chain. Last, the results of the quantitative analyses (especially the multiple regression analysis) indicate that most travelers decide where to visit without regard to destination advertising, but destination advertising does influence most other trip-related decisions to varying degrees and have significant marginal impact on total destination expenditures. Thus, it is argued that there is strong evidence supporting the DAR model.

From a destination marketing perspective, the results of the DAR analysis provide an opportunity to assess the relative value of focusing on each trip decision in greater detail. Table 6 presents the marginal impact of advertisement response for each facet in terms of dollars that can be determined by computing the reverse log function of the coefficients included in the final regression model (Table 6). From this analysis, it appears that the choice of attractions, on average, has the largest marginal impact at $1.24, followed by accommodations at $1.21, restaurants at $1.17, events at $1.10, and shopping at $1.06; this analysis suggests that an average increase of $5.78 in spending per trip could be realized if destination advertisements can influence accommodation, attraction, restaurant, event, and shopping purchases. These marginal increases in visitor spending may seem insignificant at first, but consider the multiplicative power of millions of destination visitors.

As an example, the state of Pennsylvania hosted approximately 54.3 million travel parties in 2010 (Longwoods International 2012), which equates to a potential $313.9 million in visitor spending that could be attributable to travelers’ responses to advertisements that influence trip decisions other than destination choice. Figure 3 presents a two-dimensional plot of the average advertisement conversion rates for each facet decision and the marginal impact of advertising response for each facet decision. From this figure, it can be seen that attractions, accommodations, and restaurants have both a higher instance of conversion and a higher marginal impact on visitor expenditures. Additionally, Figure 3 makes clear that shopping and event marketing should yield greater marketing returns as compared to a destination-centric marketing campaign. Thus, it is argued that by examining the marginal impact of advertisements at a facets level, a much more accurate and informative view of the return on investment of marketing dollars can be obtained and more effective advertisement campaigns can be developed that strategically focus on the facets most likely to increase destination revenue.

Figure 3.

Conversion versus impact of trip facets to destination advertising.

Having established the potential usefulness of the DAR model, future research should consider how each travel decision can be integrated into destination advertising campaigns in order to optimize awareness (i.e., attention, comprehension, etc.), visitor expenditure, and lifetime value. For example, future research could look into questions such as the types of advertisements and media channels most effective for influencing visits to attractions, and whether those conditions are equally effective in influencing decisions to visit accommodations, restaurants, or other destination facets. Also, as the use of mobile technology continues to gain in popularity among travelers, the DAR model should be expanded to reflect other moderating variables that may influence destination advertising response including trust, flexibility, and situational variables related to the temporal and physical distance from a trip decision.

Footnotes

Appendix

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors wish to recognize Madden Media for their financial support of the initial data collection effort.

Author Biographies

Jason L. Stienmetz is a PhD student at Temple University’s Fox School of Business studying business administration with a concentration in tourism and sport. Jason works as a research assistant at the National Laboratory for Tourism & eCommerce and his research interests include tourism metrics, big data analytics, and SMART tourism.

Joel G. Maxcy is an economist and associate professor in the School of Tourism and Hospitality Management at Temple University. He received his PhD in economics from Washington State University in 1996. Professor Maxcy conducts research and consults with the sport and hospitality industries on economic issues and projects including facility finance, feasibility, and pricing.

Daniel R. Fesenmaier is a professor and director of the National Laboratory for Tourism & eCommerce, School of Tourism and Hospitality Management, Temple University. He teaches and conducts research focusing on the role of information technology in travel decisions, advertising evaluation, and the design of tourism places.

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