All figures for the German and US market are retrieved from comScore reports for the time period of 1 month, in this case May 2014. Total airline visitors is the number of consumers that look at any airline website. If an individual visits two or more airline websites, they are only counted once. This is the number of unduplicated, unique visitors in the sample. The total number is divided into two categories, those that look at one website only (e-service), and those that look at two or more websites (searchers).
The unique visitor results for Germany and the US are shown in Tables 2, 3, 4 and 5.
It can be seen in Tables 2 and 4 that the individual airlines attract large numbers of online users. The first stage of the analysis is to measure the breadth (extent) of the primary research. That is, the online consideration set is based on direct visits to individual airline websites only, regardless of whether or not users visit a comparison website. The results for both markets are shown in Table 6.
The total airline website visitors is the number of consumers looking at any of the airline websites. Note that this is smaller than the sum of the unique visitors to each airline shown in Table 2 (6048) because some visitors go to more than one airline website. The figure of 6048 is the total number of visits made to different websites by all online users. The total airline visitors of 4240 are divided into two groups: e-service and search.
The number of airline websites visited by searchers is calculated by subtracting the e-service consumers from the total number of visits made by all online users, i.e. 6048 − 3092 = 2956 because by definition e-service customers only visit one airline brand. In Germany, the total number of websites visited by the searchers (2956) is divided by the number of searchers (1148), which equals a consideration set of 2.58.
The online consideration sets for Germany and the US are similar and fall within the range of 2.5–3.0. Hypothesis 1 is therefore accepted. This means that consumers in both markets look at just 2 or 3 airline websites on average, with very few conducting a more extensive search process. This is a striking result given that there are 18 major airlines operating in Germany and also 18 in the United States.
The comparison websites are significantly larger than the airline companies in both markets, measured by unique visitors. It is therefore important to understand the generic online search models as shown in Figs. 1 and 2 in order to gain an overview of online consumer search behaviour. The results are shown in Table 7.
The unduplicated visitors to all airlines is the total number of individuals that visited one or more of the airline websites within the time period of 1 month, in this case May 2014. The definition of unduplicated visitors to all of the comparison websites is the same. Based on the empirical results from the three unduplicated visitor reports, the distribution of searchers across the three search models is calculated, as shown in Fig. 2.
The importance of comparison websites is demonstrated by the sum of Model 2 and Model 3 users, which gives the percentage of all users that visit a comparison website, either in conjunction with primary search (Model 3), or visiting comparison websites only (Model 2). In Germany 60 % (35 and 25 %) of the total user group in this sample visit comparison websites and in the US the figure is higher at 73 % (43 and 30 %). Hypothesis 2 is therefore accepted.
These results also mean that in Germany, 40 % of users only visit airline websites, and in the US, this number is only 27 %. This means that there are two distinctive groups of online users that visit airline websites: those that don’t use comparison websites, and those that do use comparison websites. This presents an opportunity to analyse the generic search models to test Hypothesis 3 by comparing the search behaviour of these two groups in more detail.
The specific research objective is to test whether comparison websites act as a substitute for primary search with airline websites, stimulate primary search, or have no discernible effect. This is a crucial question because a plausible explanation for small online consideration sets is that consumers use OTAs or meta-search engines, which have comparison functionality, rather than conduct their own search directly with individual airline websites. On the face of it, this seems a rational search strategy. However the actual effect of comparison websites on primary search has not been tested in previous research and online panel data provides an ideal opportunity to conduct what is a natural experiment on a very large sample of online users (Meyer 1995; Chen et al. 2011; McLeod 2012).
The purpose of this hypothesis is to test the effect of the use of comparison websites on the propensity to conduct additional primary search. A sample of the largest airline pairs in Germany and the United States was taken in order to investigate the propensity to search for a further airline within this group. In order to test the interaction of searchers with airlines and comparison websites, the following OTAs with the largest number of visitors were selected for each country: Fluege.de (Germany) and Expedia.com (US), see Tables 3 and 5 for further details. The set analysis used to calculate the results is shown in Fig. 3. The empirical results for Germany are shown in Table 8 and those for the United States are shown in Table 9.
Note that Group 1 members only conduct primary search and are Model 1 type users. Group 2 conduct primary search and also visit comparison websites, and are Model 3 type users (see Figs. 1, 2). This analysis therefore applies to 65 % of the German market and 57 % of the US market. The remainder in both markets only visit comparison websites and the question of the effect of the comparison website on primary search is not applicable.
The probabilities shown for Groups 1 and 2 represent the probability for a user of the airline in column 1 also visiting the airline shown in column 2, within the sampling period of 1 month. For each airline pair in both Germany and the United States, Group 2 users are significantly more likely to conduct search in both airline websites. The third column shows the ratio of the probabilities to conduct further search for Group 2/Group 1. N.B. Similar analyses were also conducted with the OTA Opodo in Germany and the results were consistent with those shown below. The analysis was also repeated in both markets using Kayak.com, a meta-search engine, and similar results were observed.
The results in Tables 8 and 9 show a clear difference between the search behaviour of groups 1 and 2 for both markets and for every single natural experiment. It is therefore reasonable to reject the hypothesis that the use of comparison websites acts as a substitute for direct search because each experiment disconfirms this idea. Hypothesis 3 is therefore rejected. Instead the results suggest that the OTAs (Fluege and Expedia) are a catalyst for the consumer to conduct further search, which is evidenced by a substantially higher probability of visiting a further airline, which will lead overall to a more extensive search process. The evidence to support this catalyst hypothesis is very strong and based on 42 separate individual experiments that use the set theory shown in Fig. 3. The US and German airline markets are both very large and highly sophisticated, and the analysis of the largest airlines and OTAs in these markets means that the results are based on very high volumes of search activity in both markets.