Hotel Trademarks in Organic Search

Abstract

The importance of the online travel search environment is well documented. In this context, trademarks play an instrumental role in resolving customer confusion in the search environment. An important element of the online search environment is the organic search output that is typically displayed on the left side of the search results screen on major search engines. This study evaluated the organic search listing performance in a U.S.-based search context of hotel trademarks (websites) on three search engines (Google, Yahoo! and MSN [later Bing]) across four countries (United States, United Kingdom, China, and India) over two separate time periods (years 2007 and 2009) for three tiers of lodging operation (economy, midscale, and upscale). Findings indicate that there are significant differences across countries, across lodging tiers and over time periods. The study draws attention for hoteliers to focus on website optimization and trademark control in the online information space.

Keywords

organic search search engine marketing trademark protection SEO hotel websites

Background

Trademarks foster the flow of information in markets and enable sellers to create reliable manifestations for identification purposes and subsequently reduce search costs for consumers (Dogan and Lemley 2004). On the Internet, the primary manifestation of a seller’s trademark is the website, which serves as a transactional gateway for consumers to seek direct access and buy products/services. In this context, hotels have also sought to bring consumers directly to their websites so as to enhance direct distribution and subsequently leverage greater control over inventory (O’Connor 2009).

On the Internet, websites are indexed by search engines such as using software popularly known as either spiders or crawlers. These indexes are built using proprietary algorithms and routines unique to individual search engines and serve as the information base for organic search output when users enter search queries. Organic here means the listings that are typically displayed on the left of the screen and are not paid for (Ramos and Cota 2008). This is compared to paid search output that typically appears on the top and to the right of the screen output and is always paid for.

Significantly, not all hotel websites (manifestations of trademarks and used in the context of this study from here onwards throughout the paper) can be easily found on search engines when actively sought for. This can be attributed to a combination of technological factors and market characteristics as proposed in Figure 1. Technological factors address the underlying sophistication of the search technology itself. For example, one search engine enjoys greater credibility in search primarily because of is superior search algorithm (Xing and Lin 2006; Telang, Rajan, and Mukhopadhyay 2004). Market characteristics because the quality of information retrieval is largely dependent on the nature of information indexed. The web is a large collection of completely uncontrolled heterogeneous documents where there is very little control over what people can publish on it (Brin and Page 1998).

Figure 1.

Factors Leading to Hotel Website Searchability Performance at a Country Level

At the technological level, the sophistication of underlying search ranking algorithms of search engines is instrumental in finding specific hotel websites. Also known as algorithm effectiveness, it highlights a search engine’s efficacy in satisfying the information needs of searchers served largely through crawling and indexing algorithms, the database index, and subsequently search and retrieval interfaces (Xing and Lin 2006). Another measure of search engine efficacy is algorithm robustness, which reflects the ability of an algorithm to reduce noise and bias in search results and subsequently improve search rankings for relevant searches (Xing and Lin 2006). At the website owner’s end, it is also up to hotel websites to be “search engine optimized,” which in turn has an impact on search ranking. Search engine optimization (SEO) is a combination of techniques deployed on a website’s code and design, which is aimed at improving its ranking in the organic search listings of search engines (Malaga 2008). Here, the level of e-commerce adoption by hotel operators is also a significant technological factor in determining the searchability of hotel websites (Dutta and Manaktola 2009).

As for market characteristics, depending on the market in question, factors such as (1) the level of organization of the hotel industry, (2) relevance of trademark laws, and the (3) nature of supplier–intermediary relations potentially manifested through agreements between supplier (hotels) and intermediaries can also affect the performance of hotel websites in Internet search rankings. In the case of market organization, the U.S. lodging market is considered to have undergone significant consolidation and is relatively more mature when compared with markets in Europe and Asia (Matovic and McCleary 2003). Greater consolidation combined with a relatively more refined market structure of the U.S. lodging industry means that the practices and the leverage of hotel trademarks are arguably more robust. In China and India, studies have found that online travel intermediaries literally crowd out popular hotels in generic keyword searches such as midscale hotels etc. (Dutta and Manaktola 2009; Wu 2008).

The relevance of trademark laws is also important to address when viewing the extent to which firms protect and publish their online trademark manifestations (Dogan and Lemley 2004; Pink 2005). For example, in the United States and United Kingdom, courts have ruled in favor of trademark infringement when third-party trademarks are incorporated in one’s website code (tags, keywords, and descriptions) without relevant permissions (Coviello 2009). Ostensibly, countries where trademark laws are relatively relevant and reasonably up to date with the fast pace of change in the technological landscape are less likely to see abuse of trademarks on the Internet by intermediaries as opposed to those where trademark laws and governance are weak.

At the industry level, the nature of relationships as articulated through agreements between hotels and intermediaries regarding the use of trademarks on websites can also be a factor in determining the extent to which hotel trademarks perform in Internet search rankings when sought after directly. For instance, intermediaries can extract signed contracts from hotels so as to enable the integration of hotel trademarks onto the source codes of their own websites. This is largely possible in markets where the emphasis is more on indirect distribution (intermediaries) as opposed to going direct.

Therefore, when all three market related factors are combined, that is, an unorganized market, lack of relevance of trademark laws, and greater intermediary control of hotel room distribution, the performance of hotel websites in Internet search rankings will be negatively affected. How then are hotels performing with their trademarks in the organic search results of major search engines? Has the growth and maturity of search engines had an impact on the searchability of hotel websites? How does this vary at a national level as well as across the key segments of the lodging industry? Past research has addressed search engine results of hotel trademarks (O’Connor 2009), although the study was limited in sample size (90 hotels), and predominantly focused on paid search. While paid search is a fast-growing segment of the search engine business, several trade press reports indicate that organic search listings outperform paid search listings either in terms of what is clicked first (organic over paid), or for credibility purposes (iProspect 2008; Hitwise 2009).

The purpose of this study is to evaluate the organic search listing performance of hotel trademarks (as manifested through their individual websites) along two separate time periods (2007 and 2009), in a multinational context (United States, United Kingdom, China, and India), and along the three prominent tiers of industry operations (economy, midscale and upscale). Findings highlight a key issue for lodging marketers to improve/refine their search optimization practices. Secondly, individual countries can articulate policies and legislations that reduce confusion for customers seeking to buy travel products online through effective trademark legislation and enforcement. The context of change between 2007 and 2009 provides added impetus to industry marketing professionals about the extent to which hotel searchability is gaining relevance on the Internet.

Travel Information Search

Information search is integral to the travel purchase decision process (Vogt and Fesenmaier 1998) and in this context, travel websites serve as both informational and transactional gateways in the travel decision-making process (Beldona, Morrison, and O’Leary 2004; Pan and Fesenmaier 2006). More specifically, hotel websites are essential information sources that highlight features, attributes, and amenities (Law and Hsu 2006). Given the ongoing battle for control over electronic room distribution between hotels and intermediaries (Carvell and Quan 2005; Nayar and Beldona 2010), hotels have for long sought emphasis on direct distribution by enabling travelers to reserve directly on their own websites (Jeong, Oh, and Gregoire 2003; Law and Hsu 2006). The importance of booking directly on the hotel websites is underscored by the emergence of strategies such as “best rates guaranteed” etc. that are aimed at luring value-based customers. Although results are mixed in terms of whether hotels have achieved greater leverage from these programs (Gazzoli, Kim, and Palakruthi 2008; Demirciftci et al. 2010), the emphasis on direct distribution and greater inventory control is a priority for suppliers in the travel distribution network (Nayar and Beldona 2010). Therefore, the ease of searchability of a hotel website in addition to its functional relevance as an informational/transactional resource influences hotels’ revenue performance (O’Connor 2009; Kracht and Wang 2010).

The Role of Search Engines

Over the past 10 years, search engines have become essential components of the global, networked, digital infrastructure (Rangaswamy, Giles, and Seres 2009, p. 59). Search engines serve as one of the primary “gateways” for travelers to access the online tourism domain (Xiang, Wober, and Fesenmaier 2008, p. 141). Trade press reports suggest that nearly 80% to 90% of all website traffic originates on generic search sites such as search engines and directories (Law and Huang 2006). In addition to this, the ranking of the website in the search engine results is crucial for generating traffic and sales (Ramos and Cota 2008). One study goes to point that 68% of search engine users click a search result within the first page of search term’s organic results (iProspect 2008). Specific to travel, novice web users are believed to begin their search for travel information on search engines such as Google (Buhalis and Law 2008).

The use of search engines on any typical day in the United States has increased from 49% in 2002 to 69% in 2008 (Pew Internet Report 2008). In India, the use of search engine use increased from 35% to 75% of active Internet users from 2004 to 2008 (IAMAI-IMRB 2008). Specific to China, the number of Chinese search engine users had reached 235 million, which is an increase of 54.49 million (34%) from 2008 to 2009 (China Report 2009). This is understandable given the evolution and expansion of search technologies in the recent years. For example, Google provides five formats of search, namely, text, images, local business through maps, mobile, and video (Google Inc. 2010).

As for the importance of organic search results, a majority of Internet users identified organic search results as the most relevant to their query (iProspect 2008). In addition, it is also believed that users typically do not look past the first page of a search’s results (Hitwise 2009; Lewis 2009). Therefore, being on the first page of search engine listings is imperative if the search term is directly seeking one’s hotel website. The analysis of click stream data indicates that people’s attention concentrates on the top portion, especially the first place, of the first page of search results amounting to 42.13% of the total clicks (Enquiro 2006a, 2006b). This importance on organic search has spurred marketers to try to manipulate the software called spiders, which are used by search engines to crawl the web, classifying each page they encounter and adding it to its database that will subsequently list in search results (O’Connor 2009). This underlies the importance of not only being in the first page of search engine listings, but also on top of the first page when it comes to a firm’s own trademarks.

There are two ways usually chosen by marketers: first, organic search engine optimization (SEO), and second, paid placement. In SEO, the website code is manipulated with keywords and related augmentations such that it represents itself accurately. Typically, these effects become visible after a few weeks although there is no guarantee that the website will rank on the top, and more so consistently (Green 2003; Sen 2005; Evans 2007). The reason that there is no guarantee that a website will rank at the top of search listings based on one or many keywords is because search engine algorithms employ other parameters such as the number of external links etc. that establish a website’s credibility for the keyword(s) in question. Search engine algorithm technology for ranking websites based on keywords is extremely complex (Sen 2005; Evans 2007). Complexity is compounded by competing websites with the same keywords (O’Connor 2009) and to ensure consistent performance on the top of the results, the website needs to be continuously updated (McKinsey 2007) and also generate traffic relevant to the keywords that have been architected to draw customers in. For example, Google is believed to have up to 200 signals (parameters) to establish rankings of websites based on keywords in organic search (Hansell 2007).

Legal Issues of Trademarks in Organic Search

Given that search engine technology is driven by keywords, which in turn can also be trademarks, the legal implications of use are significant. In organic search, the impact of trademark misuse typically stems from a nonowner incorporating trademarks that belong to third parties onto the software code of its website. More specifically, meta tags and title tags, which are key descriptions of a webpage that spiders draw information from are filled with keywords representing third party trademarks. For example, an online travel intermediary can include trademarks of established brands in its website code so as to appear higher in the rankings, thereby compromising the ranking of the website that actually owns the trademark. In the United States, this is legally protected under the “initial interest confusion” doctrine, from which trademark dilution is a typical consequence (Pink 2005). Put differently, a consumer’s frustration with finding a specific trademark will result in confusion of the search environment because intermediaries have integrated this trademark into their website codes (Dogan and Lemley 2004). In such cases, search engines do not serve as gatekeepers of such illegal acts and almost always ask affected trademark owners to directly contact the website(s) engaging in such practices (Coviello 2008).

In the hotel information search context, initial interest confusion for consumers can take place either through transactional intermediaries or informational intermediaries such as destination marketing organizations, convention and visitor bureaus, travel blogs, and related consumer-generated media. Often, travel websites serve in both capacities (transactional and informational), for example, TripAdvisor. In many scenarios, the “initial interest confusion” is itself negated because the rich amount of information can actually enrich the consumer’s decision making. For example, a consumer looking for a particular hotel enters a search query only to see reviews of the property on review sites at the top of the search rankings. Using this information, the consumer makes a more informed choice. However, this does not diminish the importance of hotels to have greater control over organic search results given that it is their own trademark. Put differently, the greater ranking of a hotel’s own website when consumers seek information directly about it is a reflection of the trademark’s efficacy.

Hotel Websites and Direct Distribution

A hotel trademark’s efficacy also depends on the extent to which it can draw consumers to its site to book directly. Recent developments such as the Expedia vs. Choice standoff highlights potential conflicts as hotels and online travel agencies/intermediaries (OTAs) seek to wrest/retain control of hotel room direct distribution (PhocusWright Connect 2009). Hotels and intermediaries have been engaged in price wars for some time and hoteliers have gradually ceded control of their inventory availability and pricing to OTAs (Carroll and Siguaw 2003; Miller 2005; Tso and Law 2005). Carroll and Siguaw (2003) emphasize the need for hotels to buck the trend of commoditization wherein price becomes the main (or only) consideration for booking a room. Trade press estimates indicate that in the United States, hotels have conceded nearly 10% of their distribution share to online intermediaries between 2007 and 2009 (Starkov 2009). In addition, OTAs, such as Expedia, Hotwire, Priceline, and Travelocity all have slightly increased their share of search traffic, with Expedia capturing 26% of all search traffic, including both paid and organic on average (Drew 2008). The significance of these trends indicates the importance for hotel websites to appear high in search listings when actively sought for.

Market Maturity and E-commerce Trends

The state of organization of the lodging industry and its market characteristics also plays a role in determining the relative efficacy of hotel trademarks. Upscale hotels seek greater prominence in the Internet marketplace primarily because of the enhanced positioning of their offerings. While the same can be said of midscale hotels (albeit to a marginally lesser extent), economy hotels have relatively fewer resources to expend into SEO practices at large. In the case of the United States, the lodging industry is relatively more organized along franchised and independent hotel lines. Given this state of consolidation, brands are well organized along tiers (economy, midscale, and upscale). Also, hotels that are largely franchised are better represented through websites with transactional capabilities to book rooms directly from (Wu 2008).

In contrast, the industry is relatively less organized in China and India when it comes to electronic distribution through the Internet (Hsu, Zhub, and Agrusac 2004; Dabas and Manaktola 2007). In the case of China, findings indicate that domestic brand hotels do not use the Internet to its full potential as opposed to international brand hotels (Wu 2008). An earlier study comparing Mainland China hotels with Hong Kong hotels also showed that hotel websites of Mainland China performed relatively lower than Hong Kong hotels (Huang and Law 2003). In the case of India, despite the limited amount of research pertaining to the country, findings show that midscale hotels depend more on traditional distribution channels (global distribution systems, call center, etc.) as opposed to leveraging the direct interactive capabilities of the Internet (Dabas and Manaktola 2007). Another more recent study found that not a single midscale hotel in the New Delhi market appeared on the first page of the search engine’s results based on generic keywords (not trademarks) and that only wholesalers (intermediaries) occupied all this space (Dutta and Manaktola 2009). Also, India’s economy segment has been less organized (Dutta and Manaktola 2009) when compared with the United States and to some extent the United Kingdom, and the same can be said of China (Wu 2008). Arguably, therefore, there is room to contend that the maturity levels of the three tiers vary significantly across the four countries.

The following research questions served as signposts for the larger objective of this paper. First, given the rapid pace of technological change, one has to prevail that there have been significant developments in search engine technologies from 2007 and 2009. Add to this, search engine optimization as a practice has gained prominence over the years. Therefore:

To what extent did organic search listing performance of hotel websites change between 2007 and 2009 across the four countries (United States, United Kingdom, China, and India)?

What are the differences in organic search listing performance of hotel websites between the three prominent tiers of hotel operations, namely, economy, midscale, and upscale, across the United States, United Kingdom, China, and India?

What are the differences in organic search listing performance of hotel websites across the three prominent search engines, namely, Google, Yahoo! and MSN, between 2007 and 2009 across the United States, United Kingdom, China, and India?

Method

In the first step of the sampling procedure, major online travel intermediaries from each country were identified, from which 300 brands (100 from each tier) were drawn. Subsequently, a stratified random sampling procedure was adopted, wherein every second hotel brand was picked from the initial data pool, which in turn resulted in 150 hotel brands (50 each from the three tiers) from each country. The category of each hotel website was clarified by either visiting their websites or validating the same from external sources. Note that although the two samples comprised the same hotel websites in 2007 and 2009, the brands across the four countries were not the same. Each country had its own representative sample for each tier of operation as described below.

The final sample comprised 50 hotel brands from three prominent lodging tiers (3 = economy, midscale, and upscale) across four countries (United States, United Kingdom, India, and China). This was done across the two years (2007 and 2009). Therefore, the overall sample used for the study was 1,200 (50 × 3 × 4 × 2 = 1,200). The sample outline detailing the search engines used is illustrated in Table 1.

Table 1.

Final Sample of Hotel Brands Analyzed

	United States (Hotels.com Tripadvisor.com Hotwire.com Expedia.com)	United Kingdom (Expedia.co.uk Booking.com Travelocity.co.uk Hotelnet.co.uk)	India (Bookmytrip.in Indiahotelreview.com)	China (Ctrip.com Elong.com)
Economy	50	50	50	50
Midscale	50	50	50	50
Upscale	50	50	50	50
Total	150	150	150	150
	Google.com	Google.co.uk	Google.co.in	Google.cn
	Yahoo.com	Uk.yahoo.com	Yahoo.co.in	Cn.yahoo.com
	MSN.com	Uk.msn.com	In.msn.com	Cn.MSN.com

Note: Intermediaries mentioned in parentheses served as the sources within each country from where the samples of hotels were drawn.

According to research from ComScore, in 2007, Google was the world’s most popular search engine, with a global average usage share of 65.0%, followed by Yahoo! and MSN (19.6% and 8.40%, respectively) (ComScore Report 2009). These three search engines were identified to conduct searches. In 2009, the rankings of the search engines in terms of market share remained largely the same (ComScore Report 2009). One change was that MSN’s Live was changed to Bing, which can still be accessed through old web address.

Data were collected in two stages: the first collection was in October 2007; the second time was in August 2009. Hotel brands were used as keywords and put into the three search engines in four countries. Researchers observed the first page of search results only. The ranking of the hotel brands’ websites were then recorded. Table 2 outlines sample search queries used pertinent to each lodging segment across the four countries. Also, when any Online Travel Agency (OTA) appeared higher than the hotel brand website on the first page, the rankings of both the hotel brand website and the OTA appearing first were recorded. The same procedure was followed exactly in 2009. Note that data were collected from researchers based in the Northeastern United States. Search engine results typically vary based on the region from where the search was conducted. Therefore, it is imperative to note that the study prevails within the context of U.S.-based travel information seekers only.

Table 2.

Sample Keywords Entered into Search Engines

	United States	United Kingdom	India	China
Economy	AmeriHost Inn and Suites	Adria Hotel	Samrat Hotel	Dong Shi Hotel
	Endicott Inn	Chelsea Guest House	Indraprastha Hotel	Jingjiang Inn
Midscale	Holiday Inn Express	Britannia Hotel	Phoenix Park Inn Resort	Chenghong Hotel
	Courtyard by Marriott	Pembridge Palace hotel	Samudra Hotel	Jinshijie hotel
Upscale	Drake Hotel	Saint Georges Hotel	Jayamahal Palace Hotel	Golden Lustre Hotel
	Westin Hotel	Jumeirah Carlton Tower Hotel	Chancery Hotel	Huandao Boya hotel

Measures

Three categorical variables, namely, country (four levels: United States, United Kingdom, China and India), year (two levels: 2007 and 2009) and lodging tier (three levels: economy, midscale, and upscale) comprised the independent factors of analysis.

A hotel website’s rank in a search query based on a search of its trademark comprised the dependent measure of the study. There was a separate dependent measure for each of the three search engines (Google, Yahoo! and MSN). When a hotel’s website did not show up on the first page of the search results, a score of 0 was assigned. Subsequently, a hotel that appeared first on the organic search listing was given a score of 10; the second rank was given a score of 9, third rank was given a score of 8, etc. Hence, the coding system was developed wherein greater the number allocated for analysis, higher was the rank that the website earned on the organic search listing. Note that a search engine’s organic search results typically comprise 10 in each page. Going by the notion that the first page is the most important in a search engine’s results (Hitwise 2009; Lewis 2009), only the first page’s results were used in the analysis.

An alternative analysis was conducted primarily to ascertain the relative positioning of hotel websites with OTAs. Here, a dichotomous variable with codes 1 or 0 was created (1 = Hotel Website Higher in Rank Compared to 1st OTA, and 0 = OTA Ranked Higher Compared to Hotel Website). This score for each of the three search engines was averaged, and if the mean was greater than or equal to 0.50, it was coded as 1 meaning that the aggregate rank of the hotel website across the three search engines was greater than that of OTAs. Alternately, if the mean score was less than 0.50 across the three search engines, it was coded as 0 meaning that the aggregate rank of the hotel website across the three search engines was lesser than that of OTAs.

Statistical Method

Multivariate generalized linear model (GLM) was used to compare groups formed by categorical independent variables (country, year, and lodging segment) on group differences in a set of three interval-dependent variables (organic search listing on Google, Yahoo! and MSN). Multivariate GLM keeps the Type I error rate down when dealing with multiple dependent variables that are somewhat correlated (Weinfurt 1998; Garson 2008).

To ascertain the relative positioning of hotel websites in comparison with OTA, simple cross tabulations were conducted between the dichotomous variable (1 = Hotel Website Higher in Rank Compared to 1st OTA, and 0 = OTA Ranked Higher Compared to Hotel Website) and the categorical variables of country, year, and lodging tier.

Findings

Table 3 highlights the results of the multivariate GLM procedure, which includes the multivariate F values as well as the univariate F values for each of the dependent variables. The model comprised seven factors, including three main effects and four interactions.

Table 3.

Multivariate and Univariate Effects

	Multivariate
Factor and Hypothesis	df	F	Partial η²	Univariate F
Year (2007 and 2009)—Hypothesis 1	3	13.84***	.034
Google				13.32***
Yahoo				0.39
MSN				24.83***
Country (United States, United Kingdom, China, and India)—Hypothesis 2	9	67.36***	.147
Google				148.01***
Yahoo				241.38***
MSN				199.35***
Lodging tier (economy, midscale, upscale)—Hypothesis 3	6	17.88***	.044
Google				49.06***
Yahoo				38.95***
MSN				37.74***
Year × Country—Hypothesis 4	9	5.84***	.015
Google				2.29
Yahoo				6.68***
MSN				4.89**
Year × Lodging Tier—Hypothesis 5	6	3.03**	.008
Google				0.22
Yahoo				4.84**
MSN				0.71
Country × Lodging Tier—Hypothesis 6	18	6.60***	.033
Google				12.94***
Yahoo				10.63***
MSN				12.64***
Year × Country ×Lodging Tier—Hypothesis 7	18	1.05	.005
Google				0.27
Yahoo				1.05
MSN				0.70

p < .01. ***p < .001.

The first main effect comprised the year variable (2007 and 2009). Findings indicate that there are significant differences in organic search listings performance between 2007 and 2009 at large. Univariate statistics indicate significant differences across all three search engines here. The second factor that comprised the main effect (country = 4 levels) showed the strongest significant differences, which is also evident from the partial eta-squared value, which stands for practical significance. This value of .147 (nearly 15% of the variance) is the highest across all other factors. Significant differences between lodging tiers was also strongly evidenced across all three search engines.

Factor 4 addressed enhancements in search listing performances over the two years across the four countries, and here too there were significant differences. This is also visually supported in Figure 2. Note, however, that the practical significance value here is relatively low (.015). The reason for this can be attributed to significant changes in Yahoo! and MSN, but lack of significance in Google search listings. Factor 5 sought significant differences based on the lodging tier over the two years. Although this was significant at the p <.01 level, its practical significance value was below .01 at .008, and so it was not considered in the realm of valid differences. One can surmise here that all three tiers have improved in their search listing performance. Here again, the difference was not prevailing with Google based on univariate results.

Figure 2.

Organic Search Listing Performances of Hotel Brand Websites in Four Countries: 2007 and 2009

Factor 6 (lodging tier × country) indicated significant differences across all three search engines. See Figure 3 that highlights the visual plots of mean scores. As shown in Figure 3, on Google, the United States reported marginal differences based on the tier of operation. In comparison, China reported the greatest differences between lodging tiers. Upscale hotels in China reported significantly higher levels of searchability over their counterparts in the midscale and economy tiers. In United Kingdom, the differences were evenly spaced out on Google and Yahoo! but when it came to MSN, there were no differences between midscale and upscale hotels, but both these tiers were noticeably higher than economy tier. In India, upscale and midscale hotels reported considerable distance to economy while trivial difference between each other. Lastly, with Factor 7, where the search listing performance of three lodging tiers in four countries between two years was evaluated, the difference was not significant.

Figure 3.

Organic Search Listing Performances of Hotel Brand Websites across Three Lodging Tiers and Four Countries

In the second phase of the analysis, the relative positioning of hotel websites compared to OTAs was conducted. Cross tabulations and the resultant percentages are illustrated in Figures 4 and5. Figure 4 is a visual representation of percentages wherein hotels were found to be higher than OTAs in the search listings. Among all, the United States represented the best performance, with 100% of hotel brand websites listing higher than OTAs in 2007 but followed by a marginal decline by 4% in 2009, that is, 4% of OTAs listed higher than the hotel brand websites when searched with the brand name as the keyword in search engines. As for the United Kingdom, 78% of hotel websites were ahead of OTAs in 2007 when sought for by their brand names. This figure increased to 93% in 2009. In contrast, India and China reported significantly lower levels of performance when compared with the United States and United Kingdom. India however improved from 55% in 2007 to 68% in 2009 as opposed to China, where an actual decrease was observed in hotel trademark performance, from 29% in 2007 to 28% in 2009.

Figure 4.

Hotel Brand Websites Listed Higher Than OTAs in Four Countries: 2007 and 2009

Figure 5.

Hotel Brand Websites Listed Higher Than OTAs Across Three Lodging Tiers: 2007 and 2009

In Figure 5, the relative positioning of hotel websites when compared with OTAs across the three hotel tiers between two time periods is illustrated. In addition to the stable increase of the organic search engine performance versus OTAs when it comes from the economic/budget tier all the way to the full-service tier, each tier presents an observable improvement from 2007 to 2009, among which the economic/budget tier represents the highest improvement. However, the full-service tier still retains the best performance either with or without taking OTAs into account.

Discussion and Implications

First, the United States has the highest organic search listings performance of hotel brand websites compared to the United Kingdom, China, and India. Also, this did not change significantly from 2007 to 2009 across all the three search engines when compared with the other three countries. While this highlights greater market organization, greater relevance of trademark laws, as well as finer practices of search engine optimization in the United States most certainly, one should be mindful that the searches were conducted in United States itself. To some extent, it is possible that given Google’s origin in the United States and the resultant familiarity of the search engine with its parent market, resulted in greater levels of searchability compared to countries outside its origin.

In the United Kingdom, the incremental performance of hotel websites in search rankings is palpable across all three search engines studied here. India reported greater improvements on Google and MSN (later Bing), but significantly greater when it came to Yahoo! Lastly, China reported the lowest level of organic search listing performance across both years when compared to the remaining three countries, although they did show significant improvements in performances between the two years across three search engines. In China’s case, one cannot but revert to the heterogeneity of documents and how this may have (to some extent at least) affected searchability from Chinese websites. For example, the Chinese language uses a logographic system wherein concepts develop meaning from a collection of symbols and, subsequently, English-language search engines need to adapt to this script (Luk and Kwok 2002). In addition, it has been argued that structure of the Internet in terms of inbound and outbound links as well as the heterogeneity in web design across multiple provinces that are culturally unique render English-language–based search engines less effective (Yu, Li, and Manning 2006). In fact, this is the central argument over Google’s lack of market leadership in China, where a search engine (Baidu) developed specifically for the Chinese market leads in overall market share of close to 60% (Chen 2010).

Needless to say, a palpable aspect of the findings is the relatively high maturity of Google’s search engine performance when compared with Yahoo! and MSN. In academic terms, Google’s performance in algorithm effectiveness and algorithm robustness was significantly better compared to Yahoo! and MSN (later Bing). Needless to say, the search engine market is growing, with nearly several formats of search evolving such as spatial search, visual search, semantic search, etc. This will in turn affect the search for hotels and subsequently how hotels leverage direct distribution capabilities. For instance, Google is considering the implementation of hotel search results through maps with prices of hotels as and when users enter queries related to them (Parr 2010). This in turn further improves direct distribution potential for hotels and subsequently emphasizes the importance over how hotels seek this direct leverage using search engines.

When taking OTAs into the comparison across four countries or three hotel tiers, the results are moderately significant. Among four countries, the United States represents the highest level of organic search listing performance versus OTAs, with China having the lowest level. When it comes to the comparison across the three hotel tiers, the full-service tier reports better performance than the midscale tier, followed by the economy/budget tiers. Although the United States showed a marginal 4% decline in 2009 when compared with 2007, it ranked first when compared with the remaining three countries. The United Kingdom and India both reported relatively remarkable increases in hotel searchability from 2007 to 2009.

Nonetheless, China’s lodging sector reported the lowest level of performance when compared with OTAs. This can be attributed to the relatively thriving intermediary sector combined with the relatively less organized midscale and economy tiers in the country. According to the Chinese Tourism and Hotel Association (CTHA), by 2002, there were 8,880 star-rated hotels in China, of which only 1.97% were five-star hotels and 7.15% were four-star hotels (CTHA 2010). In addition, there are thousands of hotels without stars in China. For example, there were 4,452 unrated hotels in China in 2000 (Pine and Phillips 2005). Most of the unrated hotels and low-rated hotels such as one-star, two-star, and three-star rated hotels are with limited sizes, for example, fewer than 99 rooms (Pine and Phillips 2005). Most of these small hotels either do not have their own websites or are not optimized because of enhanced intermediary control over Internet-based distribution. In contrast, in addition to certain major national OTAs, almost each city in China has several OTAs that cover online hotel bookings for most of the hotels in that city. Besides, unlike the United States, the travel agencies, both physical and virtual, retain greater importance in the tourism and hospitality industry.

Marketers and policy makers in China and India need to put the findings in perspective for marketing purposes. On one hand, trademark laws need to be tightened to ensure that intermediaries do not significantly encroach into the travel information space and subsequently cause confusion for travelers seeking specific hotels. This leads to greater confusion in the minds of buyers and subsequently serves as a deterrent in converting browsers to buyers (Matzler and Waiguny 2005). Hotels need to understand that weaker searchability of websites weakens the relationship between the supplier and the customer at the prepurchase stage. If intermediaries occupy much of the virtual space of hotel brands, the loss of trademark control on the Internet can lead to faster commoditization of the product category at large.

The study’s findings are useful for hotel practitioners and academic researchers to better understand the trend of the organic search listing performance development. From the view of the industry, this article serves as a reminder to hotels to examine their websites management and as to how their trademarks are being used. This is more important given the ensuing battle for customer control taking place between hotels and online intermediaries. Websites of hotels have to be increasingly responsive to searches from customers who seek to book directly. Put differently, hotels need to make the best of enhancing direct distribution efforts, leverage loyalty programs, and subsequently increase the profit (Gazzoli, Kim, and Palakurthi 2008).

The findings of this research can also help hoteliers to identity their positions in the website SEO and trademark protection campaign. Academically, this research presents the important truth in terms of organic searching listing performance at a typical point of time after 20 years of development of the Internet in hospitality and tourism industry (Buhalis and Law 2008).

Limitations

For the most part, every study has its share of limitations. This one is not different. One limitation of the study is that the analysis comprised only the first page of the search results. However, extending the measure to more pages would have given only a marginally more detailed perspective given the importance of the first page in the search results. In addition, the study was less about the process of travel information search and more about the searchability of trademarks, which is why the first page’s results were largely decided upon.

Second, the study is a review of the results only collected from one geographical zone (the United States). Although the context of the study was travel information seekers from the United States, search results vary based on the geographic location of where the search is being conducted. More importantly, heterogeneity in scripts (Chinese scripts, for example) and web structure need to be accounted for especially when taking into consideration cross-national differences. Future studies can investigate this across multiple geographic contexts.

Lastly, while it can be argued that online travel information seekers use generic keywords more as opposed to hotel brands as keywords, this study’s scope was limited to performance of hotel trademarks when actively sought. Future research can expand into the context of generic keywords and build on the findings of this study.

Footnotes

The author(s) declared no conflicts of interests with respect to the authorship and/or publication of this article.

The author(s) received no financial support for the research and/or authorship of this article.

References

Beldona

Srikanth

Morrison

Alastair M.

O’Leary

Joseph

. (2005). “Online Shopping Motivations and Pleasure Travel Products: A Correspondence Analysis.” Tourism Management, 26 (4): 561-70.

Brin

Sergei

Page

Larry

. (1998). “The Anatomy of a Large-Scale Hypertextual Web Search Engine.” Computer Networks and ISDN Systems, 30 (1-7): 107-17.

Buhalis

Dimitros

Law

Rob

. (2008). “Progress in Information Technology and Tourism Management: 20 Years on and 10 Years after the Internet—The State of eTourism Research.” Tourism Management, 29 (4): 609-23.

Carroll

Bill

Siguaw

Judy

. (2003). “The Evolution of Electronic Distribution: Effects on Hotels and Intermediaries.” Cornell Hotel and Restaurant Administration Quarterly, 44 (4): 38-50.

Carvell

Steve A.

Quan

Daniel C.

(2005). “Low-Price Guarantees: How Hotel Companies Can Get It Right.” Cornell Center for Hospitality Research Reports, 5 (10): 1-16.

China Search Engine User Behavior Research Report. (2009). Nanjing Marketing Group. http://www.nanjingmarketinggroup.com/knowledge/china-search-engine-report-2009. Accessed [10th July, 2010).

Chen

Alice

. (2010). “Baidu Ranking Algorithm and How It Differs from Google.cn.” http://www.slideshare.net/alicewansichen/chinese-search-market-baidu-vs-google-china (accessed September 2, 2010).

“ComScore Report: July 2009 U.S. Search Engine Rankings.” (2009). http://www.comscore.com/Press_Events/Press_Releases/2009/8/comScore_Releases_July_2009_U.S._Search_Engine_Rankings. (Accessed August, 31st, 2009).

Coviello

Jane

. (2009). “Use of Trademarks in Search Engine Optimization and Web Marketing.” http://www.janecoviellolaw.com/id4.html (Accessed April 10th, 2010).

10.

Chinese Tourism and Hotel Association (CTHA). (2010). “The 2002 Statistic Report of Star-rated Hotels in China.” http://info.hotel.hc360.com/2010/01/281744169154.shtml (accessed April 29,2010).

11.

Dabas

Swati

Manaktola

Kamal

. (2007).”Managing Reservations through Online Distribution Channels.” International Journal of Contemporary Hospitality Management, 19 (5): 388-96.

12.

Demirciftci

Cobanogulu

Beldona

Cummings

(2010). Room Rate Parity Analysis Across Different Distribution Channels in the US. Journal of Hospitality Marketing and Management. 19(4), 295.

13.

Dogan

Stacey L.

Lemley

Mark A.

(2004). “Trademarks and Consumer Search Costs on the Internet.” Houston Law Review, 41: 777-838.

14.

Drew

Jack

. (2008). “Travel Search Intelligence: OTA performance.” http://www.competeinc.com/research/newsletters/print/travel-search-intelligence-ota-performance/ (accessed April 8,2010).

15.

Dutta

Kirti

Manaktola

Kamal

. (2009). “Managing Hotel Distribution for Tourism Growth.” Worldwide Tourism and Hospitality Themes, 1 (1): 40-51.

16.

Enquiro. (2006a). “Enquiro Eye Tracking Report I: Google.” http://pages.enquiro.com/whitepaper-enquiro-eye-tracking-report-I-google.html (Accessed June 17, 2009).

17.

Enquiro. (2006b). “Enquiro Eye Tracking Report II: Google, MSN and Yahoo! Compared.” http://pages.enquiro.com/whitepaper-enquiro-eye-tracking-report-II.html (Accessed June 17, 2009).

18.

Evans

Michael P.

(2007). “Analyzing Google Rankings through Search Engine Optimization Data.” Internet Research, 17 (1): 21-37.

19.

Garson

David G.

(2008). PA 765 Statnotes [Online]. An Online Textbook. http://www2.chass.ncsu.edu/garson/pa765/statnote.htm (accessed August 25, 2005).

20.

Gazzoli

Gabriel

Kim

Woo Gon

Palakurthi

Radesh

. (2008). “Online Distribution Strategies and Competition: Are the Global Hotel Companies Getting It Right?” International Journal of Contemporary Hospitality Management, 20 (4): 375-87.

21.

Google Inc. (2010). “Google Online Marketing Challenge: Academic Guide 2011.”http://static.googleusercontent.com/external_content/untrusted_dlcp/www.google.com/en/us/onlinechallenge/files/academic_guide.pdf.

22.

Green

David C.

(2003). “Search Engine Marketing: “Why It Benefits Us All.” Business Information Review, 20 (4): 195-202.

23.

Hansell

Saul

. (2007). Google Keeps Tweaking Its Search Engine.New York Times. http://www.nytimes.com/2007/06/03/business/yourmoney/03google.html (accessed June 3, 2007).

24.

“Hitwise Report—Top 20 Sites | Engines.” (2009). http://www.hitwise.com/datacenter/main/dashboard-10133.html (Accessed June 17, 2009).

25.

Hsu

Kathy

Zhub

Zhiwei

Agrusac

Jerome

. (2004). “Turning Click-through Visitors into Customers: A Study of Chinese Hotel Websites.” Journal of Hospitality Marketing and Management, 11 (4): 81-91.

26.

Huang

Truman

Law

Rob

. (2003). “Modeling and Comparing Internet Marketing: A Study of Mainland China andHong Kong based Hotel Websites.” In Information and Communication Technologies in Tourism, edited by Frew

Andrew J.

New York: Springer, Wien, pp. 173-82.

27.

Internet and Mobile Association of India and Indian Market Research Bureau (IAMAI-IMRB). (2008). “Search Engine Marketing.” http://www.slideshare.net/targetseo/search-engine-marketing-india-sem-india-imrb-presentation. (Accessed May 5, 2010)

28.

iProspect (2008). “iProspect Blended Search Results Study.” http://www.iprospect.com/premiumPDFs/researchstudy_apr2008_blendedsearchresults.pdf (Accessed June 18, 2009).

29.

Jeong

Miyoung

Haemoon

Gregoire

Mary

. (2003). “Conceptualizing Web Site Quality and Its Consequences in the Lodging Industry.” International Journal of Hospitality Management, 22 (2): 161-75.

30.

Kracht

John

Wang

Youcheng

. (2010). “Examining the Tourism Distribution Channel: Evolution and Transformation.” International Journal of Contemporary Hospitality Management, 22 (5): 736-57.

31.

Law

Rob

Hsu

Cathy

. (2006). “Importance of Hotel Website Dimensions and Attributes: Perceptions of Online Browsers and Online Purchasers.” Journal of Hospitality and Tourism Research, 30 (3): 295-312.

32.

Law

Rob

Huang

Truman

. (2006). “How Do Travelers Find Their Travel and Hotel Websites?” Asia Pacific Journal of Tourism Research, 11 (3): 239-46.

33.

Lewis

(2009). “Top Ten Search Engines—Top 10 SEs.” http://www.seoconsultants.com/search-engines/ (Accessed June 22, 2009).

34.

Luk

Robert W. P.

Kwok

K. L.

(2002). “A Comparison of Chinese Document Indexing Strategies and Retrieval Models.” ACM Transactions on Asian Language Information Processing, 1 (3): 225-68.

35.

Malaga

Ross A.

(2008). “Worst Practices in Search Engine Optimization.” Communications of the ACM, 51 (12): 147-50.

36.

Matovic

Dragan

McCleary

Ken

. (2003). “Marketing in the Next Decade: A Qualitative Study of the US Hotel Industry.” Journal of Travel and Tourism Marketing, 14 (2): 47-65.

37.

Matzler

Kurt

Waiguny.

Martin

(2005). “Consequences of Customer Confusion in Online Hotel Booking.” In Information and Communication Technologies in Tourism, edited by Frew

Andrew J.

New York: Springer, Wien, 2005, pp. 306-17.

38.

McKinsey Group. (2007). “How Businesses Are Using Web 2.0: A McKinsey Global Survey.” The McKinsey Quarterly—2007 McKinsey Survey in Internet Technologies.

39.

Miller

Brian

. (2005). “Building E-loyalty of Lodging Brands—Avoiding Brand Erosion.” Journal of Travel and Tourism Marketing, 17 (2): 133.

40.

Nayar

Ajith

Beldona

Srikanth

. (2010). “Interoperability and Open Travel Alliance Standards: Strategic Perspectives.” International Journal of Contemporary Hospitality Management, 22 (7): 1010-32.

41.

O’Connor

Peter

. (2009). “Pay-per-Click Search Engine Advertising—Are Hotel Trademarks Being Abused?” Cornell Hospitality Quarterly, 50 (2): 232-44.

42.

Pan

Fesenmaier

D.R.

(2006). Online information search and trip planning process. Annals of Tourism Research, 33(3), 809-832.

43.

Parr

(2010). Google testing hotel prices through Google Maps. Mashable. http://mashable.com/2010/03/22/google-maps-hotel-prices/ Accessed Sep 10, 2010.

44.

Pew Internet Report. (2008). “Search Engine Use.” http://www.pewinternet.org/~/media//Files/Reports/2008/PIP_Search_Aug08.pdf.pdf (Accessed June 18, 2009).

45.

PhocusWright Connect. (2009). “Freedom of Choice in Expedia Contract Negotiations?” http://connect.phocuswright.com/2009/10/freedom-of-choice-in-expedia-contract-negotiations/.

46.

Pine

Ray

Phillips

Paul

. (2005). “Performance Comparisons of Hotels in China.” International Journal of Hospitality Management, 24 (1): 57-73.

47.

Pink

Jonathan

. (2005). “Initial Interest Confusion Doctrine. Intellectual Property Law.” http://www.ipfrontline.com/depts/article.asp?id=4697&deptid= 4.

48.

Ramos

Andreas

Cota

Stephanie

. (2008). Search Engine Marketing. New York: McGraw-Hill.

49.

Rangaswamy

Giles

Lee

Seres

Silvija

. (2009). “A Strategic Perspective on Search Engines: Thought Candies for Practitioners and Researchers.” Journal of Interactive Marketing, 23 (1): 49-60.

50.

Sen

, 2005. Optimal search engine marketing strategy. International Journal of Electronic Commerce, 10(1), 9-25.

51.

Starkov

Max

. (2009). “Archive for the ‘Online Travel Agencies (OTAs)’ Category: The End of the OTA Merchant Model—This Time for Real.” HeBS Internet Marketing Blog. http://www.hospitalityebusiness.com/blog/topics/online-travel-agencies/ ((accessed April 8, 2010).

52.

Telang

Rahul

Rajan

Uday

Mukhopadhyay

Tridas

. (2004). “The Market Structure for Internet Search Engines.” Journal of Management Information Systems, 21 (2): 137-60.

53.

Tso

Angie

Law

Rob

. (2005). “Analyzing the Online Pricing of Hotels in Hong Kong.” International Journal of Hospitality Management, 24 (2): 301-7.

54.

Weinfurt

Kevin P.

(1998). “Multivariate Analysis of Variance.” In Reading and Understanding Multivariate Statistics, edited by Grimm

L. G.

Yarnold

P. R.

Washington, DC: American Psychological Association, pp. 245-76.

55.

Xiaoyi

. (2008). “Performance Evaluation of Commercial Web Sites: An Exploratory Study of Hotels in Mainland China.” 4th International Conference on Wireless Communications, Networking and Mobile Computing, 2008. WiCOM ‘08, pp. 1-4, 12-14. doi:10.1109/WiCom.2008.221610.1109/WiCom.2008.2216. http://ieeexplore.ieee.org/search/freesearchresult.jsp?newsearch=true&queryText=10.1109%2FWiCom.2216&filter=&x=28&y=21 . (Accessed May 23, 2010)

56.

Xiang

Zheng

Wöber

Karl

Fesenmaier

Daniel R.

(2008). “Representation of the Online Tourism Domain in Search Engines.” Journal of Travel Research, 47 (2): 137-50.

57.

Xing

Lin

Zhangxi

. (2006). “The Impact of Search Engine Optimization on Online Advertising Market.” In Proceedings of the 8th International Conference on Electronic Commerce: The New E-commerce: Innovations for Conquering Current Barriers, Obstacles and Limitations to Conducting Successful Business on the Internet, vol. 156. New York, NY: ACM, pp. 519-529. http://portal.acm.org/citation.cfm?id=1151454.1151531 (Accessed May 5, 2010)

58.

Louis

Kin Fun

Manning

Eric G.

(2006). “The Adaptability of English Based Web Search Algorithms to Chinese Search Engines.” Lecture Notes in Computer Science, 3841: 402-13. http://www.springerlink.com/content/7m144gx315370357 (Accessed Sep 5, 2010).

59.

Vogt

Fesenmaier

(1998). Expanding the functional information search model, Annals of Tourism Research, 25(3): 551-578.