Research Journal of Hospitality & Tourism

Journal of Hospitality & Tourism Research http://jht.sagepub.com

Developing and Benchmarking Internet Marketing Strategies in the Hotel Sector in Greece Marianna Sigala Journal of Hospitality & Tourism Research 2003; 27; 375 DOI: 10.1177/10963480030274001 The online version of this article can be found at: http://jht.sagepub.com/cgi/content/abstract/27/4/375

Published by: http://www.sagepublications.com

On behalf of:

International Council on Hotel, Restaurant, and Institutional Education

Additional services and information for Journal of Hospitality & Tourism Research can be found at: Email Alerts: http://jht.sagepub.com/cgi/alerts Subscriptions: http://jht.sagepub.com/subscriptions Reprints: http://www.sagepub.com/journalsReprints.nav Permissions: http://www.sagepub.com/journalsPermissions.nav Citations (this article cites 28 articles hosted on the SAGE Journals Online and HighWire Press platforms): http://jht.sagepub.com/cgi/content/abstract/27/4/375#BIBL

Downloaded from http://jht.sagepub.com by marianna sigala on July 4, 2007 © 2003 ICHRIE. All rights reserved. Not for commercial use or unauthorized distribution.

ARTICLE

JOURNAL Sigala / INTERNET OF HOSPIT MARKETING ALITY & TOURISM STRATEGIES RESEARCH

DEVELOPING AND BENCHMARKING INTERNET MARKETING STRATEGIES IN THE HOTEL SECTOR IN GREECE MARIANNA SIGALA University of Strathclyde Despite the exponential growth of e-commerce on the Internet, little is still known on how the new medium is transforming marketing concepts/practices and their effectiveness. This empirical study aims to fill in this gap. This article first analyzes the Internet’s capabilities and features as well as the new virtual marketspace that Internet advances have fostered. After reviewing models and strategies for Internet marketing, an Internet marketing mix is proposed based on the Internet strategies of hotels in Greece that were investigated. Using a nonparametric technique, the data envelopment analysis (DEA), Internet strategies were also benchmarked to identify best practices and provide suggestions on the development of effective Internet marketing strategies. KEYWORDS: Internet; marketing; strategy; hotel; benchmarking; DEA

The omnipresent nature of the Internet has been a defining characteristic of the new world of e-commerce. It has been estimated that e-commerce transactions would exceed the $410 billion by 2003 (Turban, King, Lee, Warkentin, & Chung, 2002). In the same vein, several studies report high Internet adoption rates by tourism and hospitality operators (e.g., Sigala, Airey, Jones, & Lockwood, 2000; Siguaw, Enz, & Namasivayam, 2000; Van Hoof, Ruys, & Combrink, 1999, 2000; Yuan & Fesenmaier, 2000). However, little is known of the degree to which the Internet has transformed business models. Although the Internet can transform the strategic position of tourism and hospitality operators by leveraging their efficiency, differentiation, operational costs, and response time, no well-defined pattern has yet emerged about how best to employ it (Baker, Cossey, & Sussmann, 1999). As most of the studies are based on lists of key Web-site features and questionable number-of-hits figures, effective Internet exploitation is still unclear because such measurement is usually meaningless and ill defined (InfotecTravel.com, as cited in Baker et al., 1999). This article goes a step further by illustrating a systematic approach to the analysis and classification of hotel-related Internet marketing strategies in light of the new opportunities and competitive pressures generated by the spread of the Journal of Hospitality & Tourism Research, Vol. 27, No. 4, November 2003, 375-401 © 2003 International Council on Hotel, Restaurant and Institutional Education

375


376

JOURNAL OF HOSPITALITY & TOURISM RESEARCH

Internet and its related services—WWW and e-mail. To that end, after reviewing the unique capabilities enabled by the Internet and the changes that they foster in the virtual marketspace, this article proposes a model of an Internet marketing mix for developing effective Internet marketing strategies. The model consists of five dimensions, namely the four P’s—product, price, promotion, and placement—and the customer relationship dimension, and it is tested by gathering data from the Greek hotel sector. In particular, the model was used for investigating how hotel operators are transforming their marketing strategies due to Internet advances as well as for benchmarking the effectiveness of the investigated marketing practices. THE CAPABILITIES AND IMPACT OF INTERNET TOOLS

The Internet revolution has introduced a wide range of new marketing tools, which are accessible and affordable for smaller organizations. The Internet has an equalizer effect in the hotel industry as it creates a level playing field where size is no longer apparent (Connolly & Sigala, 2001). Indeed, Sigala’s (2002) and Van Hoof et al.’s (2000) findings revealed that the size and the type of the hotel property had no effect on the use of Internet tools, including Web page and e-mail. In comparing the Internet with other technological tools, Zott, Amit, & Donlevy (2000) identified three major and unique capabilities of the Internet: 1. Interactivity. Because of the real-time nature of the Internet, relationships between organizations and customers are becoming more interactive—two way and information rich—creating new paradigms of product design and customer service. For example, by gathering customer information, Amazon.com sends personalized emails to its customers with book offers relevant to their interests. On the other hand, Amazon.com also plans to increase customer participation in the creation of new books by gathering and analyzing customer feedback regarding what they want to read in the future (Zott et al., 2000). 2. Connectivity. The open and global nature of the Internet is fostering the creation of a shared global marketspace. The radical increase in connectivity is giving rise to new communication and coordination mechanisms across organizations and customers as well as within groups of customers themselves. As the law of network externalities implies, the value of the network increases with an increase in numbers. 3. Convergence. Digital technologies themselves are converging, making the Internet ubiquitous. The emerging wireless application protocol (WAP) technology, for example, permits mobile Internet access.

Internet capabilities have created a new ecosystem, the marketspace, that is characterized by a change in the conventional economics of information. The latter can be represented by a trade-off between the richness of information and reach of message (Evans & Wurster, 1997), which dictates that information rich in dialogue, customization, and interactivity can reach only a limited audience. Reach is about access and connection, meaning how many customers and suppliers a business can connect with and how many products can be offered. For exam-


Sigala / INTERNET MARKETING STRATEGIES

377

ple, the Internet offers great opportunities for pursuing multichannel strategies, boosting businesses’ onward advertising, and reaching different market segments (Hagel, 1999; O’Connor, 2002; Rowley, 2001). On the other hand, richness is the depth and detail of information that can be given to customers as well as the depth of the collected customer information. For example, businesses can simply publish price catalogues or continually update online prices depending on demand patterns. The Internet makes this trade-off obsolete, creating a virtual marketspace characterized by three features, namely reach, richness, and digital representation (Zott et al., 2000). Reach implies that any vendor that can connect to the network can sell a product to connected consumers anywhere in the world at any time. This leads to increased consumer power because consumers now have less temporal and spatial restrictions and more choices between products and suppliers. Richness provides an enhanced potential to reduce any asymmetry of information between buyers and sellers as both sides are empowered to use information for their own interests. The digital representation denotes the inability to touch and feel the product, to visit a physical storefront, and to have a human interaction. The burden of alleviating the previous limitations lies with the sellers by guiding the consumers through appropriate steps to complete a purchase. MODELING INTERNET MARKETING STRATEGIES

Marketing, by nature, should be a creative and adaptive discipline that is constantly regenerating itself. In this vein, the new character and capabilities of the Internet have made marketing theoreticians to start developing new concepts and paradigms. Some contend that the very principles of marketing will, and should, be changed and developed, for example, principles regarding brand and marketing communications management (Chen, 2001; Hanson, 2000; Rowley, 2001). Others argued that the traditional marketing practices will continue—right product, place, promotion, and price—but what are changing are the techniques by which the marketing mix variables are realized to exploit the enhanced and new capabilities of the medium (Brady, Saren, & Tsokas, 1999: Leeflang & Wittink, 2000; Mahajan & Venkatesh, 2000). One view of the information age’s impact on marketing is that the search for a new marketing paradigm has begun. Indeed, the increased power of consumers and the dynamics and features of information resources have given rise to several new concepts and marketing practices, for example, community, viral, reversed, contextual/situational, and interactive marketing (Hagel, 1999; Hardaker & Graham, 2001; Kozinets, 1999; Werbach, 2000). In the same vein, many contend that a new and genuine transformation is taking place in relationship marketing (Alford, 2001; Gilbert, Powell-Perry, & Widijoso, 1999; Kiang, Raghu, & Shang, 2000; Siguaw & Enz, 1999; Walsh & Godfrey, 2000). A literature review identified the following models aiming at measuring the transformation of marketing activities on the Internet and the level of Web-site functionality. Cronin’s (1995) Internet value chain illustrates how the Internet can be applied in all marketing functions from presales and sales activities to aftersales services.


378


The model identifies three areas of Internet marketing capabilities, namely, marketing and product research, sales and distribution, and support and customer feedback. Several studies investigating the Internet use by tourism and hospitality operators have used the Internet value chain as a basis to list Web site attributes into specific business functions (e.g., Baker et al., 1999; Procaccino & Miller, 1999; Weeks & Crouch, 1999). However, as the Internet value chain does not consider the three Internet capabilities, these studies inherently fail to incorporate Internet’s transformational impact (Zott et al., 2000). On the other hand, empirical research on Internet use (Jarvela, Loikkanen, Tinnila, & Tuunainen, 1999; Kuusela, Maisala, & Saarinen, 1999) that is based on the e-commerce (EC) service index framework overcomes the previous limitation regarding the inclusion of technological advances of the Internet. The EC framework has two dimensions: the existence of different services (e.g., dissemination of information, communication, order, payment, and delivery) and the development of services toward more sophisticated and integrated processes between the customer’s actions and the suppliers’ IT (information technology) systems (e.g., traditional processes, personalized or customized services). However, although the framework embraces the types of customer services and the technical advancement, it fails to highlight how the Internet’s capabilities have transformed marketing strategies (Dussart, 2000; Zott et al., 2000). Angehrn (1997) proposed a model for Internet marketing strategies incorporating both dimensions—the route of the number and types of services and the sophistication of the Web sites. Angehrn’s (1997) information communication distribution and transaction (ICDT) model describes four virtual marketspaces. The virtual information space (VIS) consists of new channels for displaying information and accessing company/product and service-related information. This is a one-way communication channel. The virtual communication space (VCS) consists of new channels for engaging in relationship-, ideas-, and opinion-building activities. It is the space where interaction and relationship building with the customer occurs. The virtual distribution space (VDS) includes new distribution channels. The virtual transaction space (VTS) provides new channels where reservations, invoices, and payments occur. The ICDT model provides the basis for distinguishing between four separate types of marketing strategies. Businesses aiming at increasing the visibility and improving the perception of products or services could achieve it by either Internet-based marketing or advertising initiatives (VIS presence) or by monitoring and influencing how businesses communicate (VCS presence). VDS and VTS presence reflects strategies aiming at reducing cost, improving quality or innovating products/services by either distributing them via the Internet or by exploiting the Internet for conducting transactions. In developing strategies in the four spaces, two issues should be considered. First, the VIS and the VCS affect the globalization level of the business by making products available at any time. Second, the VDS and the VTS presence foster disintermediation through a direct contact with customers. Each dimension of Internet presence can be further classified in terms of its technical sophistication (simple or advanced) and level of customization (low or



379

high). For instance, a simple, generic VIS can be established by publishing marketing and advertising material whereas an advanced VIS presence might include sophisticated, interactive multimedia presentations (e.g. three-dimensional, interactive entertainment presentations, web cameras). A high level of customization can be achieved when mechanisms exist to: (a) gather data on how potential or current customers interact with the company’s Internet site, (b) infer customers’ preferences, and then (c) react through personalized replies. The level of sophistication and customization are argued to affect the transformational power of the Internet on marketing and the effectiveness and results of the related strategy. For example, the pursuit of a customer relationship management strategy with the use of data mining tools and intelligence agents promises huge online sales and Web-site stickiness (Alford, 2001; Zott et al., 2000). The underlying assumption is that the greater the level of sophistication and customization, the greater the results. This hypothesis is based on the threestage framework of information, and communication technologies (ICT) exploitation referring to automation, information, and transformation (Zuboff, 1988) that is usually used for tracking the business value of ICT. Zuboff (1988) argued that ICT boosts greater benefits when ICT tools are used not only for automating business processes but also for informating or transformating them. These stages are also similar to arguments identifying the different levels of ICT benefits depending on the ICT level and type of use that were referred to as efficiency, effectiveness, and unthinkable according to Haeckel (1985), or as efficiency, effectiveness, and innovation according to Hammer & Mangurian (1989). Overall, the ICDT model is argued to be a valuable tool for measuring the degree of Internet-fostered marketing transformation and its business value and effectiveness (Wen, Chen, & Hwang, 2001). Thus, this article proposed and tested a model for measuring and benchmarking Internet marketing strategies that is based on the ICDT model and is analyzed as follows. The unique Internet capabilities enable the development of an extended Internet marketing mix that is composed of five dimensions—the traditional four Ps (product, place, price, and promotion) in which each one corresponds to each virtual space, and the customer relations (C) dimension, which accounts for the new knowledge-based applications enabled by Internet tools (e.g., Murphy, Hofacker, & Bennett, 2001; Rowley, 2001). Similar to the four virtual spaces, the four Ps and the C are characterized by different levels of sophistication, which, as previously analyzed in Angehrn’s (1997) arguments, represent the extent to which hotels are exploiting the interactivity and connectivity capabilities of the Internet to innovate their products through customization and/or configuration. Customization describes the process of individualizing a product or service (Pine, 1993), whereas configuration/aggregation refers to the bundling of different product/service components to some kind of integrated offerings (Werthner & Klein, 1999). The appropriateness, effectiveness, and competitiveness of designing Internet marketing strategies according to these five dimensions is widely argued. Evans and Wurster (1999) argued that the struggle of competitive advantages on the Internet would be along three dimensions, namely reach, richness, and affiliation (i.e., efforts to create and maintain long-term customer relations). O’Connor


380


(1999) claimed that electronic distribution strategies should aim to achieve reach, content, interactivity, and feedback to provide value-added services and lock up customers. Sigala, Lockwood, and Jones, (2001) argued that successful hotel marketing strategies in the future would fully exploit the network and interactive capabilities of the Internet technologies to pursue multichannel distribution strategies that optimize yield by distribution channel, hotel location, hotel brand, and individual customers. Indeed, research (O’Connor, 2002; O’Connor & Horan, 1999) reveals that successful hotel brands use multiple simultaneous routes to the marketspace (i.e., multichannel strategy to extent their reach), while at the same time align their online price strategies to reflect the type and profile of the market segments found in different channels (i.e., manage affiliation and richness). Zott et al. (2000) also found that personalization of product and/or information and the development of virtual communities can create Web site stickiness as well as enhance businesses’ capabilities to effectively address the opportunities and threats created by the virtual marketspace. Liu and Arnett’s (2000) findings revealed that personalization and online promotion were critical success factors for Web site effectiveness as they stimulated online sales and repeat visits. In assessing the impact of Internet on hotel productivity in the UK hotel sector, Sigala (2002) provided evidence that hotels exploiting the networking capabilities of the Internet (e.g., for onward advertising and promotion) and its informalization tools (e.g., for gathering and analyzing customer information and then personalizing offerings/services) significantly outperformed hotels that simply used the Internet as an additional distribution channel for making reservations and/or promotion. RESEARCH OBJECTIVES AND METHODOLOGY Research Objectives and Questions

The primary aim of the study was to propose a model for developing and benchmarking Internet marketing strategies that fully exploit Internet features. The model was tested by gathering data from the Greek hotel sector and analyzing them by using the data envelopment analysis (DEA). Apart from my knowledge and interest for the Greek hotel sector, Greece was chosen as the geographical area of the research for a major and important reason. Although several research studies have focused on the use of Internet in the hotel sector either on different countries and/or the international sector (e.g., Sigala et al., 2000; Siguaw et al., 2000; Van Hoof et al., 1999, 2000; Yuan & Fesenmaier, 2000), no data and research currently exist regarding the use of the Internet by hotels in Greece. This lack of knowledge and research becomes more crucial because of the great economic importance of tourism in Greece as well as the substantial potential of the Internet for empowering hotel operators by providing them with an additional, more efficient, and effective marketing and distribution channel (Sigala, 2000). Thus, by investigating the current Internet marketing strategies and identifying areas of improvement, this study is of great value for the academic, professional,



381

and public community for fostering and supporting further research as well as identifying areas of public involvement and support regarding the subject area. Moreover, the Greek hotel product and industry is very similar to that of other Mediterranean countries in Europe and is characterized by intense seasonality and a large number of small and medium family-owned and managed hotels (National Statistical System of Greece, 2000). Thus, although the findings of this study may be location specific and biased, they might be considered as a pilot indication and basis for researchers at other Mediterranean tourism destinations. Moreover, this article also presents a research methodology and findings that international researchers can use for conducting comparative cross-country/continent studies. Indeed, comparative studies are very important for enlightening the impact and/or the moderating effect of contextual factors affecting the adoption and development of Internet for marketing strategies and for identifying appropriate strategies for eliminating the digital divide among countries. Thus, overall, this article models and evaluates the Internet marketing strategies developed by hotels in Greece to exploit the unique capabilities enabled by the Internet. Specifically, this article investigates how hotels in Greece are exploiting the real-time interactivity and global connectivity of the Internet for developing their Internet marketing mix strategies. Hotels’ Internet marketing strategies are also benchmarked for identifying best practices and appropriate actions for improvement are proposed. Specifically, for measuring the use of the Internet by Greek hotels and then modeling their Internet marketing strategies, the Internet marketing mix model was used, as it was previously debated to be a valuable tool in analyzing how the transformational capabilities of the Internet are being exploited for developing successful Internet marketing practices. The model identified two areas of Internet-enabled marketing transformation: (a) the type and the number of the Internet marketing mix dimensions being transformed and (b) the level of the sophistication of the transformed dimensions that exploit the Internet’s customization/aggregation capabilities. The type and the number of the transformed dimensions have been argued to reflect the type of the business strategy (e.g., disintermediation, cutting costs, great exposure, globalization), whereas the level of the sophistication indicates the level of the exploitation of the Internet’s capabilities that, in turn, determines the effectiveness of the related Internet marketing strategy. Thus the research objectives can be reformulated in the development and test of the following research questions: • What is the composition/structure of the hotels’ Internet marketing mix? Which dimension(s) are being transformed, and what Internet marketing strategies are being followed? • What is the level of sophistication/transformation of the Internet marketing mix dimensions? Do hotels fully exploit the Internet capabilities and features? • Are more extended Internet marketing strategies more effective than less extended Internet marketing strategies? In other words, do hotels that have transformed more


382


Internet marketing mix dimensions outperform hotels that have transformed fewer Internet marketing mix dimensions? • Are higher sophisticated Internet marketing strategies more effective than lower sophisticated Internet marketing strategies? Do hotels with higher levels of Internet marketing mix sophistication outperform hotels with lower Internet marketing mix sophistication? Development and Measurement of Constructs

For measuring the sophistication of the Internet marketing strategies, each of the five dimensions of the model was further analyzed in several aspects/features (Table 1) that indicate the degree to which hotels have adopted sophisticated (i.e., customized and/or aggregated) Internet strategies. Consistent with Angehrn’ s (1997) arguments and the previous analysis of the Internet marketing mix model, the aspects of each dimension are characterized as of low or high sophistication depending on whether the interactive and connectivity capabilities of the Internet are used. In Table 1, features written in italics represent activities of low sophistication. For each hotel, a transformation degree of each dimension was calculated by the ratio of the sum of the aspects used to the total number of the aspects within the dimension (each feature carried the same weight). So, in the VIS the availability of static information on the Web site is characterized as of a low sophistication aspect, whereas the remaining aspects were characterized as of high sophistication because they reflected the use of multimedia features as well as the use of customer information for providing customized interfaces and products/services (Angehrn, 1997; Wen et al., 2001). The provision of prices on the Web site was characterized as of a low sophistication aspect in the VTS, whereas when hotels regularly changed their prices online depending on demand, guest profiles, and/or when customers could negotiate room rates online (i.e., bidding/auctions), then these two aspects of the VTS were characterized as of high sophistication because they reflected the use of the networking and informalization (i.e., reach and richness) capabilities of the Internet (O’ Connor, 2002; Sigala et al., 2001). In the same vein, the two aspects of the VDS indicating that online booking option was not available (i.e., bookings were received by email requests) as well as that the hotel Web site was promoted on the Internet (e.g., through search engines, infomediaries) were characterized as of low sophistication, whereas the availability of online bookings and secure payments represented high sophistication aspects because they required the use of greater and more complex technological functionality (Wen et al., 2001; Zott et al., 2000;). In the VCS, the first two aspects reflecting the use of the Internet for advertising and for the provision of online promotions corresponded to a low sophistication use of Internet tools, whereas the last three aspects were characterized as of a high sophistication because they indicated that the informalization and networking capabilities of the Internet were exploited for customizing promotions and allowing guests/partners to customize promotions depending the guests’ profiles and needs (Gretzel, Yuan & Fesenmaier, 2000; Rowley, 2001). All aspects of the customer relations dimension were characterized as of a high sophistication because


Table 1 The Development of the Internet Marketing Mix (% of respondents) Transformation of Products (VIS) Downloaded from http://jht.sagepub.com by marianna sigala on July 4, 2007 © 2003 ICHRIE. All rights reserved. Not for commercial use or unauthorized distribution.

The availability of product-related information online The availability of interactive entertainment multimedia information The customization of products/Web page for individual or groups of customers The participation of customers in the specification and design of itineraries, hotel rooms, and/or amenities/services

Transformation of Pricing (VTS) 100 17

12

40 5 9

0

Transformation of Place (VDS)

The availability of e-mail request for bookings The promotion of the Web site on the Internet The availability of online booking The availability of secured online payment

The availability of pricing information online The dynamic customization of prices based on either personal information or on demand patterns The availability of online price negotiation either on property Web site or on partners Web site

Transformation of Promotion (VCS)

100 41 17 16

The use of online advertising The use of online promotions such as sales and discounts The customization of online promotions The participation of customers in online promotions Links with other organizations in organizing online promotions

21 52 7 0 33

Transformation of Customer Relations The provision of online customer service The online identification and tracking of customers to provide customized services The provision of online communications to customers The creation of online communities for customers The solicitation of online feedback from customers

28 0 0 3 26

383

Note: VIS: virtual information space; VTS: virtual transaction space; VDS: virtual distribution space; VCS: virtual communication space. Features written in italics represent activities of low sophistication.

384


they actually required the exploitation of enhanced technological tools and features for their provision (Murphy et al., 2001; Rowley, 2001). Although several metrics are used for measuring Web-site marketing effectiveness, there is a lack of standardization of practices (Murphy et al., 2001). However, number of visits rather than hits is widely agreed as a more robust metric of measuring online traffic. This is because one person logging into a Web page equals to one visit but to several hits depending on the number of tables, pictures, and other features that exist on the Web page. However, because such metrics are obtained through log files, it is difficult to ascertain the meaning behind the results. For example, the visit of a Web site might be an intended but also an incidental behavior. In reviewing and assessing the use of several metrics, Gretzel et al. (2000) argued that metrics reflecting whether consumers seek additional information online (e.g., sending a request) are a better way to establish the effectiveness of a Web site. O’Connor (2002) argued that the evaluation of the effectiveness of hotel Web sites should also consider the conversion of lookers into bookers, and data for bookings generated from the Web site should also be measured. Schlosser, Shavitt, and Kanfer (1999) added that productivity gains, such as savings in commissions and from direct e-mailing campaigns, should be considered. However, in measuring the impact of Internet marketing practices on hotel productivity, Sigala’s (2002) findings revealed that such savings do not have any significant impact on ultimate hotel performance metrics unless a high percentage of hotel reservations are received through the Internet. In other words, such savings are not important for ultimate performance benefits unless Internet marketing strategies do achieve a high volume of Internet reservations. Thus, the achievement of Internet sales is a major and primary metric for evaluating Internet marketing effectiveness. Recent studies (e.g., Cox & Dale, 2001; Jun & Cai, 2001) have also stressed the importance of measuring effectiveness of ecommerce practices for enhancing the quality of customer service. In this vein, five types of statistical data were gathered to benchmark the effectiveness of Internet marketing strategies: 1. 2. 3. 4.

the number of annual visits on the Web page, the number of annual Web-generated requests for information/reservations, the proportion of reservations generated through the Internet during the past year, respondents’ perceptions on how the Internet marketing practices have enhanced the way and quality of customer service, and 5. the average daily rate (ADR) of Internet-generated reservations expressed as a proportion of the hotel’s rank rate.

However, because the ADR is significantly related to the classification (category) of the hotel, to make the achieved ADR of Internet-generated reservations comparable among hotels from different categories, the ADR was expressed as a percentage of the hotel’s rank rate, and this percentage, rather than the raw figure of the ADR, was used in the benchmarking analysis. Rank rate in this study was defined as the official room rate that the hotel has published and reported to the Greek Tourism Organization and that is related to the hotel’s classification (i.e., higher categories have higher rank rates).



385

Figure 1 The Data Envelopment Analysis (DEA) Internet Marketing Strategy Efficiency Score # of visits + # of requests + # of reservations + ADR of Internet reservations + customer service DEA efficiency score = VIS + VCS +VTS +VDS + C Whereby:

#

= number of

To benchmark the different Internet marketing strategies on all these five dimensions simultaneously, the DEA technique was used. DEA is a multivariate, nonparametric technique that benchmarks units by comparing their ratios of multiple inputs to produce multiple outputs at the same time and using the concept of the performance frontier (Avkiran, 1999). In identifying the efficient units under the DEA technique, there is no particular structure superimposed on the data, therefore, it becomes a valuable tool in benchmarking (Al-Shammari & Salimi, 1998). DEA has been used extensively for performance benchmarking (e.g., Chatzoglou & Soteriou, 1999) in various industries (Avkiran, 1999). However, DEA can actually be used to benchmark the performance of any system because DEA modeling allows the analyst to select inputs and outputs in accordance with managerial focus and the desired analysis. Moreover, the use of DEA for benchmarking has also the following advantages (Banker & Thrall, 1992): • DEA is independent of the units in which inputs and outputs are measured, which gives great flexibility in specifying the outputs and inputs to be studied. • DEA compares simultaneously multiple inputs and outputs of comparable operating units from the reference set and generates one overall performance score by using a benchmark score of 100% efficiency (i.e., the optimum performance within the comparison set of units).

Thus, in this study, the DEA was used for benchmarking the Internet marketing strategies based on the overall Internet marketing efficiency score that is calculated by comparing the transformation degree of each of the five Internet marketing mix dimensions (five inputs) to the five Internet output figures (i.e., number of Internet visits, requests and reservations, the ADR of Internet reservations, and managers’ perceptions of the enhanced customer service) (Figure 1). The value of this technique for benchmarking Internet marketing strategies is significant. Because the impact of each dimension on the output figures cannot be separated, the DEA’s value and applicability is argued because it produces an overall marketing performance score by simultaneously comparing the five inputs to the five outputs. This cannot be achieved through other ratio or regression analysis techniques. As inputs and outputs were also expressed in different measurement quantities, DEA can also deal with these different measurements. Ultimately, the overall Internet marketing performance score was used to separate best practices from inefficient Internet marketing practices and to provide suggestions for Internet strategies’ improvement and development.


386


Sample Design

A Web search using the key words hotel, Greece, reservation in two search engines (one Greek, in.gr, and Yahoo!) and three travel directories (two Greek, greek-hotels.com and hotel-in-greece.com, and one foreign operated/based, worldres.com) was conducted to identify the research potential population (i.e., hotels that had and use a Web site). In investigating hotels’ use of the Internet for marketing purposes, Murphy (1996) used a Web search for identifying his study’s population. Indeed, Internet search for the identification and study of online practices is heavily found in the literature. For example, O’Connor (2002) and O’Connor and Horan, (1999) highlighted the importance of doing Internet research for gathering data regarding the online pricing and multiple distribution policies of hotels. Zott et al. (2000) advocated the importance of carrying out extensive Internet research for identifying e-commerce practices and selecting best practices online, whereas Chen (2001) exemplified the importance and value of the continuous online studies that are carried out by Internet companies for gathering data regarding the online Internet population and its online behavior. The Web search was conducted in April 2001 and after adding up the results taken from each search engine, and directory a total figure of 1,677 hotels with a Web site was found. All the results of the Web search including hotels’ names, addresses, and URL were copy pasted into a hotel database in a Microsoft Word file. However, because the Web sites of several hotels were listed in more than two directories and/or search engines, the former database had to be filtered to consider for duplications of data. The Find facility of Microsoft Word was used to identify multiple entries of hotels and to automate the filtering process. Finally, only 548 hotels with a Web site were identified. All 548 Web sites were visited for the following reasons: (a) investigate whether the Web sites existed and were functional at the time the research was conducted, (b) get an overall picture of the category of the hotels, and (c) investigate whether an e-mail address is provided. It was found that Web sites referred to A-, B-, or C-category hotels (i.e., 5-, 4- or 3star hotels respectively) and that an e-mail address was published on all 548 Web sites. It was so decided to conduct an e-mail survey. However, due to time constraints, it was not possible to target and analyze data from all 548 hotels, therefore, a systematic sampling method was applied for getting a sample. Every other e-mail address from the filtered hotels’ list was obtained, and a database of 274 e-mail addresses was compiled. However, a careful examination of the e-mail database revealed that multiple entries of e-mail addresses existed. After investigating the reason for the latter, it was found that in several Web sites the e-mails provided were similar, because the hotel properties that the Web sites promoted were managed under the same owner and/or hotel chain. It was decided not to require information for more than one hotel property per e-mail sent to avoid putting off the e-mail recipient from helping in the research and to increase response rates. Thus, the e-mail database was checked for multiple entries, and finally, 216 unique e-mail addresses were identified. The study’s questionnaire was e-mailed (as a Microsoft Word attachment) to these 216 addresses in late April 2001. When e-mails could refer to multiple Web sites, the respondent was asked to complete the questionnaire by providing data for only



387

Table 2 Sample Design and Response Rates Sample Hotel Category A B C Total

Response Rates

n

%

n

%

67 84 65 216

31 39 30 100

13 38 42 93

19 45 64 43.05

one of the Web sites/properties for which he or she had information to avoid confusion and errors in questionnaire completion. A follow up e-mail was also sent to those that had not responded after 2 weeks to increase the response rate. Overall, a final figure of 93 usable responses was gathered giving a response rate of 43.05%. Questionnaire Design

A structured questionnaire targeting the owners/managers of A-, B-, and Ccategory hotels in Greece that had a Web site was developed. The questionnaire was a two-page long, self-administered instrument, which would not take respondents more than 15 minutes to fill in. All questions were closed ended. The first section of the questionnaire identified three important hotel characteristics: (a) hotel size (number of rooms), (b) hotel category (A, B, or C), and (c) management arrangement (independently, chain, or franchise managed hotel). The second section was designed to gather information on whether the hotel Web site was used for practices in any of the five identified marketing dimensions (Table 1). Respondents had to indicate with a yes or no answer whether their Web site was designed for such activities. In the last section, respondents had to provide information regarding the five statistical data required for benchmarking the Internet marketing strategies. Respondents were reassured for the anonymity and confidentiality of the research. ANALYSIS OF THE FINDINGS Profile of the Respondents

The respondents’ profile is summarized in Table 2. Although all the three category hotels were roughly equally represented in the sample design (31%, 39%, and 30% of the sample accounted for A-, B-, and C-category hotels, respectively), response rates among hotel categories differed significantly. Thus, the majority of respondents (64%) were from C-category hotels, whereas A-category hotels were a minority (19%). In terms of hotel size (Table 3), large hotels are the least represented (22% of respondents had more than 61 rooms), which is not surprising when considering that generally A-category hotels in Greece (which are a small proportion of the respondents) tend to be of a large size (National Statistical System of Greece, 2000). Regarding management arrangement, a great majority of respondents (75%) are independently managed, whereas only 24% of respon-


388


Table 3 Management Arrangement and Size of Operations of Respondents A Management Arrangement Independently managed Hotel chain managed Franchised Management contract Total

B

n

%

n

%

5 7 1 0 13

38 54 8

24 14 0 0 38

63 37 0 0 100

100 A

Hotel Size 1 to 30 rooms 31 to 60 rooms 61+ rooms Total

C

n 41 1 0 0 42

B

Total %

n

%

98 2 0 0 100

70 22 1 0 93

75 24 1 0 100

C

n

%

n

%

2 4 7 13

15 31 54 100

5 22 11 38

13 58 29 100

n 29 11 2 42

Total %

n

%

69 26 5 100

36 37 20 93

39 40 22 100

dents are managed by a hotel chain. Only one hotel reported to be under a franchise management agreement. These findings are not unexpected when considering that a great majority of hotels in Greece are independent and/or family-run businesses and that hotel chains and/or franchise operations are very limited in the hotel industry (National Statistical Service of Greece, 2000). Table 3 illustrates that A- and B-category respondents are more likely to be large hotel properties (in terms of room numbers) and managed by a hotel chain than C-category hotels, which again is similar to the profile of the Greek hotel industry (National Statistical System of Greece, 2000). Thus, respondents are argued to be a reasonably representative sample of the hotel population in Greece. Hotel Internet Marketing Strategies: Composition of the Internet Marketing Mix

The overall degrees of achievement of the surveyed hotels along the dimensions of the Internet marketing mix are shown in Figure 2. Overall, hotels make very limited use of Internet tools in terms of the number of the dimensions in their Internet marketing mix as well as in terms of the transformation degree of each dimension. Hotels focus the use of their Web sites on having a presence in the information and distribution virtual spaces. Indeed, all of the respondents (100%) used the Internet in the product and place dimension, whereas less than one half of the respondents are using the Internet tools to innovate the rest of their Internet marketing mix dimensions (Figure 2). Customer relations is the dimension that attracted the fewest hotels. The transformation degree of each dimension is less than 50%, meaning that hotels are not even implementing one half of the possible features in each dimension. However, not all dimensions share the same degree of transformation. Price accounts for the highest degree of transformation, whereas customer relations is



389

Figure 2 Dimensions of Hotels’ Internet Marketing Mix and Their Degree of Transformation

Transformation degree of dimension

% of hotels with a transformed dimension

20

40

60

80

100

%

0

Product Place Customer Relations

Promotion Price

the least transformed dimension (Figure 2). So, most hotels follow a strategy that primarily aims to increase their distribution channels and visibility in the virtual distribution space. Figure 3 illustrates the structure of Internet marketing mix from a different perspective by illustrating the percentage of hotels that have transformed two, three, four, and five dimensions of their Internet marketing mix. Most of the respondents have developed an Internet marketing mix along two or three dimensions. Despite the fact that 7% of hotels have a five-dimensional Internet marketing mix, it should be noted that the transformation degree of each dimension is relatively very low. Table 1 provides more details regarding the structure of the hotels’ Internet marketing mix. Few hotels are rethinking their business models to take advantage of the unique interactive and connective capabilities of the Internet. Most hotels are stuck in the first stage of Internet exploitation as they focus on publishing hotels’ rooms/services and price information, on heavily advertising and promoting their Web sites on the VDS, while often offering sales promotions to attract bookings. Few have actually moved onto the next stage of conducting ecommerce with only about 16% providing online reservations and secure payment methods. All hotels, however, provided the option of an e-mail reservation


390


Figure 3 Number of Dimensions Composing the Internet Marketing Mix

2 Dimensions 3 Dimensions 4 Dimensions 5 Dimensions 0

20

40

60

80

100

% of hotels

request. Even fewer have actually tried to shift gears into the third and most interesting stage of Internet exploitation: business transformation in cyberspace. Less than 10% of the respondents allowed for dynamic pricing or online negotiation of their room and price inventories. Moreover, the transformation effects of the new interactive capabilities of the Internet have been limited, because few hotels allow customization of their products, promotions, and Web site based on their customers’ profile as well as few hotel Web sites identify and reward their regular customers. Few hotels also allowed for the formation of cybercommunities among customers, and very few were innovative in stimulating intracommunity interactions. Benchmarking Hotel Internet Marketing Strategies

The DEA methodology was used to benchmark the hotels’ Internet marketing strategies. The use of the Internet is considered as a system whereby multiple inputs (the Internet marketing mix dimensions) are managed to produce multiple outputs. Figure 1 illustrates the development of the DEA model. The DEA Internet marketing efficiency score was calculated assuming output maximization rather than input minimization, because the primary objective of Internet marketing strategies is market growth and expansion in contrast to the cost reduction that input minimization strategies aim for. The model also assumed increasing returns to scale to account for the synergy between dimensions, because the impact of a sum of dimensions is greater than the impact derived when adding up the impact of each dimension individually. However, the DEA was conducted for 60 out of the 93 respondents, as some respondents did not provide all required statistical data. However, in calculating the DEA efficiency score, the number of annual requests highly correlated with the annual number of Web site visits (0.71 Pearson correlation, a = 0.05, meaning that only one metric could be included in



391

Low sophistication

Low & high sophistication

Table 4 DEA Market Efficiency Score per Internet Marketing Mix Cluster Efficiency score (%) Number of hotels 100 5 90 – 99 1 2 80 - 89 1 70 - 79 3 60 - 69 1 Average score: 79.8% Efficiency score (%) Number of hotels 100 1 90 – 99 0 3 80 - 89 4 70 - 79 7 60 - 69 10 40 - 59 2 Average score: 77.5%

Efficiency score (%) Number of hotels 100 8 90 – 99 1 1 80 - 89 6 70 - 79 2 60 - 69 5 Average score: 83.1% Efficiency score (%) Number of hotels 100 0 90 – 99 0 4 80 - 89 0 70 - 79 3 60 - 69 0

Two & Three dimensions

Four & Five dimensions

Average score: 71.3%

the DEA model (Avkiran, 1999). Thus, the DEA score was calculated again by excluding the number of Web site visits from the DEA outputs. To investigate the effectiveness of the sophistication of Internet marketing strategies, the following steps were conducted: Four categories of Internet marketing strategies were identified based on a two-dimensional matrix; hotels were classified in each of the four categories; and hotels’ marketing DEA efficiency scores in each category were identified (Table 4). In the matrix, the horizontal axis measures the number of dimensions composing the Internet marketing mix. As no hotel reported use of only one dimension, hotels were divided into two segments: those that have transformed two and three Internet marketing mix dimensions and those that have transformed four and five. The vertical axis takes into account the degree of sophistication of the Internet marketing mix dimensions. As the aspects in each dimension are divided into low and high sophistication, the cut-off point of the vertical axis distinguishes between hotels reporting use of features of low sophistication only and hotels with low and high sophistication features in their Internet marketing mix. The greatest number of efficient hotels (eight hotels with a 100% efficiency score) is found on Segment 1 at the right top corner, (i.e., four & five dimensions and low and high sophistication). Less efficient hotels (five hotels) are found in Segment 2 that represents the same level of sophistication but a smaller Internet marketing mix (two & three dimensions). Hotels that have not extended their Internet marketing mix to more than three dimensions and have not transformed them into highly sophisticated aspects (i.e., hotels on Segment 3) indicated only one efficient unit. Very few hotels (three hotels) are found to have a highly extended but low transformed Internet marketing mix (Segment 4), and they are all inefficient. Thus, it becomes evident that the more extended the Internet marketing mix is and the higher its sophistication degree is, the greater the perfor-


392


mance of the hotel Internet marketing strategy. By looking at the average efficient score in each segment the same conclusion is derived. Hotels in the right top corner have a higher average efficient score that those with less extended and sophisticated Internet marketing mix. The Impact of Demographic Variables on Internet Marketing Strategies

Further statistical tests were conducted to investigate whether DEA Internet marketing efficiency scores were significantly affected by the hotel size, management arrangement, and/or hotel category of respondents (Table 5). A- and B-category hotels were included into one group (because only 13 A-category hotels existed, therefore, this number is small for conducting statistical tests), and a t test (df = 58, p = 0.001) revealed that A- and B-category hotels had a significantly higher DEA score than C-category hotels. A t test (df = 8, p = .013) also indicated that hotel chain managed hotels had also a significantly higher DEA score than independently managed hotels. As concerns hotel size, an analysis of variance (ANOVA, df = 59, p = .036) and post hoc Scheffé tests revealed that hotels that had more than 61 rooms had a significant higher DEA score than hotels with 1 to 30 rooms. Overall, it was found that hotel size, category, and management arrangement had a significant effect on the DEA Internet marketing efficiency score. Having identified an effect of the demographic variables on the DEA score, the next step is to investigate why such differences exist among different types of hotels. Unfortunately, because the size of groups was very small (e.g., 13 Acategory hotels), it was not possible to conduct DEA scores for each group separately and investigate differences. A DEA analysis of five inputs and four outputs requires a minimum number of 40 hotels [i.e., 2 x (5 + 4)] to effectively discriminate amongst hotels (Avkiran, 1999). However, because the number of dimensions and the sophistication of Internet marketing strategies were found to affect DEA scores (Table 4), statistical tests were conducted to investigate whether hotel size, category, and management arrangement had any direct effect on the these two issues of the Internet marketing strategies and therefore an indirect effect on DEA scores. Table 6 summarizes the results of these tests. Hotel category and hotel size had a significant effect on the number of dimensions of the Internet marketing mix and its sophistication as revealed from t tests and Pearson chi-square tests. Specifically, A- and B-category hotels had a significantly greater number of dimensions of their Internet marketing mix transformed (t test, df = 58, p = .011) than C-category hotels as well because the former tend to use significantly (Pearson χ2, df = 1, p = .021) more low and high transformation aspects in their Internet marketing mix than the latter. The ANOVA (df = 59, F = 3.94, p = .028), post hoc Scheffé and Pearson chi-square tests (df = 2, p = .036) also revealed that hotels with more than 61 rooms outperformed hotels having between 1 and 30 rooms in the number of dimensions transformed and their sophistication. Overall, this meant that differences in DEA scores among hotels of different size and category can be attributed to the fact that A- and B-category hotels as well as hotels of more than 61 rooms better performed in the dimensions



Table 5 Impact of Demographics on DEA Scores Hotel Category

Average DEA score Standard deviation Statistical tests

Size of Hotel

A&B Categories (26)

C Category (34)

1 to 30 Rooms (21)

31 to 60 Rooms (20)

88.5 18.7

75.7 18.5

75.8* 18.9

84.5 18.48

t test: df = 58, p = .001

Management Arrangement 61+ Independently Rooms (19) Managed (37) 86.3* 15.24

ANOVA: df = 59, F = 3.26, p = .036,

78.4 19.4

Hotel Chain (23) 87.4 13.93

t test: df = 58, p = .013

*Multiple comparisons – Scheffé Note: DEA: data envelopment analysis. The 60 hotels of which the DEA scores were calculated are included in the analysis (the franchise managed hotel is not included in these 60 hotels). *Significant at the .05 level.

393

394


Table 6 Impact of Demographics on Internet Marketing Dimensions and Sophistication Hotel Category A&B Categories (26) Structure of Internet marketing strategies (number of dimensions) Average number of dimensions Standard deviation Statistical tests Sophistication of Internet marketing strategies (figures represent number of hotels) Low sophistication Low and high sophistication Statistical tests

C Category (34)

3.93 2.27 0.91 0.63 t test: df = 58, p = .011

9 18 17 16 Pearson χ2: df = 1, p = .021

Size of Hotel 1 to 30 Rooms (21)

31 to 60 Rooms (20)

Management Arrangement 61+ Independently Rooms (19) Managed (37)

2.30* 3.62 3.81* 0.80 0.93 1.04 ANOVA: df = 59, F = 3.94, p = .028 *Multiple comparisons – Scheffé

17 4

7 3 13 16 2 Pearson χ : df = 2, p = .036

Hotel Chain (23)

3.49 4.12 1.25 1.13 t test: df = 58, p = .03

16 11 21 12 2 Pearson χ : df =1, p = .056

Note: The 60 hotels of which the DEA scores were calculated are included in the analysis (the franchise managed hotel is not included in these 60 hotels). *Significant at the .05 level.


395

and the sophistication of the Internet marketing mix dimensions than C-category hotels and small-size hotels respectively. On the other hand, as concerns the impact of management arrangement, although a t test (df = 58, p = .03) revealed that hotel chain managed hotels had a significantly greater number of dimensions transformed than independently managed hotels, a Pearson chi-square test (df = 1, p = .056) did not reveal any significant impact of management arrangement on the sophistication of the Internet marketing mix. In this vein, the differences in DEA scores among hotel chain and independently managed hotels can only be attributed to the number of dimensions that these two groups use and not to the sophistication of their Internet marketing mix. However, because hotel chain managed hotels achieved a significantly higher DEA Internet marketing score than independently managed hotels that did better in both Internet marketing strategies dimensions, this may also illustrate that other factors as well significantly contributed to Internet marketing effectiveness. Indeed, Internet marketing strategies of chain hotels also benefit from Internet reservations forwarded from their corporate offices, Internet reservations attributed to the online promotion, advertising and distribution efforts of their hotel chain, and the brand name of their hotel chain. In other words, in considering the effectiveness of Internet marketing strategies, chain hotels also benefit from the synergies and complementarities of their hotel chain strategy and practices, which, in turn, raises an issue regarding the power of hotel brands on the Internet and how independent hoteliers can address it. DISCUSSION OF FINDINGS AND SUGGESTIONS

Overall, it was found that hotels in Greece use their Internet network and interactive capabilities in a limited fashion. Most hotels concentrate their efforts in exploiting the Internet for disseminating information and receiving reservation requests through e-mail, whereas very few are using the Internet for more enhanced and sophisticated activities. However, these findings are not surprising because they are similar to those reported in Azzone, Blanchi, and Noel’s, (2000) study. This study confirmed their conclusions regarding Web site development. Azzone et al. (2000) specifically argued that Web-sites development follows an evolutionary path from a communication (i.e., dissemination of information from business to consumers) to a transaction phase (i.e., online purchases) during which practices evolve from monodirectional simple communications to bidirectional interactive communications. The DEA benchmarking of Internet marketing strategies revealed that hotels with more extended and sophisticated Internet marketing strategies outperform those that exploit the Internet less. In other words, hotels that simply create a Web site and provide an e-mail for reservation requests are very unlikely to encapsulate all benefits that the Internet promises. Such a conclusion is compatible with Sigala’s (2002) findings, which revealed that the ICT productivity paradox (i.e., the fact that the impact of ICT on performance is elusive) is explained when the type and the level of the sophistication of ICT use are taken into account.


396


However, this does not mean that more sophisticated Internet strategies are always more effective. Special attention is also required for hotels that were found to be inefficient in Table 4. Hotels in Segment 1 account for the most efficient hotels, as they had a higher average efficiency score and the greater number of efficient units. However, the efficiency score of the five most inefficient units in Segment 1 is relatively very low (between 60 and 69). The breakdown of the inefficiency scores of hotels in Segment 2 (the same sophistication but with a less extended marketing mix) indicates a smaller number (one instead of five units) of very low inefficient units. The DEA analysis also found that the hotels in Segment 1 accounted for the greatest slack (unused) of the input, namely customer relations. Thus, the extension and sophistication of an Internet marketing mix may lead to greater results, but it also introduces complexity, which may lead to higher inefficiencies, underused and slack resources, if coordinated and planned managerial and organizational approaches are not implemented to manage it. Having transformed fewer marketing dimensions, hotels in Segment 2 face a less complicated strategy model and can effectively cope the simpler managerial issues with their existing organizational and management structure. The complexity issue is also very important when deciding on the design and implementation of Internet marketing strategies. Efficient units in Segments 2 and 1 differ in that hotels in Segment 2 did not claim any use of the customer relations dimension. However, hotels in Segment 1 that did claim to use this dimension were found to be either efficient or inefficient with very high slack customer relations resources. Thus, inefficient hotels in Segment 1 will have to either enhance and coordinate the use of this resource with the other resources or divest it and follow the practices of hotels in Segment 2. In the same vein, if hotels in Segment 2 decide to enhance their customer relations dimension, they should take into account the complexities and enhanced responsibilities that this entails to avoid any inefficiencies. On the other hand, hotels wishing to follow a simpler Internet marketing strategy could follow the strategy of the efficient hotels found in Segment 3 by designing a Web site for information provision with e-mail communication. Finally, significant differences in DEA Internet marketing scores were found among hotels of different sizes, management arrangements, and categories. As hotel size and category were found to significantly affect the number of Internet marketing dimensions and their sophistication, it was concluded that the greater DEA Internet marketing efficiency scores of A- and B-category hotels as well as of hotels with more than 61 rooms can be significantly attributed to their more enhanced Internet marketing strategies. On the other hand, although hotel chain managed hotels had a significantly greater number of transformed dimensions than independently managed hotels, the former did not significantly differ in the sophistication of their Internet marketing strategies from the latter. Because of that, differences in DEA scores among hotel chain managed and independently managed hotels were also attributed to the synergies and complementarities that chain hotels could gain from their hotel chain and brand practices. Thus, in designing their Internet marketing strategies, chain hotels should consider, align, and coordinate their practices with those of their corporate strategy. In addition,



397

any competitive analysis in the hotel sector should also consider the strategic benefits that participation in a hotel chain provides (e.g., brand name, multichannel strategies). Differences in the development of Internet marketing strategies between hotels of different size, category, and management arrangement are also not surprising. Siguaw et al. (2000) found that the adoption and use of information technologies significantly depended on the demographic profile of the hotels that they had examined. In addition, small, lower category and independently managed hotels lack the appropriate skills and resources to fully exploit Internet technologies. Currently, there are governmental initiatives (e.g., seminars) and financial subsidies provided to small and medium tourism and hospitality enterprises for supporting their use of technologies. Moreover, the type of target market and the overall strategic orientation of a hotel can also affect the development and effectiveness of its Internet marketing strategy. Thus, the way that hotels should decide and develop their overall strategies, develop and position their product, and then align and coordinate their Internet strategies is a more complex question that requires more comprehensive competitive and environmental analyses. This study provided evidence of the current effectiveness of aspects and practices of Internet marketing that need to be considered. Moreover, because the findings of this study are crucially dependent on the timing of the research, further research should focus on when, why, and how hotels should change, adapt, and implement their Internet strategies for responding to the continuously changing market dynamics. CONCLUSIONS

The results of the study are interesting because they demonstrate that much more can be done by most hotels in exploiting the Internet. Although the media reports of increasing volumes of online commerce are encouraging, it was found that the majority of hotels are doing little to exploit the unique transformational potential of the Internet. Most of the hotels are simply treating the Internet as a publishing medium, and a lot are simply transferring their existing business models onto the Internet. Moreover, the use of the DEA for benchmarking the hotels’ Internet marketing strategies provided evidence that it is those hotels that have an extended as well as sophisticated Internet marketing mix that gain the most benefits. Differences in DEA Internet marketing efficiency scores were also found among hotels of different size, category, and management arrangement. However, it was found that such differences existed because hotels of greater size and higher category outperformed other hotels in terms of the number and sophistication of their Internet marketing dimensions. Furthermore, although hotel chain managed hotels had a more extended marketing mix than independently managed hotels, the former did not significantly differ in the sophistication of their Internet marketing mix than the latter. However, hotel chain managed hotels achieved significantly higher DEA efficiency scores because of the synergies and complementarities they gained from their hotel chain strategies. Thus, it appears


398


that big and well-known hotel brands may significantly dominate the online market and that they can easily create their Internet marketing strategies. However, this strengthens the pressure on small and independently managed hotels that vitally need to overcome their resource and skills deficiencies to successfully compete in the virtual marketspace. Overall, as competition intensifies and customers become more demanding on customizing products/services to their profile, needs, and on negotiating better prices, hotels should seriously think of transforming their virtual presence and strategy. This coupled with the increased transparency of online operations and practices requires hotels to aim for a better quality and customer relations management in their Internet marketing strategy. In the digital economy, businesses create wealth by applying knowledge, networked human intelligence, and effort to manufacture their services. However, transformation requires good management and design skills that hotels should seek out. Indeed, the DEA analysis of the marketing efficiency scores also indicated that multidimensional and sophisticated Internet marketing mixes require enhanced and coordinated management and organizational approaches, otherwise Internet marketing strategies can lead to great inefficiencies and waste of resources. Therefore, future qualitative research is required to investigate in-depth how hotels are transforming their organizational and management practices and policies to successfully implement and organizationally wide adopt Internet strategies. On the other hand, although hotels that decided to follow a less sophisticated marketing approach were also proved to be as efficient as others, this was true at the time the study was conducted. Research findings also reflect Internet marketing practices of only a part of the hotels that had a Web site at the time the study was conducted. Moreover, as competitive forces on the Internet are continually and rapidly changing, effective strategies and best strategies are also dynamically altering. Thus, continuous and large-scale research is required for identifying best online practices. REFERENCES Alford, P. (2001). eCRM in the travel industry. Travel & Tourism Analyst, 1, 57-76. Al-Shammari, M., & Salimi, A. (1998). Modelling the operating efficiency of banks: A non-parametric methodology. Logistics Information Management, 11(l), 5-17. Angehrn, A. (1997). Designing mature Internet business strategies: The ICDT model. European Management Journal, 15(4), 361-369. Avkiran, K. N. (1999). Productivity analysis in the service sector with DEA. Sydney, Australia: Author. Azzone G., Blanchi, R., & Noel, G. (2000). The company’s Web site: Different configurations, evolutionary path. Management Decision, 38(7), 470-479. Baker, M., Cossey, A., & Sussmann, S. (1999, April 7-9). The WWW and the hotel: Online or off course? Proceedings of the CHME Hospitality Research Conference and EuroCHRIE Conference (pp. 330-345). Guildford, United Kingdom: University of Surrey.



399

Banker, R. D., & Thrall, R. (1992). Estimation of returns to scale using data envelopment analysis. European Journal of Operational Research, 17, 74-84. Brady, M., Saren, M., & Tzokas, N. (1999). The impact of IT on marketing: An evaluation. Management Decision, 37(10), 758-766. Chatzoglou, P., & Soteriou, A. (1999). A DEA framework to assess the efficiency of the software requirements capture and analysis process. Decision Sciences, 30(2), 503531. Chen, S. (2001). Assessing the impact of the Internet on brands. Journal of Brand Management, 8(415), 288-302. Connolly, D., & Sigala, M. (2001). Major trends and IT issues facing the hospitality industry in the new e-economy: A review of the 5th annual Pan-European Hospitality Technology Exhibition and Conference (EURHOTEC 2000). International Journal of Tourism Research, 3(4), 325-327. Cox, F., & Dale, B. G. (2001). Service quality and e-commerce: An exploratory analysis. Managing Service Quality, 11(2), 121-131. Cronin, M. J. (1995). Doing more business on the Internet—How the electronic highway is transforming American companies. New York: Van Nostrand Reinhold. Dussart, C. (2000). Internet: The one-plus-eight “Re-volutions.” European Management Journal, 18(4), 386-397. Evans, P. B., & Wurster, T. S. (1997, September-October). Strategy and the new economics of information. Harvard Business Review, 91, 71-82. Evans, P. B., & Wurster, T. S. (1999, November-December). Getting real about virtual commerce. Harvard Business Review, 93, 85-94. Gilbert, D., Powell-Perry, J., & Widijoso, S. (1999, April 7-9). Hotels, relationships marketing and the Web: Searching for a strategy. Proceedings of the CHME Hospitality Research Conference and EuroCHRIE Conference (pp. 346-363). Guildford, United Kingdom: University of Surrey. Gretzel, U., Yuan, Y. L., & Fesenmaier, D. (2000). Preparing for the new economy: Advertising strategies and change in destination marketing organizations. Journal of Tourism Research, 39, 146-156. Haeckel, S. (1985). Internet marketing. In R. Buzell (Ed.), Marketing in an electronic age (pp. 25-34). Cambridge, MA: Harvard Business School Press. Hagel, J. (1999). Net-gain: Expanding markets through virtual communities. Journal of Interactive Marketing, 13(1), 55-65. Hammer, M., & Mangurian, G. E. (1989, Winter). The changing value of communication technology, Sloan Management Review, 73, 65-71. Hanson, W. (2000). Internet marketing. Cincinnati, OH: South-Western College Publishing. Hardaker, G., & Graham, G. (2001). Wired marketing: Energizing business for ecommerce. New York: Wiley. Jarvela, P., Loikkanen, J., Tinnila, M., & Tuunainen, K. (1999). Business models for electronic commerce in the travel services. Information Technology & Tourism, 2, 185-196. Jun, M., & Cai, S. (2001). The key determinants of Internet banking service quality: A content analysis. International Journal of Bank Marketing, 19(7), 276-291. Kiang, M., Raghu, T., & Shang, K. (2000). Marketing on the Internet—Who can benefit from an online marketing approach? Decision Support Systems, 27, 383-393.


400


Kozinets, R. V. (1999). E-tribalised marketing? The strategic implications of virtual communities of consumption. European Management Journal, 17(3), 252-264. Kuusela, S., Maisala, C., & Saarinen, L. (1999). E-commerce service index—Framework and methodology (working paper). Helsinki, Finland: Elektronisen Kaupan Instituuti. Leeflang, P. S. H., & Wittink, D. R. (2000). Building models for marketing decisions: Past, present and future. International Journal of Research in Marketing, 17, 105-126. Liu, C., & Arnett, K. (2000). Exploring the factors associated with Website success in the context of electronic commerce. Information & Management, 38, 23-33. Mahajan, V., & Venkatesh, R. (2000). Marketing modelling for e-business. International Journal of Research in Marketing, 17, 215-225. Murphy, J. (1996, June). Hotel management and marketing on the Internet. Cornell Hotel & Restaurant Administration Quarterly, 37(3), 70-81. Murphy, J., Hofacker, C., & Bennett, M. (2001, February). Website-generated marketresearch data. Tracing the tracks left behind by visitors. Cornell Hotel & Restaurant Administration Quarterly, 42(1), 82-91. National Statistical Service of Greece. (2000, August 2-8). Tourism movement (special information report). Athens, Greece: Author. O’Connor, P. (1999). Electronic information distribution in tourism and hospitality. London: CABI Publishing. O’Connor, P. (2002). An analysis of the pricing strategies of the international hotel chains. In K. Wober, A. Frew, & M. Hitz (Eds.), Information and communication technologies in tourism 2002 (pp. 285-293). New York: Springer-Verlag O’Connor, P., & Horan, P. (1999). An analysis of web reservation facilities in the top 50 international hotel chains. International Journal of Hospitality Information Technology, 1(1), 77-85. Pine, B. (1993). Mass customization—The new frontier in business competition. Boston: Harvard Business School Press. Procaccino, D., & Miller, F. R. R. (1999). Tourism on the WWW: A comparison of Web sites of United States and French-based businesses. Information Technology & Tourism, 2, 173-183. Rowley, J. (2001). Remodelling marketing communications in an Internet environment. Internet Research: Electronic Networking Applications & Policy, 11(3), 203-212. Schlosser, A. E., Shavitt, S., & Kanfer, A. (1999). Survey of Internet users’attitudes toward Internet advertising. Journal of Interactive Marketing, 13(3), 34-71. Sigala, M. (2000, December 14-17). Empowering Greek hotels through the use through online Internet marketing strategies. Paper presented at International Scientific Conference in Tourism, University of Aegean, Business School, Chios, Greece. Sigala, M. (2002). Investigating the ICT productivity paradox: Evidence from the UK hotel sector. In K. Wober, A. Frew, & M. Hitz (Eds.), Information and communication technologies in tourism 2002 (pp. 417-426). New York: Springer-Verlag. Sigala, M., Airey, D., Jones, P., & Lockwood, A. (2000). The adoption and diffusion of multimedia technologies in the tourism and hospitality sectors. In D. Fesenmaier, S. Klein, & D. Buhalis (Eds.), Information and communication technologies in tourism 2000 (pp. 397-407). New York: Springer-Verlag. Sigala, M., Lockwood, A., & Jones, P. (2001). Strategic implementation and IT: Gaining competitive advantage from the hotel reservation process. International Journal of Contemporary Hospitality Management, 17(3), 364-371.



401

Siguaw, A., & Enz, C. (1999, October). Best practices in information technology. Cornell Hotel & Restaurant Administration Quarterly, 40, 58-71. Siguaw, J. A., Enz, C. A., & Namasivayam, K. (2000). Adoption of information technology in U.S. hotels: Strategically driven objectives. Journal of Travel Research, 39, 192-201. Turban, E., King, D., Lee, J., Warkentin, M., & Chung, H. M. (2002). Electronic commerce 2002: A managerial approach. Upper Saddle River, NJ: Pearson Education. Van Hoof, H., Ruys, H., & Combrink, T. E. (1999). The use of the Internet in the Queensland accommodation industry. Australian Journal of Hospitality Management, 6(1), 11-24. Van Hoof, H., Ruys, H. F., & Combrink, T. E. (2000). Global hoteliers and the Internet: Use and perceptions. International Journal of Hospitality Information Technology, 1(1), 45-61. Walsh, J., & Godfrey, S. (2000). The Internet: A new era in customer service. European Management Journal, 18(1), 85-92. Weeks, P., & Crouch, I. (1999). Sites for sore eyes: An analysis of Australian tourism and hospitality websites. Information Technology & Tourism, 2, 153-172. Wen, H. J., Chen, H. G., & Hwang, H. G. (2001). E-commerce web site design: Strategies and models. Information Management & Computer Security, 9(1), 5-12. Werbach, K. (2000). Syndication: The emerging model for business in the Internet era. Harvard Business Review, 78(3), 85-93. Werthner, H., & Klein, S. (1999). Information technology and tourism: A challenging relationship. Wien, Austria: Springer-Verlag Yuan, Y. L., & Fesenmaier, D. R. (2000). Preparing for the new tourism economy: The use of the Internet and Intranet in American convention and visitor bureaus. Information Technology & Tourism, 3, 71-85. Zott, C., Amit, R., & Donlevy, J. (2000). Strategies for value creation in e-commerce: Best practices in Europe. European Management Journal, 18(5), 463-475. Zuboff, S. (1988). In the age of the smart machine. London: Heinemann.

Submitted February 4, 2002 First Revision Submitted April 17, 2002 Final Revision Submitted September 21, 2002 Accepted September 30, 2002 Refereed Anonymously Marianna Sigala, Ph.D. (e-mail: [email protected]) is a lecturer and Assistant Director of Research at The Scottish Hotel School, University of Strathclyde (Glasgow, Scotland). She is also the co-chair of the Euro-CHRIE Special Interest Group in IT in Hospitality and a member of the Executive Board of Euro-CHRIE and IFITT.
