Theoretical and Empirical Considerations of Guests’ Perceptions of Biometric Systems in Hotels

Abstract

This study extended the technology acceptance model (TAM) by applying it to the context of biometric systems in the hotel industry and by introducing a consumer-oriented construct, perceived innovativeness, as an antecedent of perceived ease of use. Using data collected from hotel guests who traveled during a 12-month period prior to the study, the model explained 79% of the variability in guests’ intentions to use biometric systems in hotels. The results showed that the TAM is an appropriate theoretical framework for the examination of adoption of biometrics in hotels, and that hotel guests are ready to adopt biometric systems, especially if they are perceived as useful.

Keywords

biometrics technology acceptance model perceived innovativeness hotel industry

Introduction

The past several years have been rather turbulent for the hotel industry, especially in terms of technology adoption (Buhalis & Law, 2008). In the beginning of the current decade, hotels adopted a passive role vis-à-vis technology (Lumsden & Beldona, 2006), which allowed for radical transformations (i.e., disintermediation, reintermediation) to occur in their value chains (Carroll & Siguaw, 2003). Later, hotels became involved more actively in technology adoption and found ways to encourage their guests to use technologies such as websites, reservation, recommendation, and in-room entertainment systems (Law & Jogaratnam, 2005). Recently, despite a slower pace of technology adoption relative to other industries (J. Kim, 2009), the hotel industry has become open to novel technologies (i.e., RFID keycards—“VingCard and Aloft Hotels,” 2010; touch screen door locks—“Marriott Secures Prestige,” 2010; virtual meeting systems—“Starwood Hotels & Resorts Goes Live,” 2010), as their benefits are increasingly evident, especially in terms of cost reduction, operational efficiency, and overall value to guests (Huh, Kim, & Law, 2009). However, another novel technology, biometrics, has yet to be adopted enthusiastically by both guests and hotel companies.

The history of biometrics in the hotel industry is relatively short, since, traditionally, hotels have generally been slow to offer them (Adams, 2002). Some of the pioneering applications used in hotels included biometric in-room safes, and iris scanning and face recognition systems designed to allow staff and guests access to certain areas (Adams, 2002; Simon, 2004). To date, except for a small number of properties, hotels have not offered biometric systems, arguably because of prohibitive factors such as reliability, lack of standards (Vijan, 2004), perceived intrusiveness (Singh & Kasavana, 2005), and privacy concerns (Adler, 2008; J. Kim, Brewer, & Bernhard, 2008; Tsai, 2007). In spite of such barriers, it is generally agreed that biometric systems could add value to guests’ hotel stay experiences (Murphy & Rottet, 2009), as they are viewed as superior to traditional identification and access technologies. For hotels, biometric systems appear to be promising, as they can reduce costs and fraud, and increase accuracy in transaction processing (Murphy & Rottet, 2009), while offering users security and convenience (Ives, Du, Etter, & Welch, 2005; Jones, Williams, Hillier, & Comfort, 2007).

As most of today’s hotel guests seek convenience (Law & Chen, 2000), biometrics appears to be one of the most promising technologies of the future, allowing guests to check in/out, access guest areas, and make payments with unprecedented convenience and speed. However, despite a recent decrease in the overall price of biometric hardware (i.e., basic fingerprint systems cost as low as $300, Biometrics, 2010; whereas sophisticated hand and face recognition [three-dimensional] systems cost beyond $5,000 per unit, J. Kim, 2009; Kirby, 2008) at a hotel’s scale, biometric systems represent considerable investments (J. Kim, 2009). In the absence of strong adoption from guests, the return on such investments may not be substantial for hotels. In this context, understanding the circumstances under which guests adopt biometric systems may provide hotels with insight into the types of systems to be offered and the characteristics that biometric systems should have to be adopted enthusiastically by guests. Thus, this study examines whether the extended theoretical base provided by the technology acceptance model (TAM; Davis, 1986, 1989), augmented with the construct of perceived innovativeness toward information technology, serves as an appropriate foundation for the study of hotel guests’ perceptions of biometric systems in hotels. Specifically, this study has three objectives:

examine the factors that influence guests’ attitudes and intentions to use biometric systems in hotels,

determine the role of guests’ perceived innovativeness toward technology on their perceptions of biometric systems in hotels, and

formulate recommendations for the hotel industry about the potential use of biometric systems in hotels.

Review of Literature and Hypotheses

Biometrics: Definition and Applications

Biometric systems are technologies that use “measurable physical, biological, or behavioral characteristics that can be processed to establish identification, to perform identity verification, or to recognize a person through automation” (Ives et al. 2005, p. 472). Biometric systems require two operational dimensions: (a) enrollment, in which biometric data are obtained and linked with a person’s identity and (b) authentication or recognition, in which new biometric data are compared with the stored data (Langenderfer & Linnhoff, 2005). A typical biometric system consists of a reader/scanner, a software application that converts a scanned human characteristic into biometric data (i.e., set of features), and a database that is used for storage and, later, for authentication (Jones et al., 2007). First, users register a physical (i.e., fingerprint, iris image) or behavioral (i.e., signature handwriting pattern) characteristic with the database and a template is created, which is known as enrollment. Then, the system compares current biometric data with data stored in the database. Authentication is based on the similarity of data from any current reading with the template stored in the database, which, theoretically, should match (Jones et al., 2007).

In contrast to traditional (i.e., password based) authentication systems, where a perfect match between two sets of characters (features) is necessary for authentication, biometric systems rarely encounter two samples of a user’s biometric that generate the same set of features (Jain & Ross, 2008). This is because even the same user providing the same biometric is rarely able to generate exactly the same set of features. Moreover, many technical (i.e., improper lighting conditions, faulty sensors) or medical factors (i.e., respiratory issues affecting voice) may exacerbate the differences between sets of features belonging to the same user (Jain & Ross, 2008). The sets of features extracted from users’ biometrics are compared by the system to calculate a “similarity score,” which is further compared with a preset system threshold to generate a decision to accept/reject a user (Chollet, Dorizzi, & Petrovska-Delacretaz, 2009). A similarity score matching sets of features for the same user is known as a “genuine score,” whereas a score matching sets of features for different users is known as an “impostor score” (Jain & Ross, 2008). If an impostor score exceeds the threshold, the system produces a false accept (an incorrect user is accepted), whereas if a genuine score falls below the threshold, the system produces a false reject (a correct user is rejected; Chollet et al., 2009). Thus, as a measure of their performance, biometric systems are characterized by false acceptance rates (FARs; probability that the system will accept an incorrect user) and false rejection rates (FRRs; probability that the system will reject a correct user; Jain & Ross, 2008). Current biometric systems vary in their performance (i.e., FRR, FAR), based on the characteristics of the biometric being used and the level of development of the technology used to capture biometric data. For example, systems based on iris scanning are very accurate (i.e., FAR rates below 1%), whereas systems based on face (especially bidimensional) and hand recognition are relatively less accurate (i.e., FRR of approximately 10% and 4%, respectively; Jain & Ross, 2008; J. Kim, 2009).

An inventory of biometric systems includes fingerprinting, face and voice recognition, hand geometry, handwriting pattern recognition, and iris and retinal scanning (Ives et al., 2005). Biometric systems use characteristics of humans that hardly change (Koltzsch, 2007). Thus, they are considered more reliable than the traditional recognition and identification systems (Jain, 2007), even though they may occasionally lead to erroneous acceptance/rejection decisions (Jones et al., 2007). A small number of historically common and popular biometric identification methodologies (i.e., fingerprinting, face recognition; Pons & Polak, 2008) remain the most popular today (Bolle, Connell, Pakanti, Ratha, & Senior, 2004), although other systems, such as hand/palm geometry, are becoming increasingly popular.

The academic literature on biometric systems is mostly limited to discussing various technical aspects of biometric systems design and application, such as authentication accuracy (Araujo, Sucupira, Lizarraga, Ling, & Yabu-Uti, 2005), technical and legal challenges in implementation (Chandra & Calderon, 2005; Deane, Barrelle, Henderson, & Mahar, 1995), and ethical issues related to biometric identification (Alterman, 2003). To date, only a few authors have discussed biometric systems from a consumer adoption perspective, both in mainstream business (see Giarimi & Magnusson, 2002; James, Pirim, Boswell, Riethel, & Barkhi, 2006; Langenderfer & Linnhoff, 2005; Laux, 2007) and hospitality (J. Kim, 2009; Murphy & Rottet, 2009).

Research on the adoption of biometrics presents conflicting findings. For example, investigating Swedish students’ perceptions of biometric systems, Giarimi and Magnusson (2002) found that a large majority had a positive attitude toward biometric systems and would use them at some point in the future. James et al. (2006) found that users’ intentions to use biometric systems are influenced by perceived usefulness, perceived ease of use, and perceived invasiveness of biometric systems. However, in a more recent study, Pons and Polak (2008) warned that consumer emotions such as fear, hesitation, and comfort regarding biometric systems might lead to improper use and result in consequently to substantial operational problems for organizations. It has been eventually agreed that the use of biometrics is driven by two main reasons: (a) the appropriateness of allowing the correct person to rightfully access restricted areas, resources, or products and (b) the convenience of not carrying a card or memorizing a password (Koltzsch, 2007).

Especially since the terrorist acts on September 11, 2001 and the London bombings in July 2005, an increased awareness of security has driven a proliferation of biometrics in various contexts, including nonbusiness (i.e., government, military; Ives et al., 2005) and business (i.e., retail, airlines; Heracleous & Wirtz, 2006; Jones et al., 2007). Recognizing the benefits of biometrics, many organizations experimented with some of these technologies to improve security and attendance control, and to offer more convenience to their employees. For example, because of the sensitive nature of their activities and data, organizations such as Merck, Bristol-Myers Squibb, IBM, and Pfizer, began using hand scanners to improve the accuracy of their employees’ access to sensitive areas and data, while simplifying their access protocols (Agnvall, 2007; Tsai, 2007). Several organizations began using biometrics to improve attendance control while offering convenience to employees. With hand scanning taking less than a second to perform, and with capabilities of linking employee access/attendance data from biometric systems to payroll and other human resource management systems, organizations such as the Hilton Waterfront Beach Resort derived important benefits from using biometric systems by eliminating unethical practices (i.e., employees fraudulently clocking in/out each other; Agnvall, 2007) while providing greater convenience to their employees (i.e., speed, no access card to be carried).

Although biometric technology implementations for employees use have been quite popular, to date, consumer-oriented applications have not enjoyed the same level of popularity. Although consumer-oriented biometric systems have been sporadically adopted in the past, their application has increased in recent years. For example, retailers such as Piggly-Wiggly, General Nutrition Center, and Blockbuster implemented fingerprint scanning and offered their customers increased levels of convenience (Best, 2005; International Biometric Group, 2005; Lucas, 2005). Other applications include check-cashing (Wolfe, 2008) and access systems at sports venues (Den Hartog & Van Munster, 2008).

In the hotel industry, adoption and dissemination of technologies has been relatively complex, as it critically impacts both internal and external components of business processes (Wang & Qualls, 2007). Biometrics made no exception: The hotel industry has been slow in implementing biometrics (K. H. Kim et al., 2008; Murphy & Rottet, 2009). As documented by the literature, to date, biometric systems have been sporadically adopted by hospitality organizations and offered predominantly to staff rather than to guests. An increasing number of hospitality operations, ranging from high-end resort hotels to fast-food restaurants such as McDonald’s, Burger King, and Dunkin’ Donuts, began using biometric systems to improve the accuracy in managing their employees and provide more convenience to their staff (Agnvall, 2007). Upscale properties such as the Venetian Macao Resort Hotel and Hilton Beachfront Beach Resort have offered biometric systems to their staff (Agnvall, 2007), but so far only a few companies have made them available to guests (Jain, 2007; J. Kim, 2009).

Hospitality organizations that offered biometric systems to their guests include Harrah’s Entertainment, Walt Disney World Resort, and the Nine Zero Hotel in Boston (Jain, 2007; J. Kim, 2009; Kirby, 2008; Sturgeon, 2005). After introducing biometric systems based on fingerprint and facial recognition to their employees at their Las Vegas locations and recording substantial savings in key management and replacement costs (Miller, 2005), Harrah’s Entertainment decided to offer guests biometric systems. At the Rio All-Suites Hotel and Casino in Las Vegas, Harrah’s Entertainment offered guests a novel biometric system designed to handle their debit card withdrawals, credit card cash advances, and cash checking based on facial recognition, called the automated cash machine (ACM). After an initial facial scan is performed, guests can access cash by using any ACM without any help from a cashier (Miller, 2005). Another company that seems to be using consumer-oriented biometric systems successfully is the Walt Disney World Resort in Orlando. At Disney, visitors provide their fingerprint along with their tickets, and a system links the ticket with the visitors’ fingerprints for later use (Jain, 2007). According to Jain, Disney’s system can handle approximately 100,000 visitors a day with efficiency and works accurately in a variety of weather conditions. The Nine Zero Hotel in Boston offers two suites equipped with biometric systems based on iris scanning as an alternative to traditional access (i.e., key based) systems. This way, the hotel offers convenience to guests, as enrollment takes approximately 4 minutes, whereas authentication usually takes a matter of seconds to complete (Kirby, 2008).

A few large-scale examples of consumer-oriented applications can be found in the travel industry. In 2004, to build and sustain competitive advantage, Singapore Airlines and Changi Airport initiated the implementation of a biometric system (fully automated seamless travel—FAST) to integrate processes such as airline check-in, preimmigration security checks, and immigration clearance (Heracleous & Wirtz, 2006). In 2008, several major U.S. airports (i.e., Washington DC Dulles, Los Angeles, Miami, Chicago O’Hare) offered travelers the possibility of obtaining cards after undergoing background checks, and fingerprint and iris scans to expedite airport security checks (Craver, 2008). A similar system, called the iris recognition immigration system (IRIS), has been implemented by the U.K. government to allow enrolled travelers to expedite immigration checks at major airports (Jain, 2007). Moreover, Japan uses an immigration-based biometric system based on fingerprinting and face recognition. Perhaps one of the most famous consumer-oriented biometric systems is the U.S. Department of Homeland Security Visitor and Immigrant Status Indicator Technology (US-VISIT) program, which is based on recognition of fingerprint and facial images of visitors to the United States. The system is capable of matching a visitor’s fingerprints and face image against a list of more than 2 million records in a matter of seconds (Jain, 2007).

Although the literature on general technology adoption in the hospitality industry is rich, and despite a few studies published during the past 3 years (e.g., Jackson, 2009; K. H. Kim et al., 2008; Murphy & Rottet, 2009), the literature on the adoption of biometrics in this industry is rather scarce. A review of executives’ opinions about the future of technology in the hotel industry revealed that biometric systems are less likely to be used by hotels in the near future, but they might have a high probability of being largely offered by 2027 (Singh & Kasavana, 2005). Lumsden and Beldona (2006) examined the benefits and drawbacks of biometric systems in hotels and noted that the use of such systems could become more widely accepted by hotels, in areas beyond security.

Examining hotel guests’ intentions to use fingerprint-based door locks, J. Kim (2009) found that such intentions are influenced by guests’ perceptions of system’s usefulness, ease of use, subjective norms, data and property security, and personal characteristics such as their demographic profile and hotel stay behavior. J. Kim et al. (2008) identified two segments of hotel guests with antagonistic views, “advocates” and “opponents” of biometric door locks, and found that convenience, physical security, data security, and personal concerns were important discriminating variables for segmentation purposes. That is, guests who expected superior convenience, physical security, and data security from fingerprint-based door locks could be classified as “advocates,” whereas guests who did not expect these benefits while increasing their personal security concerns could be classified as “opponents” of biometric door locks (J. Kim et al., 2008).

Murphy and Rottet (2009) explored hotel guests’ adoption behaviors in Switzerland and found correlations between different processes (i.e., booking, access, payment) and different biometric technologies. For example, they found that guests prefer to use fingerprint-based systems for payment, confirmation of identity, access, and activation, and they prefer voice/speech recognition–based systems for booking and information requesting. Interestingly, from a list of seven biometric systems that could be used in hotels, the highest ranking was attributed by Swiss hotel guests to iris scanning (Murphy & Rottet, 2009). Recently, Jackson (2009) provided a discussion of earlier predictions of Singh and Kasavana (2005), arguing that the hospitality industry could soon deploy a variety of applications of biometric systems.

Research Model and Hypotheses

In spite of a general slow pace of technology adoption in the hospitality industry, hotels eventually recognized the critical importance of technology, in both consumer settings (Morosan & Jeong, 2008) and employee settings (Huh et al., 2009). Specifically, hotels can use information technologies to improve internal functions (i.e., employee productivity; Law & Jogaratnam, 2005) and their relationships with their guests, while building and sustaining competitive advantages (Connolly, Olsen, & Moore, 1998). Thus, a process as important and multidimensional as the adoption of technology in hospitality generated a strong interest from the research community (Buhalis & Law, 2008). Such an extensive interest was geared toward providing the community with the best theoretical and practical insight into technology adoption in particular industrial contexts. Consequently, the resulting hospitality/tourism industry literature provides ample discussions on technology adoption, both from an institutional and consumer perspective (T. G. Kim, Lee, & Law, 2008). For example, in an earlier study, Wober and Gretzel (2000) discussed adoption of marketing decision support systems in the tourism industry and established links among concepts such as usefulness, ease of use, experience, and attitudes toward information technologies. Later, numerous scholars focused on adoption of technologies such as front office systems (T. G. Kim et al., 2008), reservation systems (H. Y. Lee, Kim, & Lee, 2006), general information technologies (Lam, Cho, & Qu, 2007), and websites (Chung & Law, 2009; Kaplanidou & Vogt, 2006; Morosan & Jeong, 2008).

Generally, technology adoption has been a major topic for many scholars because of its importance in understanding technology diffusion (Oh, Kim, Lee, Shim, & Park, 2009). In this context, a multitude of theoretical frameworks have been used, many of them being variants derived from the same class of attitudinal/behavioral models (i.e., technology acceptance model [TAM], Davis, 1986, 1989; theory of planned behavior [TPB], Ajzen, 1985, 1991). All these models explained adoption behavior by linking individuals’ beliefs to their attitudes and intentions to use new technologies, of which, the TAM seems to stand out as the most influential and valid in both employee and consumer settings (Oh et al., 2009). TAM’s solid theoretical foundation and its ability to parsimoniously link beliefs to behavior made it a simple, yet robust theoretical framework, which led to its wide replication (Schepers & Wetzels, 2007). TAM has been validated in a variety of technological contexts, with samples of students and nonstudents, and in Western cultures and others (Agarwal & Karahanna, 2000; Devaraj, Fan, & Kohli, 2002; Hsu & Lu, 2004; Koufaris, 2002). Moreover, a number of meta-analyses concluded that TAM is very useful model to examine adoption of new technologies (Legris, Ingham, & Collerette, 2003; Ma & Liu, 2004; Schepers & Wetzels, 2007).

The classic TAM was developed by Davis (1986) and has its origins in attitudinal/behavioral models such as the theory of reasoned action (TRA; Fishbein & Ajzen, 1975) and TPB (Ajzen, 1985, 1991). The model parsimoniously explains users’ new technology adoption behavior via four constructs: perceived usefulness, perceived ease of use, attitudes, and intentions to use the new technology. The model theorizes that users would adopt a new technology if that technology is perceived to help them perform a task better (Davis, 1989), referred to as “perceived usefulness.” In addition, users would adopt a new technology if the technology is perceived as easy to use and requires little effort to perform a task, referred to as “perceived ease of use” (Davis, 1989). Both perceived usefulness and ease of use affect users’ attitudes toward new technologies, which in turn, affect their intentions to use the new technologies. Thus, the TAM proposes a direct belief–attitude–intention relationship (Oh et al., 2009).

Despite occasional divergent opinions favoring theoretical models other than the TAM (e.g., Taylor & Todd, 1995), most scholars who examined technology adoption in a variety of contexts extensively employed the TAM during the past 20 years (Oh et al., 2009; Schepers & Wetzels, 2007), giving it an extensive empirical support in mainstream business research (K. H. Kim et al., 2008; G. S. Kim & Park, 2008; Stern, Royne, Stafford, & Bienstock, 2008) and hospitality (Huh et al., 2009). Particularly, in hospitality, Huh et al. (2009), studying three competing models, (i.e., TPB, decomposed theory of planned behavior [DTPB], and TAM) in terms of overall model fit, explanatory power, and path significance, concluded that, among the three models, TAM was the most preferable in predicting employees’ intentions to adopt hotel information systems. To date, there are no known comparisons of competing models that could explain biometric adoption by guests in hospitality. However, two studies focusing on guests’ adoption of fingerprint-based door locks (J. Kim, 2009) and overall biometric system adoption in Swiss hotels (Murphy & Rottet, 2009) used variants of TAM as theoretical foundations. Thus, given its parsimony, robustness, and broad empirical support (H. F. Lin, 2007), the TAM has been used in this study to predict guests’ intentions to use biometric systems in hotels.

The literature on technology adoption recognizes various extensions of TAM to fit various technological contexts (Schepers & Wetzels, 2007), as some scholars argue that TAM, despite its broad validation, needs to be extended in order to provide a more comprehensive understanding of technology adoption. In an effort to understand what else drives consumers’ adoption of technologies, Agarwal and Prasad (1998) using Rogers’s (1983) diffusion of innovation framework, posited that the most immediate influences on an individual’s cognitive assessment of information technology are represented by factors unique to the individual. Rogers found that individuals who are highly innovative are active seekers of new ideas, can cope with higher levels of uncertainty, and develop more positive intentions toward acceptance. Agarwal and Prasad viewed personal innovativeness as expressing the risk-taking propensity that certain individuals possess, and defined it as an individual’s willingness to try out a new technology (Midgley & Dowling, 1978). Thus, in an effort to increase the model’s explanatory power by including personal factors (perceived innovativeness toward information technology), the TAM has been extended in this study by adding a new hypothesized relationship between perceived innovativeness toward technology and perceived ease of use. The conceptual model and its hypothesized relationships are represented in Figure 1.

Figure 1

The Model and Its Hypothesized Relationships

Perceived usefulness is a fundamental component of technology adoption in the hotel industry (Christou & Kassianidis, 2002; Law & Jogaratnam, 2005). The predominant belief is that users will adopt a technology if they perceive it to be useful (Premkumar, Ramamurthy, & Liu, 2008). For hotel guests, usefulness of biometric systems is linked to the extent to which biometric systems help them in performing tasks (i.e., check in/out, access, payment) better than alternative technologies (i.e., keycards). The literature on biometrics provides evidence that biometric systems improve transaction speed (Jones et al., 2007; J. Kim, 2009; Kirby, 2008) and employee recognition and payment (Heracleous & Wirtz, 2006). In the hospitality industry, Murphy and Rottet (2009) emphasized the importance of biometric systems in improving efficiency for hotel guests. Moreover, J. Kim (2009) confirmed that a relationship existed between perceived usefulness and guests’ intentions to use biometric door locks in hotels. Thus, in the light of the existing literature, the following hypothesis has been developed:

Hypothesis 1: There is a positive relationship between guests’ perceived usefulness and their attitudes toward using biometric systems in hotels.

The second fundamental construct of TAM is perceived ease of use. It refers to an individual’s assessment of the amount of effort needed to perform a task using a new technology (Lu, Liu, Yu, & Wang, 2008). Although Davis (1989) put more emphasis on the relationship between perceived usefulness and attitudes than between perceived ease of use and attitudes (Huh et al., 2009), he posited that users would not adopt a new technology unless it is easy to use. In the hospitality industry, the relationship between perceived ease of use and attitudes toward new technologies has been validated in several studies (J. Kim, 2009; K. H. Kim et al., 2008; Morosan & Jeong, 2008). Overall, biometric systems are believed to enhance convenience for users in hospitality (J. Kim, 2009; Lumsden & Beldona, 2006) and beyond (Heracleous & Wirtz, 2006), and their ease of use and transparency are key in adoption by the consumers (Pons & Polak, 2008). Thus, based on the above, the following hypothesis has been developed:

Hypothesis 2: There is a positive relationship between guests’ perceived ease of use and their attitudes toward using biometric systems in hotels.

The literature on TAM provides overwhelming evidence of a strong, positive relationship between perceived ease of use and perceived usefulness (Lu et al., 2008; Ruth, 2000). In the hospitality/tourism industry, this relationship has been validated by Wober and Gretzel (2000) and recently by J. Kim et al. (2008) and Morosan and Jeong (2008). Thus, the following hypothesis has been developed:

Hypothesis 3: There is a positive relationship between guests’ perceived ease of use and perceived usefulness of biometric systems in hotels.

Attitudes refer to an individual’s inclination to display certain responses toward concepts or objects (Vijayasarathy, 2004). Fishbein (1963) generally viewed attitudes toward objects as outcomes of an individual’s beliefs about an object and evaluative responses associated with these beliefs. In the context of TAM, positive attitudes toward new technologies have been validated as antecedents of strong intentions to adopt such technologies (Legris et al., 2003; Shih, 2004). The relationship between the attitudes toward technologies and the intention to use those technologies has been well documented in both mainstream marketing (Walczuch, Lemmink, & Streukens, 2007) and hospitality (Morosan & Jeong, 2008; Wang & Qualls, 2007). Thus, the following hypothesis has been developed:

Hypothesis 4: There is a positive relationship between guests’ attitudes toward biometric systems and their intentions to use biometric systems in hotels.

Despite the widespread acceptance of TAM by scholars, there is a substantial deal of dissent in terms of the final level in the TAM: intentions to use or actual use. Although an objective measure of behavior would be ideal, most TAM studies used intentions as surrogate measures of adoption behavior, grounded in the belief that intentions are, in fact, strong predictors of actual behavior (Legris et al., 2003; Shih, 2004). This assertion has been validated in mainstream marketing (Bruner & Kumar, 2005) and hospitality (Wang & Qualls, 2007). In this study, however, intentions to use rather than actual use have been used, as the offering of biometric systems to hotel guests is still in infancy. Such an approach to measuring the final level in TAM has been popular in studies of adoption of technologies being in early stages (Lu et al., 2008).

Although the literature on technology adoption is rich in applications of TAM, recent studies recognized the deficiency of the classic TAM to capture all aspects of adoption in various industry or application contexts (Moon & Kim, 2001). Most studies extended TAM by adding antecedents to perceived usefulness and ease of use and focusing on the effects of such external variables on TAM’s original constructs (Agarwal & Prasad, 1999; Bruner & Kumar, 2005). Some of these antecedents included constructs related to human and social change processes (Legris et al., 2003; Premkumar et al., 2008), such as playfulness (Chung & Tan, 2004), enjoyment, and trust (Yu, Ha, Choi, & Rho, 2005).

Most studies using extended versions of the TAM in the hotel industry focused on the supply side, by adding organizational factors (i.e., technology climate, technology characteristics, strategic orientation of the organization; Wang & Qualls, 2007), information quality, system quality, service quality, perceived value (J. Kim et al., 2008), and task, career, and organizational fit (Lee et al., 2006). For example, studying the general information technology adoption in hospitality organizations, Wang and Qualls (2007) examined the impact of organizations’ technology capacity and technology characteristics as moderators of the classic TAM constructs. They have also focused on finding the antecedents of perceptions of usefulness and ease of use from internal and external perspectives and challenged the popular assumption that no other factors affected the relationship between perceptions and adoption behavior. However, despite the predominant supply-side orientation of technology adoption research in the hotel industry, a recent study has been found to focus on the demand side by using an extended TAM. For example, studying consumers’ adoption of hotel reservation websites, Morosan and Jeong (2008) found that playfulness can provide additional explanatory power to the adoption of reservation websites and that perceived playfulness was a strong predictor of attitudes toward adopting reservation websites.

Defined as the extent to which an individual is willing to try out any new product (i.e., technology; Midgley & Dowling, 1978), perceived innovativeness is one of traditional factors in the innovation diffusion research (Rogers, 1983) that is believed to influence an individual’s adoption of innovations (Agarwal & Prasad, 1998). Generally, perceived innovativeness measures the extent to which an individual is open to technological innovations (Rogers & Shoemaker, 1971). Since the early 1970s, studies showed that perceived innovativeness had a substantial impact on the practice of marketing, especially on segmentation, allowing marketers to divide consumer populations into groups such as “innovators” and “noninnovators” (Agarwal & Prasad, 1998). The mainstream marketing literature eventually recognized the conceptual and operational distinction between “global” and “domain specific” innovativeness, with tremendous implications for the study of adoption behaviors (Flynn & Goldsmith, 1993). On one hand, global innovativeness has been viewed as a characteristic that humans may possess to a greater or lesser extent (Agarwal & Prasad, 1998). However, it has been argued that global innovativeness had a low prediction power in certain domain-specific adoption contexts (Goldsmith & Hofacker, 1991). On the other hand, domain-specific innovativeness may have a substantial influence on adoption behavior in specific, narrower domains (Goldsmith & Hofacker, 1991). Since this study focused on a narrower context (technology adoption), perceived innovativeness toward information technology has been used in this study.

Earlier operationalizations of domain-specific innovativeness have been based on a “time-of-adoption” methodology, measuring the time elapsed from the introduction of a product until its adoption by the consumers. However, such methodologies have been severely criticized for not being able to accurately measure the underlying construct they were designed to measure, and therefore lacking reliability and validity (Hurt, Joseph, & Cook, 1977). In response, Goldsmith and Hofacker (1991) developed and tested a six-item measurement self-reported scale for the domain-specific innovativeness. This scale has been used extensively and has been adapted to measure innovativeness toward novel domains, such as the information technology.

Perceived innovativeness toward information technology has been used in a variety of contexts, often as an extension of the classic TAM. Karahanna, Straub, and Chervany (1998) argued that, generally, as individuals are more innovative, the less complex their belief sets about the new technologies. Viewing innovativeness as a trait, which is not influenced by environmental or internal variables, Walczuch et al. (2007) found a significant relationship between perceived innovativeness and perceived ease of use of technology by employees of a Belgian multisite financial service provider. Similarly, Lu, Yao, and Yu (2005), studying the adoption of wireless Internet services via mobile technology, found significant relationships between perceived innovativeness toward information technology and perceived ease of use. In their study of adoption and use of Internet technologies by faculty and instructors in their teaching activities, Lewis, Agarwal, and Sambamurthy (2003) confirmed a hypothesized relationship between perceived innovativeness and perceived ease of use.

It is generally expected that individuals with high innovativeness would also be naturally inclined in finding their way around a novel technology. In the context of biometric systems in hotels, it is believed that, given the natural inclination of highly innovative individuals to learn how to use technologies easily, they would easily learn how to use biometric systems. Thus, consistent with previous research (Agarwal & Prasad, 1998; Lu et al., 2005; Walczuch et al., 2007), a relationship between perceived innovativeness toward IT and perceived ease of use of biometric systems is expected. Therefore, the following hypothesis has been developed:

Hypothesis 5: There is a positive relationship between guests’ perceived innovativeness toward information technology and perceive ease of use of biometric systems in hotels.

Method

The measurement scales (Appendix A) have been adapted from the rich literature on TAM (Davis, 1989) and related models (TRA, TPB; Ajzen, 1985, 1991) to fit the context of biometric systems in hotels. To measure perceived usefulness, the scale included four items, measuring the extent to which biometric systems in hotels would enhance the effectiveness of guests’ interactions with the hotel (i.e., check in/out, guest area access, food/beverage payment; S. H. Kim, 2008; Lopez-Nicolas, Molina-Castillo, & Bouwman, 2008), improve the quality of stay (Ahn, Ryu, & Han, 2007), and allow guests to do things better (Lopez-Nicolas et al., 2008). This scale concluded with a fourth item, which was a general statement about the overall usefulness of biometric systems in hotels (Lu et al., 2008). The scale for perceived ease of use included four items, measuring the extent to which learning to use biometric systems in hotels would be easy (Lopez-Nicolas et al., 2008), the interactions between guests and biometric systems would be clear and understandable, and would not require a lot of mental effort (Lu et al., 2008). Similarly, the fourth item on this scale was a general statement about the overall ease of use of biometric systems in hotels (Lopez-Nicolas et al., 2008). Both scales were Likert-type and were anchored in 5 points, with values ranging from 1 = strongly disagree to 5 = strongly agree.

To measure attitudes toward biometric systems in hotels, a 5-point semantic differential scale was used. The anchors were good idea–bad idea (Ahn et al., 2007); wise–foolish (Ahn et al., 2007); beneficial–not beneficial (Y. Lee & Kozar, 2008), and enjoyable–not enjoyable (Y. Lee & Kozar, 2008). The scale for intentions to use biometric systems in hotels was similar to the scales used to measure perceived usefulness and ease of use. The scale included three items, measuring the extent to which the respondents intend to use biometric systems in the future (Shin, 2009), whether it would be one of their favorite technologies to use (Im, Kim, & Han, 2008), and whether the user would recommend the use of biometric systems in hotels to others. A 5-point Likert-type scale with values ranging from 1 = strongly disagree to 5 = strongly agree was used for these items.

In this study, perceived innovativeness toward the specific domain of information technology was used. The scale for perceived innovativeness toward information technology was adapted from the existing literature on domain-specific innovativeness (Goldsmith & Hofacker, 1991; Lu et al., 2008, Walczuch et al., 2007). It included four items measuring the extent to which the respondents were among the first to try out new technologies (Goldsmith & Hofacker, 1991), were interested in experimenting with new technologies as soon as they heard about them (Goldsmith & Hofacker, 1991; Walczuch et al., 2007), liked to experiment with the new technologies (Agarwal & Prasad, 1999), and liked to keep up with the latest technological developments in their areas of interest (Walczuch et al., 2007). All items have been anchored in 5 points, with values ranging from 1 = strongly disagree to 5 = strongly agree. The survey concluded with a section on guests’ travel behavior (i.e., type of hotel used, trip purpose, number of nights spent in a hotel during the past 12 months) and a brief demographic section (i.e., gender, age, marital status, and income) to create a profile of respondents.

An online survey was conducted with respondents who traveled during a period of 12 months prior to the survey. The sample frame of 621 valid email addresses was provided by the administration of a small private university located in a metro area in southwestern United States. The sample frame included email addresses of students enrolled in the MBA program as of the spring semester of 2008. In March 2009, an invitation email to participate in the online survey was sent out, and, after two reminders, a number of 185 respondents completed the survey. Only the respondents who stayed at least one night in a hotel during a period of 12 months prior to the study were kept in the analysis. After removing the records containing heavily missing values, a total of 162 responses remained in the analysis, accounting for a response rate of 26%. The respondents were asked to refer to a scenario (Appendix B), in which they had to imagine being guests in a hotel that offered biometric systems as well as traditional systems (i.e., electronic cards) for guest-area access and payment. The biometric systems offered by the hotel in the scenario were the most commonly discussed systems found in the current literature (i.e., fingerprinting, face recognition, iris scan; Murphy & Rottet, 2009).

Results

Descriptive Analysis

Since a permanent concern in survey research is the possibility that respondents may have different opinions than nonrespondents, a nonresponse bias analysis was conducted by comparing groups of early with late respondents (Ary, Jacobs, & Razavieh, 1996; Connors & Elliot, 1994). The respondents were divided into three groups (early, mid, and late respondents) and an analysis of variance (ANOVA) was conducted to assess if there are any significant differences among these groups. As nonsignificant differences were found, it was concluded that nonresponse bias is not a concern in this study. The results of the ANOVA can be found in Appendix C.

The demographic profile of respondents (Table 1) revealed that most of them had annual household incomes between $50,000 and $79,999, although the sample was almost evenly split among income categories. The proportion of males and females was approximately even, with slightly more females in the sample. Most respondents were relatively young, with the majority being between 25 and 34 years old. A large majority of respondents (57.9%) were single, whereas a total of approximately 40% were couples.

Table 1

Demographic Profile of Respondents

Item	Frequency	Percentage
Gender
Male	70	48.6
Female	74	51.4
Household income ($)
<29,999	25	17.5
30,000-49,999	21	14.7
50,000-79,999	33	23.1
80,000-99,999	20	14.0
100,000-149,999	25	17.5
≥150,000	19	13.3
Age (years)
18-24	27	18.6
25-34	84	57.9
35-44	21	14.5
45-54	9	6.2
55-64	3	2.1
≥65	1	0.7
Marital status
Single	84	57.9
Couple without children	35	24.1
Couple with children	24	16.6
Divorced/separated/widowed	2	1.4

Most respondents stayed in hotels for 6 to 9 nights (22.6%), whereas a combined 13.7% stayed for more than 20 nights (Table 2). In their most recent trip involving a hotel stay, most respondents traveled for leisure (54.8%), whereas 19.2% traveled only for business. However, approximately 23% of respondents traveled for combined leisure and business purposes. In terms of hotel choice, most respondents stayed in 4-star hotels (51.7%) followed by 3-star hotels, whereas only a very few respondents stayed in lower rated hotels (2-star and 1-star).

Table 2

Travelers’ Behavior

Item	Frequency	Percentage
Nights spent in hotels (past 12 months)
1-2	17	11.6
3-5	27	18.5
6-9	33	22.6
10-14	27	18.5
15-20	22	15.1
21-35	11	7.5
>35	9	6.2
Purpose of the most recent trip (hotel stay)
Leisure	80	54.8
Business	28	19.2
Combined leisure–business	34	23.3
Other	4	2.7
Hotel classification
5-star	24	16.6
4-star	75	51.7
3-star	38	26.2
2-star	6	4.1
1-star	1	0.7
other	1	0.7

Model Testing

To examine the reliabilities of the scales, Cronbach’s alpha values were calculated (Table 3). The alpha values for all scales ranged between .819 and .934, thus exceeding the suggested cut-off point of .7 (Hair, Anderson, Tatham, & Black, 1998; Nunnally, 1978). Thus, it was concluded that the constructs had satisfactory reliability and were kept for further analyses. A confirmatory factor analysis (CFA) was conducted (Agarwal & Prasad, 1998; Table 3) using the Amos 5.0 software package. The goodness-of-fit indices for the model indicated strong fit, with χ² = 269.86 (p < .001), with 142 degrees of freedom, a normed-χ² of 1.90, a goodness-of-fit index (GFI) of .85, an adjusted goodness-of-fit index (AGFI) of .80, a normalized-fit index (NFI) of .90, an incremental fit index (IFI) of .94, a Tucker–Lewis index (TLI) of .93, and a root mean square error of approximation (RMSEA) of .07. All the fit indicators were above the common acceptance levels suggested by the literature (Bollen, 1989; Jöreskog & Sörbom, 1993).

Table 3

Confirmatory Factor Analysis

Constructs and Items	Item Loadings	Squared Multiple Correlations	Mean (SD)	Cronbach’s α	Composite Construct Reliabilities
Perceived usefulness			3.718 (0.880)	.903	.905
PU1	.856	.733
PU2	.822	.676
PU3	.729	.531
PU4	.942	.887
Perceived ease of use			4.011 (0.644)	.819	.821
PEOU1	.752	.566
PEOU2	.659	.434
PEOU3	.870	.757
PEOU4	.630	.397
Perceived innovativeness			3.634 (0.734)	.859	.862
PI1	.781	.610
PI2	.767	.588
PI3	.803	.645
PI4	.774	.599
Attitudes			3.727 (1.045)	.888	.886
ATT1	.936	.876
ATT2	.891	.794
ATT3	.758	.575
ATT4	.644	.415
Intentions			3.496 (1.081)	.934	.937
INT1	.913	.834
INT2	.938	.880
INT3	.884	.781

The examination of convergent validity requires scrutiny of factor loadings and squared multiple correlations (SMCs) of the measurement items (Bollen, 1989). All factor loadings had values between .630 and .942 on their underlying constructs and were significant (p < .001). In addition, the SMCs were calculated for all items (Table 3). All items had SMC values greater than the suggested value of .4 (Bollen, 1989), whereas one item (PEOU4), had a SMC value of .397, which is within 0.5% from .4. According to Fornell and Larcker (1981), discriminant validity is established if, for any two constructs, A and B, the average variance extracted (AVE) for A and the AVE for B exceed the squared correlation between A and B. The inter-construct correlations were calculated based on the averaged scales for these constructs, that is, items pertaining to each underlying construct were averaged. In this case, all AVE scores, ranging from .538 to .832, were greater than the suggested cut-off value of .5 (Fornell & Larcker, 1981; Table 4). Furthermore, the AVE scores for any two constructs were greater than their corresponding squared interconstruct correlations (Fornell & Larcker, 1981). In addition, the composite construct reliabilities (CCRs) were calculated for all constructs (Table 3). The composite reliability is a measure that depicts the extent to which a number of items indicate a common construct (Hair et al., 1998). In this study, all CCR values were greater than the acceptable level of .7 (Hair et al., 1998). Therefore, the conditions for convergent and discriminant validity were met for all constructs.

Table 4

Convergent and Discriminant Validity

	PU	PEOU	PI	ATT	INT
PU	.707
PEOU	.340	.538
PI	.022	.070	.611
ATT	.623	.394	.030	.665
INT	.533	.378	.047	.643	.832

Note. PU = perceived usefulness; PEOU = perceived ease of use; PI = perceived innovativeness; ATT = attitude; INT = intentions. The values on the diagonal (in boldface) represent the average variance extracted for each construct (AVE) whereas the variables below the diagonal represent the squared correlations between each pair of latent constructs.

The research model was analyzed using structural equation modeling (SEM), using the Amos 5.0 software package. Fitting the model to the sample resulted in a chi-square value of 277.11 (p < .001), with 147 degrees of freedom, and a normed-χ² of 1.88. Furthermore, the model had a GFI value of .85, an AGFI value of .81, a NFI value of .90, an IFI value of .94, a TLI value of .93, and a RMSEA value of .07. All the fit measures exceeded their suggested values (Jöreskog & Sörbom, 1993), and thus, it was concluded that the model fit was good.

Significance of the path loadings provided results for hypothesis testing (Figure 2). Perceived usefulness (β = .72, p < .001) was a significant predictor of attitudes, thus providing support for Hypothesis 1. In hotels, guests’ use of biometric systems that are perceived as useful (i.e., perceived to be performing tasks for consumers better than alternative technologies) are likely to lead to the development of positive attitudes toward using biometric systems. For example, as guests perceive that biometric systems increase the effectiveness of their interactions with the hotels (i.e., check in/out, guest area access, food/beverage payment), it is likely that they recognize the benefits of biometric systems and start liking the idea of using them. Perceived ease of use (β = .23, p < .01) had a significant direct impact on attitudes toward using biometric systems in hotels, providing support for Hypothesis 2. In hotel settings, users who perceive biometric systems as easy to use are likely to develop positive attitudes toward using biometric systems. J. Kim et al. (2008) pointed out that a technology that is perceived as easy to use might help users redirect unused effort to other tasks. Similarly, as biometric systems require minimal effort to interact with a hotel, guests can redirect their unused effort toward other tasks or leisure/entertainment activities.

Figure 2

Model Testing Results

The TAM literature documents inconsistent results with regard to the direct relationship between perceived ease of use and attitudes (J. Kim et al., 2008). Several scholars could not validate a direct relationship between perceived ease of use and attitudes, and indicated that in certain technological contexts this relationship appeared to be mediated by perceived usefulness and enjoyment (J. Kim & Forsythe, 2008). J. Kim and Forsythe further argued that the existence of a direct relationship between perceived ease of use and attitudes might vary by technology. However, in the context of the hotel industry, this study indicated that a direct relationship exists between perceived ease of use and attitudes toward using biometric systems. As indicated by the SMCs, perceived usefulness and ease of use together explained 80% of the variability in attitudes toward biometric systems in hotels. Hotel guests will develop positive attitudes toward using biometric systems if such systems are perceived as useful and easy to use. The path coefficients of the two predictors of attitudes indicated that perceived usefulness was a stronger predictor of attitudes than was perceived ease of use. Guests’ attitudes toward use of biometric systems in hotels seem to be more strongly influenced by perceptions of usefulness (i.e., performing tasks better than alternative systems) than by perceptions of effort-free use. Furthermore, perceived ease of use was a significant predictor of perceived usefulness (β = .69, p < .001), explaining 48% of the variability in perceived usefulness, and thus, providing support for Hypothesis 3. The easier to use a biometric system is perceived to be by hotel guests, the more likely it is that they find it useful.

The direct relationship between attitudes toward use and intentions to use has been at the heart of theory, both in innovation diffusion (Rogers, 1983) and technology adoption (Davis, 1986, 1989). In this study, attitudes toward use of biometric systems explained approximately 79% in the variability of intentions to use biometric systems in hotels (β = .89, p < .001), thus providing support for Hypothesis 4. This result suggests that guests who develop positive attitudes toward the use of biometric systems in hotels are likely to use these systems.

Although explaining only 9% of variability in perceived ease of use, perceived innovativeness (β = .29, p < .01) was found to be a significant predictor of ease of use, thus supporting Hypothesis 5. A fit seems to exist between guests inclined toward technology and their perceptions of ease of use of biometric systems. That is, for guests with a general inclination toward technology, biometric systems would eventually seem easier to use than it would be for guests not inclined toward technology. As expected, all the hypotheses were supported providing empirical validation of this variant of TAM, which can be used to examine guests’ intentions to use biometric systems in the hotel industry.

Conclusions

The research presented here was motivated by the fact that biometric systems are becoming increasingly present in all aspects of modern life (Langenderfer & Linnhoff, 2005). This research advances the current knowledge in the theory and practice of hospitality by empirically validating the TAM in the context of adoption of biometric systems in the hotel industry. In addition, this study could be considered one of the first steps in the examination adoption of biometric systems by hotel guests in the United States, which has seen relatively more applications of biometrics than other nations. To this end, an ampler discussion of several important theoretical and practical implications and recommendations of this research is following.

From a theory advancement perspective, a first key contribution of this study is that it validates the historical robustness of the TAM within the context of a novel technology—biometrics—in the hotel industry. Thus, this study advances the scholarly effort in hospitality aimed at finding the most appropriate theory for the study of adoption of biometric systems in hotels. The overwhelming empirical support for the first four hypotheses confirms that, as expected, the TAM methodology is a parsimonious, yet robust framework to explain hotel guests’ attitudes and intentions to use biometric systems via classic perceptions such as usefulness and ease of use. Thus, this study offers researchers an unambiguous methodology, which can become established as a compelling theoretical foundation to further study various aspects of biometric systems’ adoption in hospitality.

A second key contribution of this research is the extension of the classic TAM by adding perceived innovativeness. This study hypothesized and found support for a theoretical model in which individual differences influence guests’ attitudes toward using biometric systems in hotels through their influence on perceptions about this technology (i.e., perceived ease of use). Thus, this study offers an improved theoretical base for the study of adoption of biometric systems in hotels, given that many studies on innovation diffusion and technology adoption focused on explaining attitudes and intentions to use based mostly on perceptions of the system under study (i.e., usefulness, ease of use, security, safety). Thus, by adding the user’s personal technology inclination dimension, this study provides a superior theoretical basis to study technology adoption, yet retaining a parsimonious structure.

Third, this study contributes to the increasing body of theoretical and empirical evidence by validating the TAM as an appropriate theoretical base for the study of voluntary technology adoption. The TAM was originally designed by to predict technology adoption in mandatory (i.e., work related) settings (Davis, 1986, 1989). Since then, a variety of replications and extensions have been developed, most of them in mandatory settings. In mandatory settings, users’ adoption of technology may be influenced by managerial presence, organization-specific circumstances, including broader organizational goals (C. H. Lin, Shih, & Sher, 2007). In contrast, in voluntary settings, users may have multiple system choices, and adoption may be truly voluntary. For this reason, the examination of biometric systems adoption in voluntary settings remains more challenging than in mandatory settings, but it may provide very important insight into the free formation of attitudes and intentions to use new technologies.

Ultimately, this study contributes to the continuous paradigmatic advancement of the TAM framework by focusing on the relatively less understood context of users’ adoption of biometric systems. As today’s interconnected society offers more occasions for the use of biometric systems, the research community can use this extended TAM framework to investigate consumer adoption in industries such as travel, foodservice, recreation, and general retail. This way, a gap in the current research could be filled, as most biometric system research focuses on the technical aspects of biometrics, largely neglecting user adoption aspects.

Overall, a large percentage (79%) of the variability in intentions to adopt biometric systems in hotels was explained by its predictors, indicating that this extended variant of TAM is an appropriate theoretical framework to examine guests’ intentions to use biometric systems in hotels. In hotel guests, perceived innovativeness had a significant impact on perceived ease of use. In turn perceived usefulness and ease of use had significant impacts on attitudes toward using biometric systems, and further, on intentions to use biometric systems in hotels. Thus, it can be concluded that, in spite of their limited use by hotels, biometric systems are ready to be adopted by guests.

Implications

An objective of this study was to determine the manner in which guests’ perceptions of usefulness and ease of use affected their attitudes toward and intentions to use biometric systems in hotels. As the results indicated, the strongest predictor of attitudes was perceived usefulness. In the hotel industry, this may be indicative that, to be adopted, biometric systems must provide evidence of superiority relative to the alternative systems. As biometric systems become increasingly available, hotels might explain potential benefits to guests in an effort to stir their curiosity about these systems. This approach might stimulate guests’ cognitions related to system performance, which might strengthen their perceptions of the system’s latent potential. In turn, this may trigger exploratory use, which is believed to impact perceived usefulness, with a direct impact on attitudes and intentions to use (Saeed & Abdinnour-Helm, 2008).

To increase usefulness, hotels could pursue the integration of biometric systems with other information systems that are offered to guests (i.e., reservations, entertainment). In that sense, a number of advantages can be foreseen, especially in terms of value to guests, accessibility, and cost. First, a fully integrated system that allows guests to use the same biometrics at multiple properties would definitely add value to guests’ hotel stay experiences, especially in terms of optimization of interactions with the hotel. Furthermore, such fully integrated systems could set up switch barriers, which, in the long term, could result in an increase in guests’ purchasing behavior of a particular hotel brand. Coupled with strong attitudes toward the brand, this may result in a true, strong sense of loyalty toward the property/brand. Second, an integration of biometrics into other systems that guests have already adopted would eventually result in an easier adoption of the biometric component, as their perceptions of usefulness may transfer from the existing (i.e., traditional access or payments methods) to the new (i.e., biometric systems) parts of the integrated system. However, from an operational perspective, the issue of compatibility between the existing and new is critical. For example, most of today’s vendors of biometric systems use proprietary algorithms for capture and authentication. Thus, as the standards related to these systems are still in infancy, it is indicated that hotels avoid vendors using proprietary algorithms, as it would limit future system interoperability (Kleist, 2007).

Another factor that might contribute to the enhancement of beliefs, especially of usefulness, of biometric systems is their popularity and extensive usage in other contexts. As biometric systems are more widely spread in contexts such as airport and border security, users might diminish some of their concerns associated with the use of biometric systems. With increasing use and resulting familiarity with biometric systems while traveling, it is anticipated that more users would eventually become convinced of the efficiency, convenience, and harmlessness of such technologies, contributing to the development of more positive beliefs and attitudes toward biometric systems. Although when crossing borders or entering certain countries travelers might find the use of biometrics to be mandatory, they might eventually develop attitudes that could be strong enough to stimulate curiosity and voluntary use in further settings, such as hotels. Thus, hotels might embrace and at least explore the offering of biometric systems to guests, with the expectation that they would eventually recover the cost associated with the implementation of such systems.

Although perceived ease of use was not a strong predictor of attitudes, it strongly impacted perceived usefulness, thus supporting one of the fundamental hypotheses of TAM. In the context of biometric systems in the hotel industry, it means that guests who learn easily how to use biometric systems also may see more clearly their benefits in performing tasks such as check in/out, access, or payment. Thus, once hotels offer biometric systems, they need to convince guests about the convenience and usefulness of such systems. As perceived ease of use was a weaker predictor of attitudes than was perceived usefulness, hotels need to implement biometric systems that are high in ease of use. At the same time, they must emphasize that such systems would eventually optimize their interactions with a hotel. However, one may speculate that, as biometric systems are not currently widespread in the hotel industry, their benefits, especially in terms of ease of use, may be underestimated by guests.

Perceived innovativeness toward technology was found to be a strong predictor of perceived ease of use. This seems to suggest that innovative people, who have a natural inclination toward technology and are willing to take risks, would easily learn how to use such systems, and in turn, their beliefs about system usefulness may strengthen. As guests may develop their beliefs about technology from a variety of sources (including mass media and interpersonal channels; Lewis et al., 2003), hotels should use all media to encourage them to try out the new systems once they are offered. Such an approach might work well in hotel settings where guests already use a variety of other technologies (i.e., self check in/out, in-room entertainment). Stimulating guests who possess higher degrees of personal innovativeness toward information technology might increase the rate of adoption. However, this might be a challenging task for hotels as it is difficult to distinguish and classify guests based on their innovativeness. One way in which hotels can move a step closer to identifying the most innovative guests is to examine their previous behavior in terms of technology adoption and usage. For this, information such as the type of distribution channel used (i.e., online vs. offline), requests for Internet service in the room, or usage of other available technologies could provide substantial hints into guests’ perceived innovativeness toward information technologies.

Limitations and Directions for Further Research

Because of several limitations, the results of this research should be interpreted with caution. A first limitation is the sample. Although a sample of the general traveler population would have been desirable, a sample of MBA students and graduates was used in this study. Thus, the demographic and behavioral characteristics of this sample of respondents may reflect the attitudes and intentions of early adopters (i.e., they may be younger and more highly educated than the general population). However, since early adopters are particularly important in driving innovation diffusion in a variety of contexts (Goldsmith, Flynn, & Goldsmith, 2003), this sample is viewed as adequate. More important, given the purpose of this study to test theoretical relationships between constructs rather than making point and interval estimates of those relationships, the nature of the sample does not constitute a great concern (Calder, Phillips, & Tybout, 1981; Cowart, Fox, & Wilson, 2008). Finally, this sample should be viewed as adequate for at least two additional reasons: (a) respondents’ familiarity with computerized systems (Brackett & Carr, 2001; Stern et al., 2008) and (b) prior use of this type of sample in theory development (Calder et al., 1981). Further research should replicate the results of this study with general population samples to improve generalizability to other traveler groups.

A second limitation is the task setting. Respondents had been given the task to imagine a scenario in which they would stay in a hotel that offered biometric systems. This task environment allowed for better control of some of the outside factors that otherwise could have induced bias. For example, to diminish the effect of security concerns, the respondents have been instructed to consider that the systems in the given scenario are 100 percent safe and secure and no harm or fraud could result from their usage. In addition, the TAM methodology is weak outside of the task environment. A direction for further research is to apply this theoretical framework to examine adoption in controlled hotel settings where a limited variety of biometric systems is in use.

The relationship between intentions to perform a behavior and actual behavior is a critical issue addressed in previous research. In this study, intentions are the final stage in the model, assuming, based on overwhelming evidence from literature (Bruner & Kumar, 2005), that intentions lead to actual behavior. This might constitute a third limitation of this study. Although using actual measures of behavior would be insightful, the practicality of such an approach may be limited, especially since today, biometric systems are still in their infancy in the hotel industry. Thus, further studies could replicate this setting and use measures of actual behavior once biometrics are more widespread in the hotel industry.

Because of the very limited research in the area of biometrics in the hotel industry (J. Kim et al., 2008; Lumsden & Beldona, 2006; Murphy & Rottet, 2009), this study provides a foundation for further studies on the adoption of biometric systems in this industry. As biometric systems become increasingly present in hospitality settings, the research community should concentrate on understanding not only how guests adopt such systems but also how organizations (i.e., managers, staff) adopt and offer them. In addition, given that this study employed an extended variant of TAM, it would be interesting to further explore how other social or personal factors (as extensions of TAM) influence the adoption of biometric systems in hotels.

One of such personal factors may be the users’ natural concerns (i.e., physical harm, identity theft, loss of privacy) related to the use of novel technologies. All novel technologies are believed to carry some degree of risk, which may be perceived by users as being hidden or unpredictable (Mordini, 2007). In the case of biometrics in hotels, some of the most critical concerns are users’ privacy and fear of physical harm (J. Kim, 2009, J. Kim et al., 2008). Outside hospitality, Schouten and Jacobs (2009) found that the most common privacy issues associated with biometrics included storage of biometric data, identity theft as a consequence of biophishing (fraudulently obtaining someone’s biometric data), and tracking/tracing of individuals. Such concerns may exacerbate technophobia (fear of technology) in some users, which may influence the dynamics of their adoption of biometric systems. Thus, a promising direction for further research is to explore consumers’ privacy concerns associated with biometric systems as a possible factor that may influence biometric systems adoption in hotels. Furthermore, from a marketing standpoint, it would be interesting to find how adoption of biometric systems varies across guest segments.

The current literature provides increasing evidence that biometric systems will be eventually widely adopted by guests and enthusiastically offered by hotels. However, the choice of biometric systems may be largely dictated by characteristics such as usefulness, ease of use, and by users’ personal characteristics. Thus, it would be the role of the research community to further investigate the implications for both guests’ hotel stay experiences and hotels’ profitability indicators once the dynamics of wide scale biometric system adoption are set in motion.

Footnotes

Appendix B

Appendix A

Measurement Scales

Perceived usefulness
PU1	Using biometric systems would enhance the effectiveness of my interactions^a with a hotel
PU2	Using biometric systems would improve the quality of my stay in a hotel
PU3	Using biometric systems would allow me to do things better in a hotel
PU4	Overall, I believe biometric systems are useful in hotels
Perceived ease of use
PEOU1	Learning to deal with biometric systems in hotels would be easy for me
PEOU2	My interactions with biometric systems in hotels would be clear and understandable
PEOU3	My interactions with biometric systems in hotels would not require a lot of mental effort
PEOU4	Overall, I believe biometric systems are easy to use
Perceived innovativeness
PI1	In general, I am among the first to try out new technologies
PI2	In general, I am interested in experimenting with new technologies as soon as I hear about them
PI3	In general, I like to experiment with the new technologies
PI4	In general, I keep up with the latest technological developments in my areas of interest
Attitudes
ATT1	Using biometric systems in hotels is a good ← → bad idea
ATT2	Using biometric systems in hotels is wise ← → foolish
ATT3	Using biometric systems in hotels is beneficial ← → not beneficial
ATT4	Using biometric systems in hotels is enjoyable ← → not enjoyable
Intentions
INT1	I will use biometric systems in hotels in the future
INT2	Biometrics would be one of my favorite technologies in hotels
INT3	I will strongly recommend others to use biometric systems in hotels

Examples of interactions with a hotel include: check in/out, guest area access, food/beverage payment.

Appendix

Appendix C

Analysis Of Variance Table: Nonresponse Bias Analysis

Items	Sum of Squares	df	Mean Square	F	Significance
PU1
Between groups	3.131	2	1.565	1.590	.207
Within groups	156.548	159	0.985
Total	159.679	161
PU2
Between groups	1.370	2	0.685	0.661	.518
Within groups	164.661	159	1.036
Total	166.031	161
PU3
Between groups	1.801	2	0.901	0.920	.401
Within groups	155.711	159	0.979
Total	157.512	161
PU4
Between groups	3.042	2	1.521	1.531	.220
Within groups	158.026	159	0.994
Total	161.068	161
PEOU1
Between groups	0.510	2	0.255	0.519	.596
Within groups	77.990	159	0.491
Total	78.500	161
PEOU2
Between groups	2.556	2	1.278	1.918	.150
Within groups	105.944	159	0.666
Total	108.500	161
PEOU3
Between groups	1.885	2	0.942	1.273	.283
Within groups	117.720	159	0.740
Total	119.605	161
PEOU4
Between groups	1.900	2	0.950	1.468	.233
Within groups	102.872	159	0.647
Total	104.772	161
PI1
Between groups	0.316	2	0.158	0.164	.849
Within groups	153.363	159	0.965
Total	153.679	161
PI2
Between groups	0.299	2	0.150	0.265	.768
Within groups	89.978	159	0.566
Total	90.278	161
PI3
Between groups	3.017	2	1.508	1.925	.149
Within groups	124.594	159	0.784
Total	127.611	161
PI4
Between groups	2.071	2	1.035	1.380	.255
Items	Sum of Squares	df	Mean Square	F	Significance
Within groups	119.293	159	0.750
Total	121.364	161
ATT1
Between groups	0.510	2	0.255	0.143	.867
Within groups	283.767	159	1.785
Total	284.278	161
ATT2
Between groups	0.117	2	0.058	0.042	.959
Within groups	221.469	159	1.393
Total	221.586	161
ATT3
Between groups	2.515	2	1.258	0.880	.417
Within groups	227.145	159	1.429
Total	229.660	161
ATT4
Between groups	3.845	2	1.922	1.529	.220
Within groups	199.933	159	1.257
Total	203.778	161
INT1
Between groups	0.781	2	0.391	0.331	.719
Within groups	187.860	159	1.182
Total	188.642	161
INT2
Between groups	1.191	2	0.595	0.453	.636
Within groups	208.840	159	1.313
Total	210.031	161
INT3
Between groups	1.557	2	0.778	0.519	.596
Within groups	238.641	159	1.501
Total	240.198	161

References

Adams

(2002). Hotels slow to embrace biometric security. Hotel & Motel Management, 217(8), 46.

Adler

(2008). Biometric systems security. In Jain

A. K.

Flynn

Ross

A. A.

(Eds.), Handbook of biometrics (pp. 381-402). New York, NY: Springer.

Agarwal

Karahanna

(2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24, 665-694.

Agarwal

Prasad

(1998). A conceptual and operational definition of personal innovativeness in the domain of information technology, Information Systems Research, 9, 204-215.

Agnvall

(2007). Biometrics clock in. HR Magazine, 52(4), 103-105.

Ahn

Ryu

Han

(2007). The impact of web quality and playfulness on user acceptance of online retailing. Information & Management, 44, 236-275.

Ajzen

(1985). From intentions to actions: A theory of planned behavior. In Kuhl

Beckmann

(Eds.), Action-control: From cognition to behavior (pp. 11-39). Heidelberg, Germany: Springer.

Ajzen

(1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211.

Alterman

(2003). A piece of yourself: Ethical issues in biometric identification. Ethics and Information Technology, 5, 139-151.

10.

Araujo

C. L.

Sucupira

L. H. R.

Lizarraga

M. G.

Ling

L. L.

Yabu-Uti

B. T.

(2005). User authentication through typing biometrics features. IEEE Transactions on Signal Processing, 53, 851-855.

11.

Ary

Jacobs

Razavieh

(1996). Introduction to research in education (5th ed.). Fort Worth, TX: Holt, Rinehart & Winston.

12.

Best

(2005, February 1). Supermarket: Let your fingers do the paying. CNET News. Retrieved from http://news.cnet.com/Supermarket-Let-your-fingers-do-the-paying/2100-1029_3-5559074.html?tag=mncol

13.

Biometrics. (2010). Retrieved from http://www.bizrate.com/biometrics/

14.

Bolle

R. M.

Connell

J. H.

Pakanti

Ratha

N. K.

Senior

A. W.

(2004). Guide to biometrics. New York, NY: Springer.

15.

Bollen

K. A.

(1989). Structural equations with latent variables. New York, NY: Wiley.

16.

Brackett

L. K.

Carr

B. N.

Jr. (2001). Cyberspace advertising vs. other medial consumer vs. mature student attitudes. Journal of Advertising Research, 41, 23-32.

17.

Bruner

G. C.

Kumar

(2005). Explaining consumer acceptance of handheld Internet devices. Journal of Business Research, 58, 553-558.

18.

Buhalis

Law

(2008). Progress in information technology and tourism management: 20 years on and 10 years after the Internet: The state of e-tourism research. Tourism Management, 29, 609-623.

19.

Calder

B. J.

Phillips

L. W.

Tybout

A. M.

(1981). Designing research for applications. Journal of Consumer Research, 8, 197-207.

20.

Carroll

Siguaw

(2003). Evolution in electronic distribution: Effects on hotels and intermediaries. Ithaca, NY: The Center for Hospitality Research at Cornell University.

21.

Chandra

Calderon

(2005). Challenges and constraints to the diffusion of biometrics in information systems. Communications of the ACM, 48(12), 101-106.

22.

Chollet

Dorizzi

Petrovska-Delacretaz

(2009). Introduction: About the need of an evaluation framework in biometrics. In Petrovska-Delacretaz

Chollet

Dorizzi

(Eds.), Guide to biometric reference systems and performance evaluation (pp. 1-10). New York, NY: Springer.

23.

Christou

Kassianidis

(2002). Consumers perception and adoption of online buying for travel products. Journal of Travel & Tourism Marketing, 12(4) 93-107.

24.

Chung

Law

(2009). Have the perceptions of the successful factors for travel web sites changed over time? The case of consumers in Hong Kong. Journal of Hospitality & Tourism Research, 33, 438-447.

25.

Chung

Tan

F. B.

(2004). Antecedent of perceived playfulness: an exploratory study on user acceptance of general information-searching web sites, Information & Management, 41, 869-881.

26.

Connolly

D. J.

Olsen

M. D.

Moore

R. G.

(1998). The Internet as a distribution channel. Cornell Hotel and Restaurant Administration Quarterly, 39(4), 42-54.

27.

Connors

J. J.

Elliot

(1994). Teacher perceptions of agriscience and natural resources curriculum. Journal of Agricultural Education, 35(4), 15-19.

28.

Cowart

K. O.

Fox

G. L.

Wilson

A. E.

(2008). A structural look at consumer innovativeness and self-congruence in new product purchases. Psychology & Marketing, 25, 1111-1130.

29.

Craver

M. L.

(2008). Registered Traveler Program finally hitting its stride. Retrieved from http://www.kiplingerforecasts.com

30.

Davis

F. D.

(1986). Technology acceptance model for empirically testing new end-user information systems: Theory and results (Unpublished doctoral dissertation). Massachusetts Institute of Technology, Cambridge.

31.

Davis

F. D.

(1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319-340.

32.

Deane

Barrelle

Henderson

Mahar

(1995). Perceived acceptability of biometric security systems. Computers and Security, 14, 225-231.

33.

Den Hartog

Van Munster

(2008). Biometrics at the turnstiles. Computer Weekly, 156, 20-22.

34.

Devaraj

Fan

Kohli

(2002). Antecedents of B2C channel satisfaction and preference: validating e-commerce metrics. Information Systems Research, 13, 316-333.

35.

Fishbein

(1963). An investigation of the relationships between beliefs and object and the attitude toward that object. Human Relations, 16, 233-240.

36.

Fishbein

Ajzen

(1975). Belief, attitudes, intentions, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.

37.

Flynn

L. R.

Goldsmith

R. E.

(1993). A validation of the Goldsmith and Hofacker Innovativeness Scale. Educational and Psychological Measurement, 53, 1105-1116.

38.

Fornell

Larcker

(1981). Structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39-50.

39.

Giarimi

Magnusson

(2002). Investigation of user acceptance for biometric verification/identification methods in mobile units (Unpublished master’s thesis). Stockholm University/Royal Institute of Technology, Stockholm, Sweden. Retrieved from http://www.dsv.su.se/research/seclab/pages/pdf-files/02-17.pdf

40.

Goldsmith

R. E.

Hofacker

C. F.

(1991). Measuring consumer innovativeness. Journal of the Academy of Marketing Science, 19, 209-221.

41.

Goldsmith

R. E.

Flynn

E. R.

Goldsmith

E. B.

(2003). Innovative consumers and market mavens. Journal of Marketing Theory and Practice, 11, 54-64.

42.

Hair

J. F.

Anderson

R. E.

Tatham

R. L.

Black

W. C.

(1998). Multivariate data analysis with readings (5th ed.). New York, NY: Prentice Hall.

43.

Heracleous

Wirtz

(2006). Biometrics: The next frontier in service excellence, productivity and security in the service sector. Managing Service Quality, 16, 12-22.

44.

Hsu

C. L.

H. P.

(2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information & Management, 41, 853-868.

45.

Huh

H. J.

Kim

Law

(2009). A comparison of competing theoretical models for understanding acceptance behavior of information systems in upscale hotels. International Journal of Hospitality Management, 28, 121-134.

46.

Hurt

H. T.

Joseph

Cook

C. D.

(1977). Scales for the measurement of innovativeness. Human Communication Research, 4, 58-65.

47.

Kim

Han

H. J.

(2008). The effects of perceived risk and technology type on users’ acceptance of technologies. Information & Management, 45, 1-9.

48.

International Biometric Group. (2005). Point of sale: Retailers try their hand at finger-scanning payment systems. Retrieved from http://www.biometricgroup.com/in_the_news/06_20_05.html

49.

Ives

R. W.

Etter

D. M.

Welch

T. B.

(2005). A multidisciplinary approach to biometrics. IEEE Transactions on Education, 48, 462-471.

50.

Jackson

L. A.

(2009). Biometric technology: The future of identity assurance and authentication in the lodging industry. International Journal of Contemporary Hospitality Management, 21, 574-582.

51.

Jain

A. K.

(2007). Biometric recognition. Nature, 449, 38-40.

52.

Jain

A. K.

Ross

A. A.

(2008). Introduction to biometrics. In Jain

A. K.

Flynn

Ross

A. A.

(Eds.), Handbook of biometrics (pp. 1-22). New York, NY: Springer.

53.

James

Pirim

Boswell

Reithel

Barkhi

(2006). Determining the intention to use biometric devices: An application and extension of the technology acceptance model. Journal of Organizational and End User Computing, 18(3), 1-24.

54.

Jones

Williams

Hillier

Comfort

(2007). Biometrics in retailing. International Journal of Retail and Distribution Management, 35, 217-222.

55.

Jöreskog

K. G.

Sörbom

(1993). LISREL 8 user’s reference guide. Chicago. IL: Scientific Software International.

56.

Kaplanidou

Vogt

(2006). A structural analysis of destination travel intentions as a function of website features. Journal of Travel Research, 45, 204-216.

57.

Karahanna

Straub

D. W.

Chervany

N. L.

(1998). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23, 183-213.

58.

Kim

G. S.

Park

S. B.

(2008). An examination of factors influencing consumer adoption of short message service (SMS). Psychology & Marketing, 25, 769-786.

59.

Kim

(2009). A comprehensive structural model of factors influencing customers’ intention to use biometrics in the hospitality industry (Doctoral dissertation). Retrieved from Proquest Dissertations & Theses (AAT 3383980).

60.

Kim

Brewer

Bernhard

(2008). Hotel customers perceptions of biometric door locks. Journal of Hospitality Marketing & Management, 17, 162-183.

61.

Kim

Forsythe

(2008). Sensory enabling technology acceptance model (SE-TAM): A multiple-group structural model comparison. Psychology & Marketing, 25, 901-922.

62.

Kim

K. H.

E. J.

Takahashi

Schellhase

Kim

M. S.

Lee

C. H.

(2008). A model of adoption of digital multimedia broadcasting (DMB) service: Comparisons in Korea, Japan, and Germany. Psychology & Marketing, 25, 806-820.

63.

Kim

S. H.

(2008). Moderating effects of job relevance and experience on mobile wireless technology acceptance: Adoption of a smartphone by individuals. Information & Management, 45, 387-393.

64.

Kim

T. G.

Lee

J. H.

Law

(2008). An empirical examination of the acceptance behaviour of hotel front office systems: An extended technology acceptance model. Tourism Management, 29, 500-513.

65.

Kirby

(2008). Buying into biometrics: Slowly but surely the industry begins to embrace the technology. Hotels Magazine. Retrieved from http://www.hotelsmag.com/article/361318-Buying_Into_Biometrics.php?q=biometricsA

66.

Kleist

V. F.

(2007). Building technologically based online trust: Can the biometrics industry deliver the online trust silver bullet? Information Systems Management, 24, 319-329.

67.

Koltzsch

(2007). Biometrics: Market segments and applications. Journal of Business Economics and Management, 8, 119-122.

68.

Koufaris

(2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13, 205-223.

69.

Lam

Cho

(2007). A study of hotel employee behavioral intentions towards adoption of information technology. International Journal of Hospitality Management, 26, 49-65.

70.

Langenderfer

Linnhoff

(2005). The emergence of biometrics and its effects on consumers. Journal of Consumer Affairs, 39, 314-338.

71.

Laux

D. D.

(2007). A study of biometric authentication adoption in the credit union industry (Master’s thesis). Retrieved from Proquest Dissertations & Theses (AAT 1443068).

72.

Law

Chen

(2000). Internet in travel and tourism—part II: Expedia. Journal of Travel & Tourism Marketing, 9(4), 83-88.

73.

Law

Jogaratnam

(2005). A study of hotel information technology applications. International Journal of Contemporary Hospitality Management, 17, 170-181.

74.

Lee

H. Y.

Kim

W. G.

Lee

Y. K.

(2006). Testing the determinants of computerized reservation system users’ intention to use via a structural equation model. Journal of Hospitality & Tourism Research, 30, 246-266.

75.

Lee

Kozar

K. A.

(2008). An empirical investigation of anti-spyware software adoption: A multitheoretical perspective. Information & Management, 45, 109-119.

76.

Legris

Ingham

Collerette

(2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40, 191-204.

77.

Lewis

Agarwal

Sambamurthy

(2003). Sources of influence on beliefs about information technology use: An empirical study of knowledge workers. MIS Quarterly, 27, 657-679.

78.

Lin

C. H.

Shih

H. Y.

Sher

P. J.

(2007). Integrating technology readiness into technology acceptance: The TRAM model. Psychology & Marketing, 24, 641-657.

79.

Lin

H. F.

(2007). Predicting consumer intentions to shop online: An empirical test of competing theories. Electronic Commerce Research and Applications, 6, 433-442.

80.

Lopez-Nicolas

Molina-Castillo

F. J.

Bouwman

(2008). An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models. Information & Management, 45, 359-364.

81.

Liu

C. S.

Wang

(2008). Determinants of accepting wireless mobile data services in China. Information & Management, 45, 52-64.

82.

Yao

J. E.

C. S.

(2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. Journal of Strategic Information Systems, 14, 245-268.

83.

Lucas

(2005). Biometrics come into focus. Collections and Credit Risk, 10(3), 18-21.

84.

Lumsden

S. A. M.

Beldona

(2006, June). Biometrics technology applications in the lodging industry. Paper presented at the 2006 Hospitality Information Technology Association (HITA) Conference, Minneapolis, MN.

85.

Liu

(2004). The technology acceptance model: A meta-analysis of empirical findings. Journal of Organizational and End User Computing, 16, 59-72.

86.

Marriott secures Prestige with help from VingCard Elsafe. (2010). Hotel Online. Retrieved from http://www.hotel-online.com/News/PR2010_1st/Feb10_VingCardVenezuela

87.

Midgley

D. F.

Dowling

G. R.

(1978). Innovativeness: The concept and its measurement. Journal of Consumer Research, 4, 229-242.

88.

Miller

(2005, December 27). Casinos leery of biometrics, new pay systems. Las Vegas Business Press. Retrieved from http://www.lvbusinesspress.com/articles/2005/12/27/news/news02.txt

89.

Moon

J. W.

Kim

Y. G.

(2001). Extending the TAM model for the World Wide Web context. Information & Management, 38, 217-230.

90.

Mordini

(2007). Technology and fear: Is wonder the key? Trends in Biotechnology, 25, 544-546.

91.

Morosan

Jeong

(2008). Users’ perceptions of two types of hotel reservation websites. International Journal of Hospitality Management, 27, 284-292.

92.

Murphy

H. C.

Rottet

(2009). An exploration of the key hotel processes implicated in biometric adoption. International Journal of Contemporary Hospitality Management, 21, 201-212.

93.

Nunnally

J. C.

(1978). Psychometric theory. New York, NY: McGraw-Hill.

94.

S. H.

Kim

Y. M.

Lee

C. W.

Shim

G. Y.

Park

M. S.

(2009). Consumer adoption of virtual stores in Korea: Focusing on the role of trust and playfulness. Psychology & Marketing, 26, 652-668.

95.

Pons

Polak

(2008). Understanding user perspectives on biometric technology. Communications of the ACM, 51, 115-118.

96.

Premkumar

Ramamurthy

Liu

H. N.

(2008). Internet messaging: An examination of the impact of attitudinal, normative, and control belief systems. Information & Management, 45, 451-457.

97.

Rogers

E. M.

(1983). Diffusion of innovations (3rd ed.). New York, NY: Free Press.

98.

Rogers

E. M.

Shoemaker

F. F.

(1971). Communication of innovations: A cross-cultural approach. New York, NY: Free Press.

99.

Ruth

(2000). Applying a modified technology acceptance model to determine factors affecting behavioral intention to adopt electronic shopping on the World Wide Web: A structural equation modeling approach (Unpublished doctoral dissertation). Drexel University, Philadelphia, PA.

100.

Saeed

K. A.

Abdinnour-Helm

(2008). Examining the effects of information system characteristics and perceived usefulness on post adoption usage of information systems. Information & Management, 45, 376-386.

101.

Schepers

Wetzels

(2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44, 90-103.

102.

Schouten

Jacobs

(2009). Biometrics and their use in e-passports. Image and Vision Computing, 27, 305-312.

103.

Shih

H. P.

(2004). Extended technology acceptance model of Internet utilization behavior. Information & Management, 41, 719-729.

104.

Shin

D. H.

(2009). Determinants of customer acceptance of multi-service network: An implication for IP-based technologies. Information & Management, 46, 16-22.

105.

Simon

(2004). Company seeks public acceptance for iris biometrics in posh hotel. Aviation Weeks Homeland Security and Defense Report, 3(32), 4.

106.

Singh

A. J.

Kasavana

M. L.

(2005). The impact of information technology on future management of lodging operations: A Delphi study to predict key technological events in 2007 and 2027. Tourism and Hospitality Research, 6, 24-37.

107.

Starwood Hotels Resorts goes live with Global TelePresence meetings suites allowing guests to meet across continents. (2010). Hospitalitynet.com. Retrieved from http://www.hospitalitynet.org/news/4045573.print

108.

Stern

B. B.

Royne

M. B.

Stafford

T. F.

Bienstock

C. C.

(2008). Consumer acceptance of online auctions: An extension and revision of the TAM. Psychology & Marketing, 25, 619-636.

109.

Sturgeon

(2005, April 19). Caesars Palace to offer Net phones, biometric IDs. CNET News. Retrieved from http://news.cnet.com/Caesars-Palace-to-offer-Net-phones%2C-biometric-IDs/2100-7355_3-5676821.html?tag=mncol

110.

Taylor

Todd

P. A.

(1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6, 144-176.

111.

Tsai

R. C.

(2007). Biometrics in the private sector: Trends and case studies. In Coats

W. S.

Bagdasarian

Helou

T. J.

Lam

(Eds.), The practitioner’s guide to biometrics (pp. 139-146). Chicago, IL: American Bar Association.

112.

Vijan

(2004). Corporate America slow to adopt biometric systems. Computer World Magazine, 38(32), 32-34.

113.

Vijayasarathy

L. R.

(2004). Predicting consumer intentions to use on-line shopping: the case for augmented technology acceptance model. Information & Management, 41, 747-762.

114.

VingCard Aloft Hotels partner to run first smart check-in program. (2010). Retrieved from http://www.vingcard.com/page?id=36&key=70073

115.

Walczuch

Lemmink

Streukens

(2007). The effect of service employees’ technology readiness on technology acceptance. Information & Management, 44, 206-215.

116.

Wang

Qualls

(2007). Towards a theoretical models of technology adoption in hospitality organizations. International Journal of Hospitality Management, 26, 560-573.

117.

Wober

Gretzel

(2000). Tourism managers’ adoption of marketing decision support systems. Journal of Travel Research, 39, 172-181.

118.

Wolfe

(2008). User milestone for a biometric system. American Banker, 173(190), 10.

119.

Choi

Rho

(2005). Extending the TAM for a t-commerce. Information & Management, 42, 965-976.