Voluntary Steps toward Air Travel Security

Abstract

Using a modified version of the Technology Acceptance Model, this study examines travelers’ voluntary adoption of registered traveler biometric systems. Such systems have important consequences for general air travel security, while improving the experiences of registered travelers. This study shows that air travelers’ intentions to use registered traveler biometric systems are influenced by their attitudes toward these systems, which in turn are strongly affected by their perceptions of usefulness, privacy, and to a lesser extent, by ease of use. This study also establishes the critical roles of perceived security and innovativeness toward information technology as indirect antecedents of travelers’ intentions to use registered traveler biometric systems.

Keywords

biometric systems security privacy innovativeness air travel

Introduction

As the world gradually interconnects into a global village, many national governments have raised concerns that the consequences of today’s turbulent events (i.e., terrorism, mass immigration) could negatively affect the security and welfare of their citizens. These concerns intensified the need for accurately keeping track of the persons who travel across national borders, especially by air. To this end, many travel-related organizations were mandated to implement new systems and procedures designed to improve security and identity management and reduce fraud. For example, travel security agencies instituted mandatory security screenings and imposed restrictions on the items allowed aboard commercial aircraft (Jain and Ross 2008), while border control authorities deployed sophisticated identity management systems (Stana 2007). At the heart of many of these systems lies an advanced technology—biometric authentication—which is currently gaining substantial ground because of its accuracy, efficiency, and ease of use (Jain and Ross 2008).

Biometric authentication is based on unique and hard to change human characteristics, called biometrics. Biometrics include personal features (i.e., fingerprints, facial features, retinal vascular patterns) and behavioral traits (i.e., speech, signature, or gait patterns) (Bolle et al. 2004). Biometric authentication is performed automatically by computerized systems, which are generically called biometric systems. Biometric systems’ main task is to ascertain an individual’s (claimed) identity, which should prevent impostors from accessing secure resources (Jain and Ross 2008). Given the uniqueness of biometrics to each individual, biometric authentication is thought to be superior to any rival authentication methodology (Jain and Ross 2008). Biometric systems using various types of biometrics, or modalities, are currently in use in a number of areas, such as identity management (Stana 2007), payment (Wolfe 2008), and attendance management (Sturgeon 2005), mostly in mandatory settings.

Although most travel-related biometric applications have been deployed in mandatory settings (i.e., mandatory identity verification), recently, a number of biometric applications became available to travelers for use on a voluntary basis (i.e., unforced enrollment/use in airports) (Lazarick and Cambier 2008). They are generically called registered traveler systems or programs. These systems play a vital role in the security of air travel as they generate a voluntary segregation of travelers into “registered,” believed to be low risk, and “nonregistered,” believed to be higher-risk (Marohn 2006). This segregation allows security agencies to devote more resources to higher-risk travelers, about whom little is known, to prevent/neutralize security threats (United States General Accounting Office 2002). Registered travelers enjoy certain benefits, including a better predictability of the time dedicated to security checks, and faster and more secure processing (Lazarick and Cambier 2008).

Although promising, to date, registered traveler systems have not been deployed extensively. Problems such as cost, reliability (Singh and Kasavana 2005), data integrity and security (Roberts 2007), and legal challenges (Woodward 2008) may still constitute important barriers that must be considered when deploying biometric systems. Yet a mass adoption of registered traveler systems could, in theory, bring substantial benefits to travelers and organizations, such as increasing air travel security while reducing congestion, and generating revenues for the organizations managing the systems. In this context, the purpose of this study is to examine air travelers’ attitudes and intentions to use registered traveler biometric systems. By revisiting and adapting an extended variant of the Technology Acceptance Model (TAM; Davis 1989; James et al. 2006) to the air travel context, this study seeks to provide a comprehensive, yet parsimonious, theoretical foundation for the examination of biometric systems in air travel. To this end, this study follows three specific objectives: (1) to examine the antecedents of attitudes and intentions to use registered traveler biometric systems; (2) to understand the roles of perceived security and privacy in shaping travelers’ attitudes and intentions to use registered traveler biometric systems; and (3) to understand the role of travelers’ perceived innovativeness in affecting their attitudes and intentions to use registered traveler biometric systems.

Review of Literature

Biometric System Architecture and Performance

The architecture of biometric systems includes several interconnected modules, which collect, interpret, match, and store biometric information and produce an accept/reject decision. They function in two stages: enrollment and authentication. To use the system, a user must first enroll. Enrollment is initiated by providing (1) a reading of a user’s biometric via a sensor module and (2) biographic information via a traditional input device (Jain and Ross 2008). Then, relevant biometric information is extracted to create a feature set, which is then associated with the user’s biographic information to create a user profile. Once the profile is stored in the database module, the user is considered enrolled, and he/she can use the system for authentication. With any authentication, a new reading of the initially collected biometric must be provided for matching. The matching module compares the feature set resulting from the new reading against feature sets stored in the database. The extent of match between the two feature sets is described by a similarity score, which later serves as the basis for the decision to accept/reject the user.

In travel, most biometric systems are primarily based on fingerprint recognition, and are considered highly accurate (Alonso-Fernandez et al. 2009). Fingerprint recognition is believed to be one of the most popular biometric modalities (Rowe, Nixon, and Butler 2008), because of its high accuracy (Maio et al. 2002), maturity (Clausen 2008), relatively low cost of acquisition devices, and ease of use (Alonso-Fernandez et al. 2009). However, despite their benefits, fingerprint biometric systems still face operational, organizational, and legal challenges. For example, operational performance of fingerprint biometric systems depends on the quality of the fingerprint images captured (Clausen 2008). Performance can be affected by distortions in the image caused by the shape of the finger, excess moisture on the sensing surface, excessive ambient lighting, or user-related factors (i.e., dry skin, worn surface features, and users’ incorrect finger placement; Schneider 2008; Rowe, Nixon, and Butler 2008). In addition, performance is influenced by test conditions like the type of sensor employed, number of users, number of readings per users, and users’ demographics (Jain and Ross 2008).

Organizational challenges include issues related to the overall system cost, a firm’s openness to technology, and the nature of a firm’s activity. Despite a wide range of costs of biometric systems ($300 to more than $5,000 per unit) (Kirby 2008), deploying large-scale biometric systems remains costly. In addition, organizations’ openness to technology, their ability to integrate biometric systems with existing systems, and their specific security needs given their industry may represent important challenges. Finally, there are legal challenges. To date, details about the legal requirements of organizations acquiring, handling, storing, and disposing of biometric information remain unelucidated, as certain biometric information is currently allowed to circulate among law enforcement agencies. Also, it is unclear what are the rights of individuals to accept or refuse biometric information provisioning. For example, biometric programs administered by the U.S. government may potentially require individuals to provide biometric information to a government agency, which may raise physical and information privacy concerns (Woodward 2008). Additional legal challenges may result from refusal of certain society groups to provide biometric information because of religious objections (Woodward 2008).

Registered Traveler Biometric Systems

In the United States, there are two initiatives generically called registered traveler programs or systems: (1) the Registered Traveler (RT) system designed to facilitate airport screening of trusted outbound air travelers and (2) the Global Entry (GE) system, designed to expedite immigration procedures for trusted inbound travelers at ports of entry. Both systems are primarily based on fingerprint modalities. The RT system was developed at the initiative of the U.S. government, and is available to any U.S. citizen or lawful permanent resident (Transportation Security Administration 2006). To register (enroll), travelers must first undergo regular background checks for the purpose of risk determination. On enrollment, registered travelers are issued electronic cards, which contain images of their fingerprints and, optionally, iris images. Once registered, travelers can authenticate at any participating airport using self-service systems. However, even on successful authentication, registered travelers cannot bypass the mandatory TSA screenings (Transportation Security Administration 2006). As of July 2008, there were 135,000 active registered travelers at the 19 participating airports (Transportation Security Administration 2008).

The GE system is operated by the U.S. Customs and Border Protection (CBP) authority. The system provides low-risk inbound air travelers expedited clearance into the United States without the need of a face-to-face meeting with an immigration officer for document control (CBP 2010). Document control is passed on to the registered travelers by allowing them to use self-service systems. The system was first deployed in June 2008 and, as of November 2009, there were approximately 16,000 GE members and 20 participating airports (i.e., Chicago O’Hare, Atlanta Hartsfield-Jackson; DHS 2009). The greatest benefit for security agencies is the classification of low- and high-risk travelers. For travelers, the greatest benefits are time savings. Other traveler benefits include minimum interaction with CBP staff at the airports, less paperwork, and mutual benefits with other countries (CBP 2010; DHS 2009).

Research Model and Hypotheses

Technology adoption and the original TAM

The abundant information technology literature eventually converges toward one central idea, that is, technology can add value to both travelers and organizations (Fuchs et al. 2010). To date, however, despite the strong interest in understanding the drivers of technology adoption, there are no studies on voluntary adoption of biometric systems by consumers in travel, especially in air travel. In general, voluntary technology adoption is based mainly on technology-related (i.e., usefulness, ease of use) and personal factors (i.e., innovativeness, demographics). In contrast, mandatory (i.e., work-related) technology adoption could be affected by organizational factors (i.e., a firm’s objectives, managerial styles, industry variables), in addition to technology and personal factors. Thus, understanding how consumers willingly adopt technology is more challenging in voluntary settings, where consumers generally have multiple system options, which makes adoption truly voluntary (Lin, Shih, and Sher 2007).

Despite the lack of academic work on biometrics in travel, the hospitality literature documents a few recent studies on customers’ perceptions of biometric systems. In hospitality, biometric systems have been viewed as a technology of the future, with potential application beyond security (Lumsden and Beldona 2006). Murphy and Rottet (2009) found hotel guests in Switzerland to be comfortable with and ready to adopt biometric systems, especially fingerprint modalities. Also, Las Vegas hotel guests’ intentions to adopt biometric door locks were found to be affected by system attributes (i.e., usefulness, ease of use, data and property security), subjective norms, and personal influences (Kim 2009). Moreover, convenience, physical security, data security, and personal concerns were important criteria in discriminating among “advocates” and “opponents” of biometric door locks in hotels (Kim, Brewer, and Bernhard 2008). Despite a large body of technical literature on biometric systems, the literature on biometric system adoption by consumers documents only a few notable efforts (i.e., Kim 2006; Kim and Forsythe 2008; Walczuch, Lemmink, and Streukens 2007).

Attempting to predict users’ technology adoption before large-scale deployment, Davis (1989) developed a theory that eventually became widely accepted in the technology adoption literature. Adapting the Theory of Reason Action (Fishbein and Ajzen 1975) to a technology context, Davis (1989) developed the Technology Acceptance Model (TAM) to explain users’ attitudes and intentions to use a technology based on users’ beliefs about that technology. In the TAM, users’ perceived usefulness and ease of use of a technology affect their attitudes toward the technology, which, in turn, affect their intentions to use that technology. Because of its solid theoretical foundation (Oh et al. 2009), parsimony, and strong empirical support (Lin 2007), the TAM gained a strong popularity in a many national (Gelderman 1998), technological (Schepers and Wetzels 2007), and industrial contexts (Lin, Shih, and Sher 2007), including travel/tourism and hospitality (Morosan and Jeong 2008).

Given the novelty of biometric technology, using the TAM to study the adoption of registered traveler systems appears to be a valid approach. However, the original TAM, based solely on the relationships among usefulness, ease of use, attitudes, and intentions, often proved insufficient to explain adoption of certain technologies in specific industrial contexts. To remedy this deficiency, the original TAM was often extended with constructs such as playfulness (Morosan and Jeong 2008), social influences on consumers (Venkatesh and Davis 2000), or security and privacy (James et al. 2006). Most TAM extensions demonstrated appropriate explanatory properties within their respective industrial and technological contexts. In this study, however, given that biometric systems may raise security and privacy concerns among users (James et al. 2006; Kim 2009), a modified version of the TAM that includes perceived security (Vatanasombut et al. 2008) and perceived privacy (Chang and Chen 2009) was adopted as a theoretical base. In addition, to account for the inherent differences among travelers in their technology orientation, the construct of perceived innovativeness toward information technologies was included in the model (Kim and Forsythe 2008). Thus, by retaining only perceived security, privacy, and innovativeness from the many possible TAM extensions, this theoretical basis provides sufficient support to capture the full context of biometric systems in travel while retaining its parsimony.

Within the TAM, usefulness, defined as an individual’s evaluation of the utility provided by a new technology, affects his or her attitudes toward using that technology (Davis 1989). In air travel, the registered traveler biometric systems substantially enhance multiple dimensions of usefulness, including processing accuracy (Tsai 2007), processing speed (DHS 2009), and security (Maio et al. 2002). The TAM is predicated on the fact that a new technology should be also easy to use for users to adopt it (Davis 1989). In general, of the two critical predictors of attitudes, perceived usefulness seems to be a stronger predictor of attitudes than perceived ease of use (Huh, Kim, and Law 2008), which indicates that the usefulness of a technology may drive its adoption even when the technology is not as easy to use (Venkatesh and Davis 2000). Generally, all contemporary biometric systems are characterized by ease of use (Jain 2007). Specifically, all registered traveler biometric systems are designed to be user-friendly in order to provide benefits to both users and the deploying organizations (i.e., expedite the processing of certain travelers in a secure manner; Lazarick and Cambier 2008), which is critical to adoption (Pons and Polak 2008). In addition, although both perceived usefulness and ease of use affect users’ attitudes toward a new technology, many studies point out that technologies perceived as easy to use enhance users’ perceptions that those technologies are actually useful (Lu et al. 2008).

At the core of the TAM lies the relationship between a user’s attitudes toward the use of a technology and his or her intentions to use that technology (Davis 1989). Attitudes are defined as an individual’s position toward performing a behavior (Fishbein 1963) and are central in many technology adoption theories. In varied technology and industry contexts, a multitude of studies based on the original TAM suggests direct relationships between perceived usefulness and attitudes, between perceived ease of use and attitudes, between perceived ease of use and perceived usefulness, and between attitudes and intentions to use new technologies. Thus, the following hypotheses central to the original TAM have been formulated:

Hypothesis 1: There is a positive relationship between travelers’ perceived usefulness and their attitudes toward registered traveler biometric systems.

Hypothesis 2: There is a positive relationship between travelers’ perceived ease of use and their attitudes toward registered traveler biometric systems.

Hypothesis 3: There is a positive relationship between travelers’ perceived ease of use and their perceived usefulness of registered traveler biometric systems.

Hypothesis 4: There is a positive relationship between travelers’ attitudes toward registered traveler biometric systems and their intentions to use registered traveler biometric systems.

An Augmented Theoretical Foundation

Inevitably, any new information technology that requires users to input personal information may raise security concerns among users (Shin 2009). Security is defined as the extent to which technology users believe that an information technology is secure when handling sensitive information (Chang and Chen 2009). In the absence of objective measures of security of information technology provided by users, most literature focuses on perceived security, which represents a user’s subjective evaluation of the security of a technology (Chang and Chen 2009). Given users’ concerns about security in many technology contexts, security-related constructs have been added to many theoretical models, including the TAM (i.e., Fang et al. 2005; Shin 2009), and, specifically, to models examining adoption of biometric systems (i.e., James et al. 2006). However, the literature provides inconclusive and sometimes conflicting results regarding the exact role of perceived security in the adoption framework. For example, James et al. (2006) validated perceived need for security as a direct antecedent of perceived usefulness. Ha and Stoel (2009) viewed perceived security (in combination with privacy) as one of four dimensions of online shopping quality, and validated it as an antecedent of perceived trust, ease of use, and enjoyment. Lallmahamood (2007) also viewed a unified construct of perceived security and privacy, and validated direct relationships between perceived security/privacy and perceived usefulness, ease of use, and intentions to use Internet banking in Malaysia. Shin (2009) and Fang et al. (2006) viewed perceived security as a direct antecedent of intentions to use new technologies. In other extended models, perceived security was found to influence non-TAM constructs, such as trust (Vatanasombut et al. 2008), and satisfaction (Chang and Chen 2009). Given this context, elucidating the exact role of perceived security in the development of perceptions of biometric systems in travel is critical.

As biometric information has an irrevocable (i.e., it cannot be changed like passwords or user names; Bolle et al. 2004) and “personal” nature (i.e., it may reveal confidential information about the user, such as medical conditions; McPhee, Papadakis, and Tierney 1997), users’ security concerns vis-á-vis biometric systems may be substantial. Such concerns may also be exacerbated by a lack of firsthand information about the true vulnerabilities of biometric systems (Bolle et al. 2004), which may be due to their novelty. By diminishing security suspicions, travelers may enhance their perceptions that biometric systems can perform their tasks properly, which are at the heart of their perceptions of usefulness. Thus, based on the above considerations, the following hypothesis was formulated.

Hypothesis 5: There is a positive relationship between travelers’ perceived security and their perceived usefulness of registered traveler biometric systems.

Privacy reflects the extent to which individuals engage in a selective disclosure of personal information, aimed at keeping the equilibrium between private and public balanced (Margulis 2003). Similarly to security, the literature does not portray a consistent picture regarding the role of perceived privacy in technology adoption. The conceptualization of privacy differs from study to study, while empirical validation presents inconclusive, sometimes contradictory, findings. For example, Ha and Stoel (2009) and Lallmahamood (2007) did not view perceived privacy as distinct from security. Lallmahamood (2007) found support for a relationship between a combined perceived security/privacy construct and perceived usefulness of Internet banking in Malaysia, while Ha and Stoel (2009) validated a relationship between privacy/security (as a dimension of online shopping experience) and perceived ease of use. Tong (2009) validated a relationship between perceived privacy risk and perceived usefulness. Research has also supported relationships between perceived privacy and trust and enjoyment (Ha and Stoel 2009), and intentions to use technologies (Lallmahamood 2007; Tong 2009), including biometrics (Kim 2009). Within the context of biometrics in the United States, Kim (2009) found support for a relationship between privacy and perceived usefulness of biometric hotel door locks, while James et al. (2006), could not validate a relationship between perceived need for privacy and perceived usefulness of general biometric devices (i.e., palm geometry door locks, retinal scanners used for access). Overall, numerous scholars using variants of TAM seem to suggest that perceived privacy directly or indirectly influences technology adoption. Yet despite a relative wealth of topical research, it is not clear privacy influences technology adoption, especially within the context of biometric systems.

Intuitively, there is a strong connection between privacy and biometric systems, which may influence users’ adoption (James et al. 2006). Among many issues surrounding biometric technology privacy, two issues have a high importance: consumers’ limited ability to control the (1) collection and (2) handling of biometric information by organizations without consent. First, in biometric technology settings, especially when dealing with behavioral biometrics, collecting biometric information without consent is critical. However, in the context of registered traveler systems, biometric data are voluntarily provided by the travelers, which may mitigate, at least to a certain degree, travelers’ privacy concerns. Nevertheless, additional privacy concerns may still arise from storing and exchanging biometric information among organizations, as today, biometric information is a part of a wealth of personal information that is digitized and transferred among trusted institutions (i.e., national security agencies, local law enforcement; Bolle et al. 2004). However, as biometric systems mature, no evidence of incidents of mishandled or leaked biometric information has been presented to travelers, which may mitigate their privacy concerns, and encourage them to view travel-related biometric systems favorably. In addition, in the context of travel, positive security perceptions of biometric systems may enhance travelers’ perceptions that their privacy is not at risk.

Combined, the findings above led to the formulation of the following hypotheses:

Hypothesis 6: There is a positive relationship between travelers’ perceived privacy and their attitudes toward registered traveler biometric systems.

Hypothesis 7: There is a positive relationship between travelers’ perceived security and their perceived privacy of registered traveler biometric systems.

In the context of travel-related biometric systems, as generally in electronic service contexts, active participation of consumers in the service creation and consumption is paramount (Lovelock and Wirtz 2004). Yet most technology adoption theory builds on system perceptions, attitudes, and intentions, and rarely takes into account individual differences in consumers’ technology orientation, which influence the degree of participation in the creation/delivery of consumption experiences. As Davis (1989) designed the TAM to explain technology adoption in work-related settings, the original TAM usually proves deficient in capturing the nature of electronic services, which is characterized by high consumer involvement (Lin, Shih, and Sher 2007). To remedy this deficiency, the TAM has been modified by considering consumers’ individual differences in technology orientation (Kim 2006; Kim and Forsythe 2008). Thus, the variant of TAM used in this study included the concept of perceived innovativeness toward information technology.

Innovativeness is defined as the extent to which an individual is willing to try out new products (Midgley and Dowling 1978). The literature recognizes two views of innovativeness: global and domain specific (Flynn and Goldsmith 1993). In narrower technological domains, such as that of biometric systems in travel, domain-specific innovativeness is viewed as a human trait (Walczuch, Lemmink, and Streukens 2007), which allows “innovative” consumers to view new technologies differently than “noninnovative” consumers (Karahanna, Straub, and Chervany 1999). Previous adaptations of the TAM to narrower technology domains validated domain-specific innovativeness as a contributor to the model (i.e., Kim and Forsythe 2008; Walczuch, Lemmink, and Streukens 2007), but its role in the model remained elusive, arguably because of contextual factors. For example, in their study of information technology adoption in Belgian workplaces, Walczuch, Lemmink, and Streukens (2007) proposed relationships between innovativeness and perceived usefulness and ease of use, but only validated a relationship between innovativeness and ease of use. Kim and Forsythe (2008) found a direct relationship between perceived innovativeness and intentions to use technology in the context of online apparel merchandising in the United States. Despite such discrepancies about the role of innovativeness in technology adoption (see Kim 2006; Kim and Forsythe 2008), there seems to be some agreement that domain-specific innovativeness is a significant antecedent of ease of use (i.e., Lu et al. 2008; Walczuch, Lemmink, and Streukens 2007). To conclusively elucidate the role of perceived innovativeness in the TAM within the context of biometric systems in travel, and based on the previous research findings, the following hypothesis has been formulated:

Hypothesis 8: There is a positive relationship between consumers’ perceived innovativeness toward information technology and perceived ease of use of registered traveler biometric systems.

A representation of the proposed conceptual model and its hypotheses is provided in Figure 1.

Figure 1.

Proposed model

Method

Instrument Development

The research instrument was adapted based on previous studies using the TAM (i.e., Davis 1989) and its extensions (i.e., Kim 2009; Lee and Kozar 2008; Lu et al. 2008) to capture the context of biometric systems in travel. All constructs were measured using 5-point Likert-type scales, with values ranging from 1 = strongly disagree to 5 = strongly agree, except for attitudes, which were measured using a 5-point semantic differential scale. The instrument concluded with a section on the travel behavior of respondents (i.e., frequency of travel, type of travel) and a demographic section, which was used to produce a profile of respondents (i.e., gender, age, household income).

Perceived usefulness was measured with five items, tapping into the extent to which the registered traveler systems are useful. The items were adapted from Lopez-Nicolas, Molina-Castillo, and Bouwman (2008) and Lu et al. (2008). They measured the extent to which registered traveler biometric systems would improve the quality of travelers’ airport experiences, and allow them to do things faster, better, and more securely at the airport. The scale concluded with an evaluative item (Davis 1989). The ease-of-use scale, which included four items, measured the extent to which registered traveler biometric systems would be perceived as easy by travelers, their interactions would be clear and understandable and would not require a lot of mental effort (Lu et al. 2008). This scale also concluded with an evaluative item (Davis 1989). The scale for attitudes consisted of three semantic differential items anchored in word pairs such as “good–bad idea,” “wise–foolish,” and “enjoyable–not enjoyable” (Ahn, Ryu, and Han 2007; Lee and Kozar 2008). The scale for intentions included three items, measuring the extent to which travelers intend to and will use registered traveler biometric systems in the future, and the extent to which these systems will be among their favorite technologies in the future (Shin 2009).

The scale for perceived security was adapted from Vatanasombut et al. (2008) and Walczuch, Lemmink, and Streukens (2007) and included four items measuring the extent to which registered traveler systems were perceived as secure when sending sensitive information, when providing personal information to the system, and that the information provided to the system cannot be used by other people. The scale for perceived privacy was constructed based on the work of Kim, Brewer, and Bernhard (2008) and others, and included three items, measuring the extent to which registered traveler biometric systems make travelers concerned about personal privacy, make them feel personally uncomfortable, and have privacy concerns. These items were reverse coded. The scale for perceived innovativeness was adapted from Agarwal and Prasad (1999) and Walczuch, Lemmink, and Streukens (2007), and it included four items measuring the extent to which travelers would be the first to experiment a new information technology when they hear about it, would be the first among their peers to explore new technologies, liked to experiment with new information technologies, and can figure out high-tech products and services without the help of others.

Sampling and Data Collection

An online survey was conducted during in the period July to August 2010 using as respondents staff, graduate (MBA) students, and alumni of a small, private university from a large metro area in Southwestern United States. The sample frame was provided by the administration of the university and included a number of 1,024 potential participants. On two reminders, a total of 168 responses from respondents who traveled by air during the past two years (16% response rate) were kept for further analysis. Before completing the survey, the participants were provided with a definition and description of registered traveler biometric systems.

Analysis

Descriptive Analysis

A weakness of survey data is that respondents may provide different responses than nonrespondents, which is known as nonresponse bias. To test if the data in this study are characterized by nonresponse bias, a procedure suggested by Ary, Jacobs, and Razavieh (1996) and Connors and Elliot (1994) was followed. The sample was divided into three groups: early, mid-, and late respondents. Data from early respondents were then compared with data from late respondents. An analysis of variance was conducted on all items measuring respondents’ perceptions, attitudes, and intentions. In this study, there were no significant differences among respondents, which indicates that nonresponse bias is not a problem in this study.

Demographic and behavioral profiles of respondents (Table 1 and Table 2, respectively) were generated. Most respondents were relatively young, with the largest age group being 25 to 34 years (51.8%). The sample had approximately 57.1% females, while the largest respondent segment in terms of household income (29.2%) earned $50,000 to $79,999 per year. A total of 53.6% of respondents were couples. Most respondents traveled three to eight times a year (45.2%). In terms of type of travel, most respondents traveled for combined business and personal reasons (33.9%), while an overwhelming majority of respondents (82.2%) traveled in the economy class.

Table 1.

Respondents’ Demographic Profile

Characteristic	Percentage	Characteristic	Percentage
Age (years)		Type of household
18-24	13.1	Single	44.0
25-34	51.8	Couple without children	29.8
35-44	19.6	Couple with children	23.8
45-54	10.7	Divorced/separated/widowed	2.4
55-64	4.8
		Household income ($)
Gender		<29,999	4.2
Male	42.9	30,000-49,999	26.2
Female	57.1	50,000-79,999	29.2
		80,000-99,999	16.7
Employment		100,000-149,999	11.9
Employed, full time	78.0	≥150,000	11.8
Employed, part time	11.9
Business owner	3.0
Unemployed	7.1

Table 2.

Respondents’ Behavioral Profile

Characteristic	Percentage
Travel frequency
Less than once a year	10.1
Approximately 1-2 times a year	39.3
Approximately 3-8 times a year	45.2
Approximately once a month	4.8
Approximately once a week	0.6
Type of travel
Mostly business	2.4
More business than personal	3.0
Business/personal combined	33.9
More personal than business	14.9
Mostly personal	45.8
Most common air fare class
Economy class	82.7
Business class	14.9
First class	2.4

Measurement Model Analysis

The main analyses of this study consisted of a confirmatory factor analysis (CFA) and a structural equation modeling (SEM) analysis (Anderson and Gerbing 1988). To examine the psychometric properties of the instrument, a CFA was conducted (Agarwal and Prasad 1998) using Amos 5.0. This analysis provided the framework for a rigorous testing of reliability and validity, which is necessary before subjecting the structural model to tests related to fit (Tables 3 and 4). The CFA allowed for the analysis of relative and absolute fit indices (Hair et al. 2009). The model’s chi-squared statistic (χ²) was 398.19 (p < .001) (df = 278) and the χ²/df ratio was 1.43. According to Hair et al. (2009, p. 698), a χ²/df ratio “smaller than 2.0 is considered very good,” while values under 5.0 are acceptable (Toh, Lee, and Hu 2006). However, the chi-squared statistic’s sensitivity to a number of nonsubstantive variations from the model (i.e., multivariate normality) required the examination of at least one absolute and one incremental fit index (Hair et al. 2009). The root mean square error of approximation (RMSEA) was found to be .05 (p-close = .44), which is within acceptable levels suggested by Hair et al. (2009) and Browne and Cudeck (1992). A 90% confidence interval for the RMSEA indicated a true population RMSEA situated between .04 and .06, thus below the suggested cut-off point of .08 (Hair et al. 2009). Moreover, a p-close value of .44 indicated that the RMSEA does not significantly differ from .05 (Browne and Cudeck 1992).

Table 3.

Convergent and Discriminant Validity Test Results

Item	Standardized Factor Loading	Squared Multiple Correlations	Composite Construct Reliabilities	Mean (SD)
Perceived usefulness (PU)			.913	3.924 (0.819)
PU1	.860	.740
PU2	.808	.653
PU3	.783	.613
PU4	.742	.551
PU5	.914	.835
Perceived ease of use (PEOU)			.881	3.887 (0.655)
PEOU1	.825	.681
PEOU2	.825	.681
PEOU3	.773	.598
PEOU4	.798	.637
Attitudes (ATT)			.880	3.774 (0.993)
ATT1	.944	.891
ATT2	.941	.885
ATT3	.692	.479
Intentions (INT)			.939	3.312 (0.964)
INT1	.914	.835
INT2	.913	.834
INT3	.916	.839
Perceived security (PS)			.924	3.147 (0.944)
PS1	.845	.714
PS2	.956	.914
PS3	.748	.560
PS4	.909	.826
Perceived privacy (PP)			.900	2.843 (0.978)
PP1	.864	.746
PP2	.810	.656
PP3	.923	.852
Perceived innovativeness (PI)			.905	3.582 (0.893)
PI1	.783	.613
PI2	.843	.711
PI3	.969	.920
PI4	.761	.579

Table 4.

Discriminant Validity Test Results

	PU	PEOU	ATT	INT	PS	PP	PI
PU	.678
PEOU	.244	.649
ATT	.531	.347	.752
INT	.446	.298	.681	.836
PS	.328	.252	.542	.523	.753
PP	.196	.147	.429	.396	.460	.752
PI	.070	.171	.088	.152	.099	.056	.706

Note: The values on the diagonal represent the average variance extracted for each latent construct (AVE). The values below the diagonal represent the squared interconstruct correlations between each pair of latent constructs. All correlations are significant at p < .01 (two tailed).

Two common incremental fit indices, the comparative fit index (CFI) and the Tucker-Lewis index (TLI) were found to be .97, and .96 respectively, exceeding the commonly suggested thresholds of .90. All of the above indexes showed that the CFA is acceptable (Hair et al. 2009; Toh, Lee, and Hu 2006), which allowed for further analyses to be performed on this model (Joreskog and Sorbom 1993). The results of the CFA allowed for a test of reliability of the instrument. The scales’ reliabilities were tested using the composite construct reliability (CCR). All CCR values were situated above the recommended value of .7, which showed adequate reliability (Hair et al. 2009).

The convergent and discriminant validity of the instrument were further assessed. Convergent validity was assessed by examining each measurement item’s factor loadings, which should be higher than .7 (Hair et al. 2009) and significant (Anderson and Gerbing 1998). In this study, all factor loadings were significant (p < .001) and higher than .7, except for one item measuring attitudes, which had a value of .692, overall indicating appropriate convergent validity. In addition, Bollen (1989) suggested that the squared multiple correlations (SMCs) for each item should be greater than .4, which was true for all items in this study. Finally, the average variance extracted (AVE) values for each construct were calculated. All AVE values were greater than the common .5 threshold, indicating appropriate convergent validity (Hair et al. 2009). One common method to assess discriminant validity is to compare the AVE values with the squared interconstruct correlations. Should all constructs be characterized by appropriate discriminant validity, the AVE corresponding to each construct would be higher than the construct’s correlations with other constructs (Fornell and Larcker 1981). In this study, the AVE values of each pair of constructs were higher than their corresponding squared correlations, which indicated appropriate discriminant validity (Fornell and Larcker 1981).

Structural Model Analysis and Hypothesis Testing

Once the psychometric properties of the measurement model were validated, an SEM analysis was performed on the proposed structural model (see Figure 2). The analysis revealed two sets of findings, the model’s fit indicators and the path coefficients, which were necessary to test the proposed hypotheses. Overall, the model had a chi-squared statistic (χ²) of 487.11 (p < .001, df = 291) and χ²/df ratio of 1.67. According to Carmines and McIver (1981), a value under 5.0, and especially under 2.0 (Hair et al. 2009), as is the case in this study, is acceptable. Similarly to the CFA, at least one absolute and one relative fit index were examined. In this study, the RMSEA was .06, with a 90% confidence interval of (.05, .07), while the p-value corresponding to the RMSEA was .01, indicating appropriate fit. Two incremental fit indexes were calculated, CFI and TLI, as suggested by Carmines and McIver (1981) and Hair et al. (2009). With values of .95 and .94 respectively, both CFI and TLI indicated appropriate fit of the model. Despite the significant chi-squared statistic, given all the other acceptable fit indexes, the structural model was considered to be supported by the data (Toh, Lee, and Hu 2006). Overall, the model explained approximately 77% of the variability of intentions to use registered traveler biometric systems, which indicated that this particular variant of the TAM is appropriate for the examination of travelers’ attitudes and intentions to use registered traveler biometric systems.

Figure 2.

Structural model

The results of the analysis allowed for the hypothesis testing, revealing that each proposed relationship was significant (p < .001) in the predicted direction, which provided support for all hypotheses. The first four hypotheses were based on the original TAM. Hypothesis 1, for example, proposed a relationship between perceived usefulness and attitudes, which was supported (β = .53, p < .001). That is, travelers who understand the usefulness of registered traveler biometric systems, in terms of a more efficient, better, and secure airport experience, are likely to view registered traveler biometric systems favorably. The second important relationship proposed, between perceived ease of use and attitudes (Hypothesis 2), was also validated in this study (β = .19, p < .001). That is, travelers for whom the registered traveler biometric systems seem to be easy to use develop favorable attitudes toward these systems. The proposed relationship between attitudes and intentions to use was found to be very strong (β = .88, p < .001), which outlined the importance of attitudes in shaping travelers’ intentions to use these types of systems. That is, travelers who view registered traveler biometric systems favorably are likely to use them.

The hypothesized relationships among perceived security, privacy, and innovativeness and the original TAM constructs were also validated in this study. First, a significant relationship was found to exist between travelers’ security perceptions and their perceptions of usefulness of registered traveler biometric systems (β = .55, p < .001), providing support for Hypothesis 5. That is, travelers’ security concerns must be addressed in order for travelers to develop a sense of usefulness of these systems. Second, travelers’ perceptions of privacy was found to directly affect their attitudes toward using registered traveler biometric systems (β = .47, p < .001), thus providing support for Hypothesis 6. Third, the hypothesized relationship between travelers’ perceptions of security (Hypothesis 7) was also validated (β = .76, p < .001). That is, in the context of registered traveler biometric systems, travelers’ concerns about security and privacy have significant consequences for the adoption of these systems. Finally, as hypothesized (Hypothesis 8), this study validates the relationship between the travelers’ technology innovativeness and their perceptions of ease of use of registered traveler biometric systems (β = .48, p < .001). That is, travelers with a higher inclination toward information technology will perceive registered traveler biometric systems to be easier to use than travelers with low or no inclination toward technology.

A central component of the current model is travelers’ attitudes toward registered traveler biometric systems. Of all antecedents of attitudes, the strongest was perceived usefulness, followed by perceived privacy and perceived ease of use. That is, travelers are more likely to develop positive attitudes toward registered traveler biometric systems if they recognize the benefits of these systems and if their privacy concerns are mitigated. Together, the three antecedents explained a large portion of variability in attitudes (SMC = .80), indicating that they are sufficient to explain the formation of travelers’ attitudes toward biometric systems. Also, of the two antecedents of perceived usefulness, perceived security was stronger relative to perceived ease of use. Together, however, they explained less than half of the variability in perceived usefulness (SMC = .38). That is, travelers’ evaluations of security and ease of use are important in developing perceptions of usefulness of registered traveler biometric systems. Given their roles as important antecedents of the original TAM components, perceived security and privacy were found to be related to each other, with perceived security explaining a large portion of variability in perceived privacy (SMC = .58). Finally, perceived innovativeness explained only 23% of the variability in perceived ease of use, indicating that there may be other antecedents, along with travelers’ inclination toward information technology, that may explain how travelers develop their perceptions of ease of use of registered traveler biometric systems.

Discussion

Theoretical Contributions

During the past several years, the development of biometric technology as an area of research has witnessed an accelerated evolution, which was unfortunately geared mostly toward understanding the technicality of biometric technology (Jain and Ross 2008). Although a number of scholars developed models to examine various aspects of biometric system adoption (i.e., James et al. 2006; Murphy and Rottet 2009), the extant literature still does not provide a conclusive understanding of consumer adoption issues vis-à-vis biometric technology. Moreover, there is no known study to investigate consumer adoption of biometric systems in travel. Against this backdrop, this study not only provides a domain statement for biometric technology as a consumer behavior field of study but also validates a parsimonious, yet robust theoretical framework that explains consumers’ intentions to use biometric technology in the context of travel. By following a line of research that has modified the TAM by considering users’ security, privacy, and innovativeness perceptions, this research aims at extending and improving the TAM framework as an adoption theory. To this end, this study offers a number of significant theoretical contributions.

The main contribution of this study is that it provides a theoretical framework for the examination of travelers’ adoption of biometric systems in travel. The proposed model in this research was validated empirically, explaining 77% of travelers’ intentions to use biometric systems. Thus, supporting this theoretical framework appropriate to explain travelers’ adoption of biometric systems, this study offers the theoretical foundation necessary for further examinations of biometric system adoption in related travel, tourism, and hospitality contexts. Second, this study validates, in the context of biometric systems in travel, the importance of three constructs that can enhance the explanatory power of TAM framework: perceived security, perceived privacy, and perceived innovativeness. By including these constructs in the model, this variant of TAM can capture more comprehensively the context of formation of attitudes and intentions to use biometric systems in travel, thus yielding a theoretical framework that is robust, comprehensive, yet parsimonious.

The third key contribution is this study’s participation in the general scholarly effort aimed at understanding technology adoption in voluntary settings. Originally, the TAM was developed to explain technology adoption in mandatory (work-related) settings. Yet in such settings, variables such as organizational interventions, objectives, and even managerial styles may influence users’ adoption of technology (Lin, Shih, and Sher 2007). That is, users may reluctantly or involuntarily engage in adoption behaviors even in the absence of positive perceptions and attitudes toward a technology. In contrast, in voluntary settings such as the one in this study, the adoption of new technology is truly voluntary, as users may have multiple technology choices available and, most importantly, are not constrained to adopt any technology. Thus, voluntary settings allow for perceptions, attitudes, and intentions to form freely, which makes it more challenging to examine and understand. Thus, by focusing on voluntary adoption, this study makes a considerable contribution to the continuous advancement of adoption theory in voluntary settings. Ultimately, this study contributes to the continuous advancement of the overall technology adoption research by being one of a limited number of studies focusing on biometric system consumer adoption issues. Thus, this study bridges the existing gap in the current mostly technical biometrics research by addressing consumer adoption. The theoretical base offered by this study and others (i.e., James et al. 2006) can be applied to further examine the adoption of biometric systems in similar, yet unexplored, consumer-related contexts, where consumers’ involvement is critical.

Managerial Contributions

By focusing on air travelers’ adoption of registered traveler biometric systems, this study offers a number of notable managerial contributions to travel-related organizations. These contributions may help these organizations provide a superior value proposition to registered air travelers, which, in theory, should result in higher number of registered travelers, who are generally believed to carry a lower security risk. A higher number of registered travelers not only results in substantial revenues but, more importantly, allows organizations to deploy more resources to scrutinize the higher-risk nonregistered travelers, leading to improvements in security and convenience for all travelers.

To offer a higher value proposition to registered travelers, organizations must concentrate on issues such as usefulness, security, privacy, and ease of use, as they strongly affect travelers’ attitudes, which in turn affect the adoption of registered traveler biometric systems. That is, organizations must convey to travelers that these systems provide benefits such as faster, better, more efficient interactions at the airport, resulting in a superior airport experience. Organizations can increase the usefulness of such systems by providing hardware that works properly (fast, accurately) and is available at sufficient airport locations. Equally, organizations must portray the registered traveler biometric systems as being able to diminish travelers’ security and privacy concerns, which are normal given the novelty of this technology. A first step is to identify travelers’ potential security concerns. A second step involves disseminating accurate information about security and privacy. A third, and most important, step is ensuring that breaches in security and privacy do not happen and that organizations can respond quickly and efficiently in case of problems. In addition, it was found that travelers who perceive registered traveler biometric systems as easy to use are likely to build favorable attitudes toward these systems. Organizations need to persuade travelers that using registered traveler biometric systems does not require substantial mental effort. Thus, the number of users may increase as a result of potential exploratory use in consumers.

Another possible way in which organizations can encourage potential travelers to use registered traveler biometric systems is to target travelers who, because of their technology innovativeness, are more likely to use the systems. Perceived innovativeness affected travelers’ perceptions of ease of use, thus contributing indirectly to adoption or registered traveler biometric systems. In air travel, segmentation of travelers into technology inclined and not inclined has important implications as innovative consumers may fulfill essential roles in the diffusion of innovation and may influence the product choices of others by sharing knowledge (Shoham and Ruvio 2008). In addition, given that innovativeness leads indirectly to adoption, organizations could facilitate adoption by knowing with certain precision which consumers are more technology inclined, that is, more likely to adopt. Generally, such information is difficult to obtain a priori. Yet, in travel, this information is obtainable by organizations, as today’s travel experiences consist of a variety of digital subcomponents (i.e., booking, checking in). For example, booking, if done online, leaves a trace of electronic information, which can be collected and interpreted by organizations. Moreover, actions such as flight checking in, if done via personal computers or mobile terminals, may reveal important information about travelers’ technology preferences.

Limitations and Directions for Further Research

This study recognizes two limitations: the choice of sample and the task setting, which are common to all studies based on the TAM. This study’s sample may have been slightly biased, as it included individuals that have been or are currently affiliated with a university, which may have exposed them to technology more extensively than the general population. Although a sample of “general” travelers would have been desirable, the sample in this study was deemed appropriate for two main reasons: (1) the study was geared toward validating hypothesized relationships rather than estimating them (Cowart, Fox, and Wilson 2008), and (2) even a sample including respondents who have been slightly more exposed to technology than the general population may reveal important findings about adoption and diffusion of technology, given respondents’ assumed computer literacy and similarity to other samples used in a multitude of theory development studies (Calder, Phillips, and Tybout 1981). The second limitation, which is common to all TAM-based studies, is the task setting, as the TAM is limited outside of the task environment (Venkatesh et al. 2003).

As this study marks an important step in the exploration of travelers’ development of attitudes and intentions to use biometric systems in travel, a variety of further research directions can be pursued. Further research could examine travelers who are actually registered, to quantify more precisely the magnitude of the relationships among the variables leading to adoption. Other directions may include studying the role of behavioral variables (i.e., frequency of travel), trip characteristics (i.e., leisure vs. business; international vs. domestic vs. regional), and their impact on various psychographic (i.e., introverts vs. extroverts) or demographic groups (i.e., males vs. females; U.S. vs. international travelers). As biometric systems become more intertwined within travel and other consumer domains, research could examine additional antecedents to the variables used in this study. For example, understanding what influences travelers’ perceptions of security and privacy should be a priority in further research. Ultimately, an investigation of the emotions associated with the use of biometric systems in both voluntary and mandatory settings (i.e., anxiety) could also bring important contributions to the advancement of this fascinating technology.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

Bio

Cristian Morosan is an Assistant Professor in the Cameron School of Business at the University of St. Thomas, Houston. His research examines the adoption of biometric systems by consumers and organizations, information provisioning and management online, and online/offline communication effectiveness.

References

Agarwal

Ritu

Prasad

Jayesh

. (1998). “A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology.” Information Systems Research, 9 (2): 204-15.

Ahn

Tony

Ryu

Seewon

Han

Ingoo

. (2007). “The Impact of Web Quality and Playfulness on User Acceptance of Online Retailing.” Information & Management, 44 (3): 236-75.

Alonso-Fernandez

Fernando

Bigun

Josef

Fierrez

Julian

Fronthaler

Hartwig

Kollreider

Klauss

Ortega-Garcia

Javier

. (2009). “Fingerprint Recognition.” In Guide to Biometric Reference Systems and Performance Evaluation, edited by Petrovska-Delacrétaz

Chollet

Dorizzi

London: Springer, pp . 51-88.

Anderson

James C.

Gerbing

David W.

(1988). “Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach.” Psychological Bulletin, 10: 411-23.

Ary

Donald

Jacobs

Lucy C.

Razavieh

Asghar

. (1996). Introduction to Research in Education, 5th edition. Ft. Worth: Holt, Rinehart, and Winston, Inc.

Bolle

Ruud M.

Connell

Jonathan H.

Pakanti

Sharath

Ratha

Nalini K.

Senior

Andrew W.

(2004). Guide to Biometrics. New York: Springer.

Bollen

Kenneth A.

(1989). Structural Equations with Latent Variables. New York: Wiley.

Browne

Michael W.

Cudeck

Robert

. (1992). “Alternative Ways of Assessing Model Fit.” Sociological Methods & Research, 21 (2): 230-58.

Calder

Bobby J.

Phillips

Lynn W.

Tybout

Alice M.

(1981). “Designing Research for Applications.” Journal of Consumer Research, 8: 197-207.

10.

Carmines

Edward G.

McIver

John P.

(1981). “Analyzing Models with Unobserved Variables: Analysis of Covariance Structures.” In Social Measurement: Current Issues, edited by Bohrnstedt

G. W.

Borgatta

E. F.

Beverly Hills, CA: SAGE, pp . 65-115.

11.

Chang

Hsin H.

Chen

Su W.

(2009). “Consumer Perception of Interface Quality, Security, and Loyalty in Electronic Commerce.” Information & Management, 46 (2): 411-17.

12.

Angeline G.

Finney

R. Zachary

Lacey

Russell Z.

Sneath

Julie Z.

(2006). “Engaging the Consumer through Event Marketing: Linking Attendees with the Sponsor, Community, and Brand.” Journal of Advertising Research, 46: 420-33.

13.

Clausen

Sigmund

. (2008). “A Single-Line AC Capacitive Fingerprint Swipe Sensor.” In Advances in Biometrics. Sensors, Algorithms and Systems, edited by Ratha

N. K.

Govindaraju

London: Springer, pp . 49-62.

14.

Connors

James J.

Elliot

Jack

. (1994). “Teacher Perceptions of Agriscience and Natural Resources Curriculum.” Journal of Agricultural Education, 35 (4): 15-19.

15.

Cowart

Kelly O.

Fox

Gavin L.

Wilson

Andrew E.

(2008). “A Structural Look at Consumer Innovativeness and Self-Congruence in New Product Purchases.” Psychology & Marketing, 25 (12): 1111-30.

16.

Customs and Border Protection (CBP). (2010). “Global Entry Program Overview.” http://www.cbp.gov/xp/cgov/travel/trusted_traveler/global_entry/global_entry_discription.xml (accessed February 25, 2010).

17.

Davis

Fred D.

(1989). “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology.” MIS Quarterly, 13 (3): 319-40.

18.

Department of Homeland Security (DHS). (2009). Secretary Napolitano Announces Global Entry Expansion to 13 Additional Airports. http://www.dhs.gov/ynews/releases/pr_1250094008914.shtm (accessed February 25, 2010).

19.

Fang

Xiaowen

Chan

Susy

Brzezinski

Jacek

Shuang

. (2005). “Moderating Effects of Task Type on Wireless Technology Acceptance.” Journal of Management Information Systems, 22 (3): 123-57.

20.

Fishbein

Martin

. (1963). “An Investigation of the Relationships between Beliefs and Object and the Attitude toward That Object.” Human Relations, 16: 233-40.

21.

Fishbein

Martin

Ajzen

Icek

. (1975). Belief, Attitudes, Intentions, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley.

22.

Flynn

Leisa R.

Goldsmith

Ronald E.

(1993). “A Validation of the Goldsmith and Hofacker Innovativeness Scale.” Educational and Psychological Measurement, 53 (4): 1105-16.

23.

Fornell

Claes

Larcker

David

. (1981). “Structural Equation Models with Unobservable Variables and Measurement Error.” Journal of Marketing Research, 18 (1): 39-50.

24.

Fuchs

Matthias

Höpken

Wolfram

Föger

Andreas

Kunz

Martin

. (2010). “E-business Readiness, Intensity, and Impact an Austrian Destination Management Organization Study.” Journal of Travel Research, 49 (2): 165-78.

25.

Gelderman

Maarten

. (1998). “The Relation between User Satisfaction, Usage of Information Systems and Performance.” Information & Management, 34 (1): 11-18.

26.

Sejin

Stoel

Leslie

. (2009). “Consumer E-shopping Acceptance: Antecedents in a Technology Acceptance Model.” Journal of Business Research, 62 (5): 565-71.

27.

Hair

Joseph F.

Black

William C.

Babin

Barry B.

Anderson

Rolph E.

(2009). Multivariate Data Analysis, 7th edition. Upper Saddle River, NJ: Prentice Hall.

28.

Huh

Hyang J.

Kim

Taegoo

Law

Rob

. (2009). “A Comparison of Competing Theoretical Models for Understanding Acceptance Behavior of Information Systems in Upscale Hotels.” International Journal of Hospitality Management, 28 (1): 121-34.

29.

Jain

A. K.

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

30.

Jain

Anil K.

Ross

Arun A.

(2008). “Introduction to Biometrics.” In Handbook of Biometrics, edited by Jain

A. K.

Flynn

Ross

A. A.

New York: Springer, pp . 1-22.

31.

James

Tabitha

Pirim

Taner

Boswell

Katherine

Reithel

Brian

Barkhi

Reza

. (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.

32.

Joreskog

Karl G.

Sorbom

Dag

. (1993). LISREL 8 User’s Reference Guide. Chicago: Scientific Software International Inc.

33.

Karahanna

Elena

Straub

Detmar W.

Chervany

Norman L.

(1999). “Information Technology Adoption across Time: A Cross-Sectional Comparison of Pre-Adoption and Post-Adoption Beliefs.” MIS Quarterly, 23(2): 183-213.

34.

Kim

Jiyeon

. (2006). Sensory Enabling Technology Acceptance Model (SE-TAM): The Usage of Sensory Enabling Technologies for Online Apparel Shopping. Doctoral dissertation, Auburn University. Proquest Dissertations & Theses, AAT 3410355.

35.

Kim

Jiyeon

Forsythe

Sandra

. (2008). “Sensory Enabling Technology Acceptance Model (SE-TAM): A Multiple-Group Structural Model Comparison.” Psychology & Marketing, 25 (9): 901-22.

36.

Kim

Jungsun

. (2009). A Comprehensive Structural Model of Factors Influencing Customers’ Intention to Use Biometrics in the Hospitality Industry. Doctoral Dissertation, University of Nevada, Las Vegas. Proquest Dissertations & Theses, AAT 3383980 (accessed March 10, 2010).

37.

Kim

Jungsun

Brewer

Pearl

Bernhard

. (2008). “Hotel Customers’ Perceptions of Biometric Door Locks.” Journal of Hospitality Marketing & Management, 17 (1-2): 162-83.

38.

Kirby

Adam

. (2008). “Buying into Biometrics: Slowly but Surely the Industry Begins to Embrace the Technology.” Hotels Magazine. http://www.hotelsmag.com/article/361318-Buying_Into_Biometrics.php?q=biometricsA (accessed March 10, 2010).

39.

Lallmahamood

Muniruddeen

. (2007). “An Examination of Individual’s Perceived Security and Privacy of the Internet in Malaysia and the Influence of This on Their Intention to Use E-commerce: Using an Extension of the Technology Acceptance Model.” Journal of Internet Banking and Commerce, 12 (3): 1-26.

40.

Lazarick

Rick

Cambier

Jim L.

(2008). “Biometrics in the Government Sector.” In Handbook of Biometrics, edited by Jain

A. K.

Flynn

Ross

A. A.

New York: Springer, pp . 461-78.

41.

Lee

Younghwa

Kozar

Kenneth A.

(2008). “An Empirical Investigation of Anti-Spyware Software Adoption: A Multitheoretical Perspective.” Information & Management, 45 (2): 109-19.

42.

Lin

Chien H.

Shih

Hsin Y.

Sher

Peter J.

(2007). “Integrating Technology Readiness into Technology Acceptance: The TRAM Model.” Psychology & Marketing, 24 (7): 641-57.

43.

Lin

Hsiu F.

(2007). “Predicting Consumer Intentions to Shop Online: An Empirical Test of Competing Theories.” Electronic Commerce Research and Applications, 6 (4): 433-42.

44.

Lopez-Nicolas

Carolina

Molina-Castillo

Francisco J.

Bouwman

Harry

. (2008). “An Assessment of Advanced Mobile Services Acceptance: Contributions from TAM and Diffusion Theory Models.” Information & Management, 45 (6): 359-64.

45.

Lovelock

Christopher

Wirtz

Jochen

. (2004). Services Marketing People, Technology, Strategy, 5th edition. New York: Pearson Prentice Hall.

46.

June

Liu

Chang

Chun S.

Wang

Kanliang

. (2008). “Determinants of Accepting Wireless Mobile Data Services in China.” Information & Management, 45 (1): 52-64.

47.

Lumsden

Shelly A. M.

Beldona

Srikanth

. (2006). “Biometrics Technology Applications in the Lodging Industry.” Proceedings of the 2006 HITA Conference, Minneapolis, MN.

48.

Maio

Dario

Maltoni

Davide

Capelli

Raffaele

Wayman

J. L.

Jain

Anil K.

(2002). “FVC2000: Fingerprint Verification Competition.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 24 (3): 402-12.

49.

Margulis

Stephen T.

(2003). “Privacy as a Social Issue and Behavioral Concept.” Journal of Social Issues, 52 (2): 243-61.

50.

Marohn

Dana

. (2006). “The US Registered Traveler Program.” Biometric Technology Today, April 2006, pp. 7-9. www.biometrics-today.com (accessed February 22, 2010).

51.

McPhee

Stephen J.

Papadakis

Maxine A.

Tierney

Lawrence M.

(1997). Current Medical Diagnosis and Treatment. Stamford, CT: Appleton and Lange.

52.

Midgley

David F.

Dowling

Grahame R.

(1978). “Innovativeness: The Concept and Its Measurement.” Journal of Consumer Research, 4 (4): 229-42.

53.

Morosan

Cristian

Jeong

Miyoung

. (2008). “Users’ Perceptions of Two Types of Hotel Reservation Web Sites.” International Journal of Hospitality Management, 27 (2): 284-92.

54.

Murphy

Hillary C.

Rottet

Damien

. (2009). “An Exploration of the Key Hotel Processes Implicated in Biometric Adoption.” International Journal of Contemporary Hospitality Management, 21 (2): 201-12.

55.

Sang H.

Kim

Yong M.

Lee

Chong W.

Shim

Gyu Y.

Park

Min S.

Jung

Hong S.

(2009). “Consumer Adoption of Virtual Stores in Korea: Focusing on the Role of Trust and Playfulness.” Psychology & Marketing, 26 (7): 652–68.

56.

Pons

Alexander

Polak

Peter

. (2008). “Understanding User Perspectives on Biometric Technology.” Communications of the ACM, 51 (9): 115-18.

57.

Roberts

Chris

. (2007). “Biometric Attack Vectors and Defences.” Computers & Security, 26 (1): 14-25.

58.

Rowe

Robert K.

Nixon

Kristin A.

Butler

Paul W.

(2008). “Multispectral Fingerprint Image Acquisition.” In Advances in Biometrics. Sensors, Algorithms and Systems, edited by Ratha

N. K.

Govindaraju

London: Springer, pp . 3-23.

59.

Schepers

Wetzels

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

60.

Schneider

John K.

(2008). “Ultrasonic Fingerprint Sensors.” In Advances in Biometrics. Sensors, Algorithms and Systems, edited by Ratha

N. K.

Govindaraju

London: Springer, pp . 63-74.

61.

Shin

Dong H.

(2009). “Determinants of Customer Acceptance of Multi-Service Network: An Implication for IP-Based Technologies.” Information & Management, 46 (1): 16-22.

62.

Shoham

Aviv

Ruvio

Ayalla

. (2008). “Opinion Leaders and Followers: A Replication and Extension.” Psychology & Marketing, 25: 280-97.

63.

Singh

A. J.

Kasavana

Michael 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 (1): 24-37.

64.

Stana

Richard M.

(2007). “US-VISIT Program Faces Operational, Technological, and Management Challenges.” Testimony before the Committee of Homeland Security, House of Representatives, http://www.gao.gov/htext/d07632t.html (accessed September 16, 2009).

65.

Sturgeon

Will

. (2005). “Caesars Palace to Offer Net Phones, Biometric IDs.” CNET News, April 19, 2005. http://news.cnet.com/Caesars-Palace-to-offer-Net-phones%2C-biometric-IDs/2100-7355_3-5676821.html?tag=mncol (accessed May 7, 2009).

66.

Toh

Rex S.

Lee

Eunkyu

Michael Y.

(2006). “Social Desirability Bias in Diary Panels Is Evident in Panelists’ Behavioral frequency.” Psychological Reports, 99: 322-34.

67.

Tong

David Y. K.

(2009). “A Study of E-recruitment Technology Adoption in Malaysia.” Industrial Management & Data Systems, 109 (2): 281-300.

68.

Transportation Security Administration. (2006). “TSA Announces Key Elements of Registered Traveler Program.” http://www.tsa.gov/press/releases/2006/press_release_0645.shtm (accessed August 3, 2010).

69.

Transportation Security Administration. (2008). “TSA Lifts Cap and Eliminates Fee on Registered Traveler.” http://www.tsa.gov/press/releases/2008/0724.shtm (accessed August 3, 2010).

70.

Transportation Security Administration. (2009). “Registered Traveler. Our Approach.” http://www.tsa.gov/approach/rt/index.shtm (accessed August 3, 2010).

71.

Tsai

Rocky C.

(2007). “Biometrics in the Private Sector: Trends and Case Studies.” In The Practitioner’s Guide to Biometrics, edited by Coats

W. S.

Bagdasarian

Helou

T. J.

Lam

Chicago: American Bar Association (ABA) Publishing, pp. 139-46.

72.

United States General Accounting Office. (2002). “Aviation Security. Registered Traveler Program Policy and Implementation Issues, Report to the Honorable Kay Bailey Hutchison, U.S. Senate.” http://www.gao.gov/new.items/d03253.pdf (accessed February 22, 2010).

73.

Vatanasombut

Banphot

Igbaria

Magid

Stylianou

Antonis C.

Rodgers

Waymond

. (2008). “Information Systems Continuance of Web-Based Applications Customers: The Case of Online Banking.” Information & Management, 45 (2): 419-28.

74.

Venkatesh

Viswanath

Davis

Fred D.

(2000). “A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies.” Management Science, 46: 186-204.

75.

Venkatesh

Viswanath

Morris

Michael G.

Davis

Gordon B.

Davis

Fred D.

(2003). “User Acceptance of Information Technology: Toward a Unified View.” MIS Quarterly, 27: 425-78.

76.

Walczuch

Rita

Lemmink

Jos

Streukens

Sandra

. (2007). “The Effect of Service Employees’ Technology Readiness on Technology Acceptance.” Information & Management, 44 (2): 206-15.

77.

Wolfe

Daniel

. (2008). “User Milestone for a Biometric System.” American Banker, 173 (190): 10.

78.

Woodward

John D.

(2008). “The Law and the Use of Biometrics.” In Handbook of Biometrics, edited by Jain

A. K.

Flynn

Ross

A. A.

New York: Springer, pp . 357-79.