Information Disclosure to Biometric E-gates: The Roles of Perceived Security,Benefits,and Emotions

Abstract

Biometric authentication systems, such as electronic (e-)gates are increasingly important in air travel because of the growing traveler flows and security challenges. Such systems allow for accurate authentication and the improvement of the air travel experience, while enhancing the security of the overall travel system. To authenticate, the travelers are required to disclose biometric information. Grounded in equity and emotion theories and using data from 511 US air travelers, this study examined several antecedents of biometric information disclosure to e-gates. It was found that security perceptions and benefits of disclosure had strong impacts on disclosure, while positive and negative emotions influenced travelers’ perceptions of security.

Keywords

biometric systems information disclosure privacy emotions air travel

Introduction

Today’s security challenges, the increasing number of travelers (Bangwayo-Skeete and Skeete 2016), and the continuous changes in the economic environment and the airline competitive arena (Graham 2013) call for innovative solutions regarding the processing of traveler flows at US airports. In this context, the limited available resources require the deployment of new technologies that can expedite travelers’ processing (e.g., check-in, screening, boarding) (Morosan 2015) while offering value (Y. Wang, So, and Sparks 2016). Addressing such issues, many legacy air travel processes have been updated via automatic authentication technologies, resulting in substantial resource savings (US Customs and Border Protection 2012). For example, authentication for border entry has been automated using biographic+biometric authentication of travelers who are pre-enrolled in trusted traveler programs such as Global Entry (US Customs and Border Protection 2016). Other processes, such as the mandatory Transportation Security Administration (TSA) security screening, have benefited from the development of trusted traveler programs like TSA PreCheck and Clear, which rely on enrollment and background checks to classify travelers based on their security risk level (Transportation Security Administration 2016). At the foundation of many of the programs/technologies that allow such important legacy processes to be optimized lies the biometric technology.

Biometric technology facilitates user authentication based on one (i.e., single-modal) or multiple (i.e., multimodal) biometrics, typically combined with biographic information (Jain, Ross, and Nandakumar 2011). Biometrics are human characteristics that rarely/never change, and are unique to each individual (e.g., fingerprint, vascular patterns, speech, face/iris image, keystroke dynamics, etc.) (Jain, Ross, and Pankanti 2006). Although their history is long, the use of biometrics for automatic authentication within information systems (IS) is relatively recent. Also, within the array of biometric-based authentication systems, the utilization of biometrics in self-service systems is relatively recent (Morosan 2011). Many biometric systems applications authenticate users to automatically give them access to restricted areas (e.g., medical facilities, sterile areas of airports, national territories). Among such applications, biometric electronic gates (e-gates) have become increasingly common, because of their ability to accurately authenticate travelers based on their biometric and biographic information. E-gates can optimize resource deployment in airports, which results in a safer air travel system while offering benefits to travelers (e.g., predictable airport processing time).

E-gates allow users to enter restricted areas upon successful verification of biographic and biometric credentials, which are stored a priori either in secure databases or in travel documents (Gohringer 2015). Discrete deployments of e-gates have been seen in European Union countries (Perala 2016) and other world regions (e.g., Australia [“Ten Million Passengers” 2016]), leading to substantial resource savings (Bengaluru Airport 2015). E-gates are aligned with current “fast travel” initiatives, aiming at making the traveler flows less fragmented and reducing congestion in sensitive areas of airports (IATA 2013), with potential positive implications for the overall air travel system. Despite the purported benefits of e-gates for the air travel system, they have not been largely deployed in the US air travel. Initiatives of potential e-gate deployment have been discussed, but have not been materialized by the time of this research.

As is the case with other self-service technologies, it remains difficult to anticipate the e-gates’ adoption behaviors of US air travelers. The difficulty could be attributable to the complexity of the task that such systems facilitate, the requirements of task completion, and the task–technology environment. Relative to the rest of self-service technologies currently deployed, biometric e-gates are unique in that they require biometric information disclosure. Such information is irrevocable (i.e., a biometric cannot be replaced) and intimate (i.e., it can be descriptive of an individual’s medical conditions, uncontrollable traits/behavioral patterns) (Morosan 2011). The use of biometric information about/from users constitutes the foundation of e-gates’ accuracy, and their adoption is grounded in travelers’ willingness to disclose biometric information.

While disclosure of biometric information is indispensable to biometric system utilization, the literature has serious limitations in providing insight into consumers’ disclosure behaviors. This is attributable to the general lack of social science research on biometric system–oriented behaviors. Such behaviors are inherently difficult to examine, especially in systems that are not widely deployed yet (e.g., e-gates), and in settings where recording direct measures of behavior is unfeasible or legally restricted. In task–technology contexts outside biometric systems, scholars have addressed such limitations by utilizing surrogate measures of behavior (e.g., willingness, intentions). Among such measures, willingness to perform a behavior is recognized as a potent surrogate of actual behavior (Fishbein 2008), as (1) it captures an individual’s openness to performing a behavior (Pomery et al. 2008), and (2) it involves limited elaboration regarding the behavior/consequences (Gerrard et al. 2005).

In the context of limited deployment of e-gates, willingness to disclose biometric information represents the only viable construct capable of tapping into e-gate utilization behaviors for two main reasons. First, willingness to disclose represents among the only self-reported constructs of e-gate behavior measurable in a restricted/regulated environment. Second, willingness to disclose captures more accurately the voluntary setting of e-gate utilization, in contrast to mandatory settings (e.g., entering a national territory). In such voluntary settings, where alternative legacy traveler processing procedures still exist because of the limited deployment of e-gates (thus giving consumers a choice to avoid disclosure), consumers’ e-gate utilization is likely to form rather unrestrictedly. Such unrestricted development of behavior is likely more challenging to conceptualize in research, but better suited to explain consumers’ motivations and elaboration that can influence behavior.

While the literature documents research on personal information disclosure for commercial purposes (e.g., location information in Sun et al. [2015], personal information in social media in Nemec Zlatolas et al. [2015]), the literature lacks insight into the biometric disclosure behaviors of consumers, including air travelers. Yet, understanding the drivers of information disclosure is critical to the development of e-gates and the corresponding business models, which are instrumental to achieving a more efficient and secure air travel system. For example, more knowledge on consumers’ disclosure mechanisms could improve the design of the e-gates to facilitate a more convenient and faster biometric reading (e.g., using ergonomics) and biographic information provision (e.g., from travel documents, electronic artifacts such as apps), while information about e-gate utilization can be communicated to consumers prior to the trip (e.g., during booking or online check-in, via mobile apps). Moreover, knowing consumers’ disclosure mechanisms can help predict e-gate utilization, allowing the e-gate users to enjoy time-savings (i.e., faster processing, boarding) and a less fragmented airport experience, while helping travel organizations to redeploy resources to address critical areas of service. For example, airline/airport staff can add richer customer service experiences to traveler processing, while security organizations can redeploy staff to better fend off threats.

In this context, grounded in theoretical foundations offered by the equity theory (Adams 1983; Lee, Im, and Taylor 2008) and emotions theory (Lee-Wingate and Corfman 2011; Westbrook 1987), this study’s main goal was to develop and empirically validate a conceptual model to explain US air travelers’ willingness to disclose biometric information to e-gates. To this end, the study follows two specific objectives: (1) to validate several antecedents of willingness to disclose biometric information to e-gates and (2) to establish the role of emotions in developing security perceptions regarding e-gates.

Review of Literature

Theoretical Development

Most theoretical developments that explain IS utilization, especially in self-service contexts, remain based on classic and neo-classic theoretical foundations of IS adoption, in which users’ system perceptions are linked to attitudinal and behavioral outcomes (Venkatesh et al. 2003). While predominantly using cognitive artifacts (e.g., system perceptions), such theory was criticized for not being able to fully address the task–technology fit in specific industry and technological contexts (Benbasat and Barki 2007). As a result, the theory evolved by relying on theoretical constructs that are not native to IS, but originated in other social sciences (e.g., sociology, social psychology). As a result, the theory transcended into hybrid frameworks that are capable of better explaining the dynamics of IS use, including the conditions for adoption such as information disclosure.

As a part of the broader IS disciplinary base, the literature on information disclosure included models based on traditional adoption artifacts (e.g., system perception) in addition to adapted fundamental theoretical frameworks. Such frameworks included theoretical insight from equity theory (Adams 1983) and evolved versions of such theory like the privacy calculus theory (Dinev and Hart 2006), while retaining some of the belief–attitude–behavior links from models adapted from seminal work such as the theory of reasoned action (Li, Sarathy, and Xu 2011). To recognize the innate consumer characteristics, adaptations of culture theory (D.J. Kim 2008) and utility theory (Bansal, Zahedi, and Gefen 2010) also guided model development in studies on information disclosure in contexts that span from instant messaging and e-commerce to healthcare. To offer a comprehensive, yet parsimonious theoretical basis that addresses the goal and objectives of this study, the theoretical model development was guided by a number of important criteria. The model must have the ability to construct a theoretical foundation that accurately reflects the task–technology fit that characterize the utilization of e-gates in the US travel. Such a foundation must consider not only the characteristics of the IS themselves but also the specifics of the legacy business/administrative processes (e.g., security checks and consumer perceptions). Also, the model must be parsimonious and scalable for future extensions and replications. Finally, the model must allow for feasibility in operationalization. Accordingly, a number of theoretical foundations were considered in this study’s model development.

First, elements from the social exchange theory (Cropanzano and Mitchell 2005; Dinev and Hart 2006) were added (i.e., benefits) to capture the manner in which the consumers evaluate the e-gate task environment. While the social exchange theory recognizes the importance of conceptualizing the benefits–costs dyad (Chen 2013), constructs that tap better into the most important psychological costs of users (e.g., privacy concerns, perceived security, negative emotions) were utilized, as they accurately reflected the evaluative processes that result in system perceptions that can be viewed as surrogates of costs. Second, to reflect the manner in which the innate characteristics of consumers contribute to the evaluation of the task environment and eventually contribute to the decision to disclose biometric information, two constructs originating in the utility theory were added: convenience orientation and general privacy concerns (Malhotra, Kim, and Agarwal 2004).

Third, in order to offer a comprehensive view of the task environment by reaching beyond the traditional cognitive constructs typically incorporated in IS-based models and in line with the dual-valence view of emotions, two types of emotions (i.e., positive and negative) were added to the model as illustrated by emotion theory (Westbrook 1987). The inclusion of emotions was imperative, given (1) the recognition of the role of affective processing in cognitive processing (Blanchette and Richards 2010), (2) the inability of purely cognitive models to fully explain task–technology environments that are prone to stimulating affective episodes (Beaudry and Pinsonneault 2010) (e.g., the potential damaging consequences of unauthorized biometric information disclosure), and (3) the evidence offered by an increasing body of research utilizing emotions to better characterize IS adoption and related behaviors (e.g., Wakefield 2013).

Benefits of Disclosure

The literature on consumer information self-disclosure has developed as a result of increasingly common commercial practices that attempt to offer consumers personalized experiences, which are based on the information about their consumption preferences (Lee, Im, and Taylor 2008). Such literature documents a systematic effort dedicated to understanding the manner in which consumers decide to disclose information (e.g., Frye and Dornisch 2010; Lee-Wingate and Corfman 2011) based predominantly on theoretical insight developed in psychology (Andrade, Kaltcheva, and Weitz 2002; Cropanzano and Mitchell 2005) and focusing mostly on online retail (Metzger 2006) or social media tasks (Nemec Zlatolas et al. 2015). Such theory generally converges toward the notion that consumers engage in a calculus by weighing the benefits (e.g., personalized product attributes) and costs/risks (e.g., risk that private information could become public) when contemplating a disclosure decision (Lee, Im, and Taylor 2008). Such calculative processes are influenced by the type of information supposed to be disclosed, with more intimate information (e.g., medical) disclosure being associated with more risk (Rothstein and Talbot 2006).

Based on such calculative processes, consumers evaluate the perceived benefits of disclosure (Sun et al. 2015). Such evaluations have been studied predominantly in transactional contexts where the disclosure of personal information leads to receiving products tailored to specific consumer preferences (Metzger 2006). In such contexts, the role of perceived benefits in influencing disclosure has been found to be significant (Lee and Cranage 2011; Sun et al. 2015). The e-gates are designed to provide unique benefits to consumers that could be viewed as substantial relative to the benefits provided by the current legacy processes (e.g., waiting in line for screening and unpredictable processing time) Thus, the unique benefits associated with the utilization of e-gates within the well-defined context of airport security make the disclosure of biometric information to e-gates plausible. Accordingly, the following hypothesis was developed.

Hypothesis 1: There is a positive relationship between air travelers’ benefits of disclosure and their willingness to disclose biometric information to e-gates in airports.

Convenience Orientation

One major characteristic of the modern commercial environment is its convenience orientation. This concept is critical to consumers’ adoption of IS (Olsen and Mai 2013), and has been reflected in consumers’ perceptions (e.g., ease of use) that are central within the technology adoption theory (Venkatesh et al. 2003). Convenience orientation has a long tradition in consumer sciences, as it has been conceptualized to denote consumer characteristics that make them value efficiency in timely completion of a task (Morganosky 1986), reflected by time-saving practices and systems (W.T.J. Anderson 1972). While its conceptual definitions evolved over time, the contemporary literature retains some of its original meaning, and defines convenience orientation as the extent to which consumers seek time and effort savings with respect to a task (Olsen and Mai 2013). The recent literature recognizes the role of convenience orientation in shaping behavior (Scholderer and Grunert 2005), as it represents a domain-specific value that integrates within the value–attitude–behavior causal chain (Vaske and Donnelly 1999). Accordingly, convenience-oriented consumers would seek time savings in the tasks they are pursuing (Cullen 1994). Recently, biometric systems have been deployed in a substantial number of industries to offer users convenient and accurate authentication (Hartog and Munster 2008), thus allowing consumers to engage in behaviors that are consistent with the belief–attitude–behavior links previously discussed. Such systems have been designed to (1) optimize authentication, which is necessary for enhancing the security of the air travel system, and (2) to offer consumers convenient access to their flights. Thus, in the context of e-gates, the following hypothesis was developed.

Hypothesis 2: There is a positive relationship between air travelers’ convenience orientation and their willingness to disclose biometric information to e-gates in airports.

General Privacy Concerns

General privacy concerns reflect users’ concerns about their privacy in general (Li, Sarathy, and Xu 2011), which relate to their uneasiness related to the use of private information in commercial tasks (Westin 2003). While the literature recognizes the role of privacy concerns regarding the use of various IS (Stewart and Segars 2002), the conceptualization of privacy concerns took two forms: general privacy, reflecting general views of privacy associated with all IS (Li, Sarathy, and Xu 2011), and system-specific privacy concerns, which denote the specific beliefs regarding the capability of a specific IS to protect privacy (Paine et al. 2007). This study uses the first conceptualization of privacy concerns, viewing them as innate consumer characteristics that span beyond a specific system’s use, thus being capable of explaining how consumers make disclosure decisions based on environmental factors (Thatcher and Perrewé 2002).

Not all private information is regarded as equal by the consumers, who recognize that the disclosure of certain kinds of private information (e.g., medical and financial) is more risky than that of others (e.g., demographic, lifestyle, habits) (Malhotra, Kim, and Agarwal 2004). Individuals concerned about the risk associated with their behaviors typically engage in prevention-focused behaviors, such as preventive measures and disclosure refusal (Lwin, Wirtz, and Williams 2007). Consumers who are innately concerned about their privacy are likely to view biometric information as highly private and engage in behaviors that prevent its disclosure (Smith, Milberg, and Burke 1996). Yet, utilizing private information is instrumental to providing benefits to consumers, which are fundamental to the evaluation of their consumption experiences (Sutanto et al. 2013). The trade literature offers examples of biometric contexts in which consumers’ general concerns regarding the privacy of biometric information required influenced major deployment decisions (Lancelot Miltgen, Popovič, and Oliveira 2013). Thus, based on the discussion above, the following hypothesis was developed.

Hypothesis 3: There is a negative relationship between air travelers’ general privacy concerns and their willingness to disclose biometric information to e-gates in airports.

Perceived Security

Disclosure of information can also be a function of the possible negative consequences of information disclosure (e.g., identify fraud) (Lee and LaRose 2011). The type of information disclosed has important connotations for the manner in which information is disclosed (Sprague 2006). For example, information that is descriptive of a person’s private life (e.g., possible medical conditions) can enhance the perceptions of risk associated with disclosure (Rothstein and Talbot 2006). Generally, biometric information can reveal private information about an individual (e.g., certain medical conditions) (Morosan 2012); therefore, the disclosure of such information is associated with a certain degree of risk. This risk may be related to users’ perceptions of system utility (Lui and Piccoli 2010), which could reflect functional system attributes that allow the system to facilitate the task (e.g., accurate authentication), but also core aspects such as security. Perceived security has been extensively studied in various IS task contexts, including tourism (Escobar-Rodríguez and Carvajal-Trujillo 2014), and found to be related to intentions to engage in behaviors toward an IS (Huang et al. 2011). In such scenarios, users’ perceptions of risk become dependent on the capability of an IS to attenuate such risk (Huang et al. 2011). Given that e-gates perceptions of security reflect the security risk of users’ private information, users’ perceptions of security regarding biometric information disclosure to such systems is likely to influence their disclosure intentions, according to the following hypothesis.

Hypothesis 4: There is a positive relationship between air travelers’ perceived security of e-gates and their willingness to disclose biometric information to e-gates in airports.

Emotions

Emotions represent an individual’s mental states that change action readiness and prioritize and organize behavior so that the individual’s organism responds properly to environmental stimuli (Lazarus 1991). The important role of emotions in shaping behavior has been increasingly recognized in the business literature, as illustrated by the constant development of theory that incorporates emotion-related concepts as antecedents of commercial behavior (Agarwall and Malhotra 2005). For example, consumers’ use of innovations was found to be associated with stimulating emotions (Wood and Moreau 2006). This orientation is grounded in the notion that adding affective artifacts to cognitive-based research illustrates more comprehensively the psychological mechanisms that influence behavior. Beyond the mainstream literature, IS scholars have also recognized the importance of the affective artifacts and the potential implications for improving the design and management models of IS (Zhang 2013).

Accordingly, the IS literature seems to agree that technology stimulates emotions (Rafaeli and Vilnai-Yavetz 2004) and that such emotions elicit further development of critical system perceptions (e.g., usefulness), as well as attitudinal and behavioral responses (Beaudry and Pinsonneault 2010). Accordingly, the IS literature adapted its theoretical models to incorporate emotion-related artifacts (Beaudry and Pinsonneault 2010). For example, it was found that enjoyment influences the perceptions of IS usefulness (Venkatesh 2000) and IS use (H.W. Kim et al. 2004), while computer-mediated communication anxiety influences the attitudes toward IS use (Brown, Fuller, and Vician 2004). While the literature investigating the role of emotions in the adoption of IS focuses on ambivalent emotions (e.g., H.W. Kim et al. 2004), several discrete emotions have been conceptualized in IS-related studies (e.g., enjoyment in Koufaris [2002], anxiety in Venkatesh et al. [2003]) and found to be significant influencers of behavior (Beaudry and Pinsonneault 2010).

A predominant view in the study of emotions is that emotions have a valence (i.e., positive vs. negative), according to which various types of emotions can be classified (Westbrook 1987). For example, happiness, joy, and contentment have been viewed as positive emotions while unhappiness, worry, anger, and nervousness have been viewed as negative (Lerner and Keltner 2000). While a direct link between emotions and intentions to disclose information to a biometric IS has not been validated to date, the literature points out that valence-conceptualized emotions tend to influence behaviors that are congruent with their valence (Lazarus 1991). That is, positive emotions tend to influence behavior toward a target object (i.e., system, brand) or behavior (e.g., purchasing, using a system) by influencing perceptions (Lerner and Keltner 2000), while the negative emotions influence the behavior away from the target object or behavior (Venkatesh et al. 2003). This is grounded in the thesis that the emotional consequences of performing a target behavior are considered when individuals contemplate performing that behavior (Bagozzi, Baumgartner, and Pieters 1998; C. Xie, Bagozzi, and Østli 2013).

Given their purported benefits and the information they require, e-gates are likely to stimulate emotions, which may influence critical system perceptions (Venkatesh 2000). A critical system perception is represented by perceived security (Morosan 2011), given the sensitivity of the biometric information and the role that e-gates have in safeguarding such information while providing it to the air travel systems stakeholders (Morosan 2015). Positive emotions can be stimulated by evoking benefits such as predictable processing of airport tasks and enhanced security of the overall travel system, while negative emotions can be stimulated as a result of the irrevocable and intimate nature of biometric information (e.g., inability to change biometric credentials if compromised) and the potential consumers’ feelings of a lack of control over the management of their biometric information postdisclosure. In such contexts, positive and negative emotions are likely to influence consumers’ perceptions of security of e-gates, according to the following hypotheses.

Hypothesis 5: There is a positive relationship between air travelers’ positive emotions regarding the use of e-gates and their perceived security of e-gates in airports.

Hypothesis 6: There is a negative relationship between air travelers’ negative emotions regarding the use of e-gates and their perceived security of e-gates in airports.

Methodology

Instrument Development

The data were collected using a survey instrument, which was designed by adapting scales from the existing literature. The scales were used in previous studies to capture similar task–technology environments, and were validated based on appropriate psychometric measures. Specifically, the scale for benefits of disclosure (three items) was adapted from Xu et al. (2011), where it was used to measure the benefits of m-coupon utilization. The scale for convenience orientation (three items) was adapted from the work of Olsen and Mai (2013), where it was validated as a measure of convenience of meal preparation. The scale for general privacy concerns (three items) was adapted from Li, Sarathy, and Xu’s (2011) work on information disclosure to unfamiliar web-based vendors. Perceived security was measured using three items, which were adapted from Wakefield’s (2013) scale that was validated in the context of web purchasing. The scale for emotions (three items) was adapted from Li, Sarathy, and Xu (2011), where the emotions related to disclosure of information to unfamiliar web-based vendors were measured using a scale developed based on the work of Shaver et al. (1987). Willingness to disclose information (three items) was adapted from the work of Xu et al. (2011), where the items were validated as measures of willingness to disclose information in m-coupon contexts. All items were Likert-type anchored in five points, ranging from 1 (strongly disagree) to 5 (strongly agree), except for the willingness to disclose scales, which were semantic differential. The Appendix illustrates the items measuring each latent construct.

Instrument Administration

The survey was posted on the Qualtrics survey environment. Data were collected in September 2015 from a nationwide sample of American travelers. The sample was accessed using the services of a large global marketing panel firm. The panel firm manages extensive consumer panels. Such consumers receive invitation emails from the panel firm on behalf of researchers to participate in surveys in exchange for compensation. For this study, general American population panels were selected and a filtering question was used so that only consumers who have taken a trip by air in commercial aviation during the past 12 months prior to the study would be allowed to access the survey. An initial e-mail inviting potential respondents to participate in the study was sent to 3,000 individuals, and after filtering, a total sample of 538 respondents was collected. Upon removing the cases that had systematic missing values, the final data set included 511 respondents. As all self-reported survey data is subject to nonresponse bias, the data in the study were subjected to a nonresponse bias analysis (Ary, Jacobs, and Razavieh 1996), where earlier respondents were compared with later respondents. As no differences were found between the two groups of respondents, it was concluded that the data were not characterized by nonresponse bias (Ary, Jacobs, and Razavieh 1996).

Results

Preliminary Analyses

A preliminary analysis checked if the data conformed to a multivariate normal distribution, based on the procedure recommended by Mardia (1970). While the individual variables followed univariate normal distributions, the overall data set did not conform to a multivariate distribution. Therefore, the confirmatory factor analysis and the subsequent structural equation modeling analysis used estimators that were robust to deviations from multivariate normality (Muthén and Muthén 2003). Common method bias was also assessed by constructing a model that allowed all items to load on a single latent factor. The model had a very poor fit, which indicated that common method bias was not a problem in this study (Malhotra, Kim, and Patil 2006). The demographic and behavioral variables were analyzed to establish a demographic and behavioral profile of the respondents (Table 1). The gender distribution was almost even, with 52.3% females, while the age distribution was similar to that of the general US population. That is, all age brackets were similar to those of the US general population as illustrated by the US Census Bureau (2013), except for the youngest and oldest respondents brackets, who were, respectively, slightly underrepresented and overrepresented by approximately 10% relative to the US Census data. Most respondents had incomes between $50,001 and $100,000 per year and almost half of them had a bachelor’s degree or equivalent. The behavioral profile revealed that most respondents had not used biometric technology (76%). Most respondents traveled by air one to two times a year, mostly for leisure purposes.

Table 1.

Demographic and Behavioral Profile of Respondents.

Variable	Percent
Gender
Male	47.7
Female	52.3
Age
25 or younger	1.7
26–30	5.7
31–40	15.3
41–50	21.4
51–60	20.6
61–70	25.0
71 or older	10.2
Annual household income
$50,000 or less	17.1
$50,001–$100,000	44.7
$100,001–$150,000	24.1
$150,001–$200,000	8.8
$200,001 or more	5.3
Education
High school degree or equivalent	16.5
Bachelor of Science/Arts or equivalent	49.6
Master’s degree or equivalent	22.5
Doctoral degree or equivalent	4.3
Medical or law degree or equivalent	4.1
Other	2.9
Have used biometric technology
Yes	24.0
No	76.0
Frequency of air travel
Less than once a year	5.7
Approximately 1–2 times a year	41.9
Approximately 3–8 times a year	38.3
Approximately once a month	8.5
Approximately once a week	4.7
More than once a week	.8

Model Analyses

In order to ascertain the psychometric properties of the instrument, a confirmatory factor analysis (CFA) was conducted, followed by a structural equations model (SEM) analysis to test the model’s hypotheses. The analyses were conducted using the Mplus v.5 statistical package (Muthén and Muthén 2003). While the model fit analyses in CFA/SEM rely on the calculation of a chi-squared value, such a value varies with sample size, and thus it may be misleading. Therefore, an alternative measure of fit has been calculated, by dividing the chi-squared value by the model’s degrees of freedom to obtain a “normed chi-squared” (Hair et al. 2009). Normed chi-square values under 3 generally indicate appropriate fit (Hair et al. 2009). The analysis of the measurement model (and the subsequent research model) fit was based on a series of absolute and incremental indexes, which together helped determine how well a model fits the data. The measurement model had an acceptable fit, being characterized by the following fit indicators: chi-square = 305.227 (df = 168); normed chi-square = 1.81; comparative fit index (CFI) = .98; Tucker–Lewis index (TLI) = .97; and root mean standard error of approximation (RMSEA) = .041 (Hair et al. 2009).

The analysis continued with the examination of item loadings, to obtain insight into the instrument’s reliability and validity. To examine each latent construct’s reliability, the composite construct reliabilities for each latent construct were calculated and were found to exceed .8, indicating strong reliability (Hair et al. 2009). Convergent validity was assessed using the magnitude of item loadings (Tables 2 and 3). They were found to exceed .7 (except for one item, which was within 3% of .7) and were significant, thus indicating appropriate convergent validity (Fornell and Lacker 1981). In addition, the average variance extracted (AVE) for each latent construct exceeded .5, supporting the appropriate convergent validity of these constructs (Browne and Cudeck 1992). To establish discriminant validity, the AVE scores for each latent construct were compared with corresponding pairs of squared interconstruct correlations. All latent constructs’ AVE scores were greater than the corresponding interconstruct correlations, demonstrating appropriate discriminant validity (Fornell and Lacker 1981).

Table 2.

Reliability and Validity Test Results.

Constructs	Items	Loadings	CCR*
Benefits	1	.825	.810
	2	.847
	3	.613
Convenience orientation	1	.932	.852
	2	.811
	3	.676
General privacy	1	.764	.808
concerns	2	.764
	3	.763
Perceived security	1	.899	.926
	2	.898
	3	.896
Emotions—positive	1	.897	.944
	2	.944
	3	.921
Emotions—negative	1	.857	.913
	2	.897
	3	.890
Willingness to disclose	1	.891	.932
	2	.908
	3	.919

Note: CCR = composite construct reliabilities.

Table 3.

Validity Test Results.

Constructs		1	2	3	4	5	6	6
1	Benefits of disclosure	.591
2	Convenience orientation	.376	.661
3	General privacy concerns	.457	.005	.584
4	Perceived security	.202	.197	.005	.806
5	Emotions - positive	.300	.045	.005	.415	.848
6	Emotions - negative	.202	.111	.043	.327	.142	.777
7	Willingness to disclose	.523	.248	.013	.672	.376	.343	.821

Note: The values above the diagonal (in bold) represent the average variance extracted (AVE) values for each latent construct. The values under the diagonal represent squared interconstruct correlations.

Once the psychometric properties of the instrument were validated, the analysis continued with a SEM analysis to test the study’s hypotheses (Muthén and Muthén 2003). The model had appropriate fit, characterized by the following indicators: chi-square = 374.501 (df = 173), normed chi-square = 2.01, CFI = .97, TLI = .96, and RMSEA = .049 (Hair et al. 2009). All the hypotheses proposed in this study were supported in their predicted direction, except for hypothesis 2 (Figure 1). Of the four predictors of willingness to disclose, perceived security was the strongest (γ = .575, p<.001), followed closely by benefits (β = .519, p<.001). Perceived privacy had a relatively lower, yet significant, effect (β = –.205, p = .011), while perceived security did not significantly impact willingness to disclose. As expected, the positive and negative emotion constructs had relatively similar but opposite effects on perceived security (positive emotion: β = .493, p < .001; negative emotion: β = –.392, p < .001).

Figure 1.

Model testing results.

Discussion

The study set out to explain US travelers’ willingness to disclose biometric information to e-gates. The conceptual model was validated empirically, providing support that this particular model is appropriate for the study of willingness to disclose biometric information. One critical objective was to validate the antecedents of willingness to disclose biometric information. All the antecedents have been empirically validated, except for convenience orientation. Not surprisingly, the strongest predictor was perceived security, which is in line with previous research in various contexts documenting the role of various business model elements (e.g., privacy statements in Hui, Teo, and Lee 2007) in stimulating information disclosure. This result indicates that system perceptions such as those related to the security of biometric systems represent important factors that facilitate biometric information disclosure. Moreover, the magnitude of the effect of perceived security on the willingness to disclose underscores the critical role of the core functional aspects of IS that facilitate consumer behavior. That is, the ability of the system to convey the information and preserve its integrity reflects the importance for the consumers of these system perceptions.

The other strong predictor of willingness to disclose was represented by the benefits of disclosure. The strength of the relationship aligns only partially with the existing literature, which documented results ranging from low (e.g., Lee and LaRose 2011) to high (e.g., Sun et al. 2015) effects of perceived benefits on information disclosure, and confirms that the extent to which consumers understand how the system facilitates the task contributes to their willingness to disclose information. Moreover, this result, corroborated with the strong relationship between perceived security and willingness to disclose, emphasizes that the willingness to disclose is linked to the ability of the e-gate to facilitate the development of a better travel experience for travelers. In other words, if e-gates allow travelers to access their flights simpler, faster, and more conveniently, and are seen as beneficial, travelers will disclose biometric information to such systems. This result stands out from the existing literature as it emphasizes the general consumption benefits, to which information disclosure represents a fundamental condition for task completion.

Another interesting result was the relationship between general privacy concerns and willingness to disclose. This relationship was negative and low in magnitude, and was in line only with some of the limited literature that conceptualized general privacy concerns (e.g., Awad and Krishnan 2006; Li, Sarathy, and Xu 2011). Most literature relied on conceptualizations of IS-specific (e.g., Nemec Zlatolas et al. 2015; Wirtz and Lwin 2009) or information type–specific (Bansal, Zahedi, and Gefen 2010) privacy concerns, which were found to have effects ranging from low to high on disclosure and coping behavior (Dinev and Hart 2006). That is, in the context of e-gates, air travelers characterized by general privacy concerns could manifest such concerns by not disclosing biometric information to e-gates, but such an effect could be offset by the benefits of disclosure and accentuated by the perceptions of security associated with the e-gates. Thus, even consumers who generally exhibit privacy concerns vis-à-vis the use of technology in commercial contexts could disclose biometric information if the benefits of such disclosure are clear and the e-gates are characterized by perceptions of security. A somehow surprising result was the not significant role of convenience orientation. This result positions itself uniquely in the existing literature, which suggests a direct relationship between convenience orientation and willingness to disclose grounded in the theoretical link between the functional attributes of the system, which manifest through saving time, and the behavior associated with such systems. This result also emphasizes that regardless of the level of convenience offered, travelers will rely more on the overall benefits of utilization of such systems and their primary concerns (i.e., security, privacy) when facing a disclosure decision.

The second objective of this study was to establish the role of emotions in shaping the perceptions of security. The two-valenced emotion constructs were empirically validated as significant predictors of perceived security. While both constructs had relatively similar effects on perceived security, the role of positive emotions was slightly higher than that of negative emotions. That is, in a travel context, the rather heuristic evaluations of such systems that may manifest through the different types of emotions are important to the evaluation of functional aspects of e-gate information disclosure. As direct relationships between emotions and perceptions of security have not been found in the literature, there is no basis for the comparison of these relationships. Yet, while the role of negative emotions could be offset by the development of positive emotions, it is important to recognize that consumers’ emotional charge influences the extent to which they believe that e-gates constitute secure systems to which they can disclose information.

Contributions

Theoretical Contributions

First, this study offers a unique perspective on the study of the social aspects of biometric systems, and positions uniquely within the IS literature focusing on biometrics, which is predominantly technical (Kim and Bernhard 2014). This study also represents one of the initial few studies explaining how travelers perceive biometric systems that are designed to enhance the security and efficiency of the air travel system in the United States, which has unique characteristics (e.g., high volume, technology-intensive) but also critical security challenges. It shows a perspective of biometric systems that is associated with unique privacy and security challenges, but addresses a task that is uniquely designed to make the travel system more efficient while enhancing its overall security. As such, this study advances the travel IS literature, which focuses predominantly on transactional aspects of IS utilization, such as distribution (Stangl, Inversini, and Schegg 2016) or consumer interfaces for purchasing (Bonsón Ponte, Carvajal-Trujillo, and Escobar-Rodríguez 2015).

Second, this study examines a task environment where the consumers disclose biometric information repeatedly for authentication purposes, while the initial disclosure is necessary for enrollment. As such, it distinguishes itself from the literature that discusses either the first time–only or continuous disclosure (Lee and LaRose 2011; Zimmer et al. 2010). Moreover, this study reflects the benefits that could be extended to air travelers, in a system that requires close scrutiny because of serious security concerns. Thus, given the uniqueness of biometric information that is necessary to be disclosed to utilize e-gates, this study distinguishes itself from most of the IS disclosure literature, which examines generally revocable information (e.g., personal information in blogs in Lee, Im, and Taylor [2008], geographical location in Sun et al. [2015]) or tasks that influence only the consumer (Zimmer et al. 2010). As a result, the study marks an important step in advancing the IS literature beyond adoption, and provides the complementary social science perspective that is instrumental to the understanding of biometric system design, deployment, and adoption.

Third, this study conceptualized benefits more generically and thus positions itself uniquely within the IS literature, which recognizes relationships between tangible (e.g., monetary) benefits, the role of motivations for disclosure (Hui, Tan, and Goh 2006), and information disclosure (Hui, Teo, and Lee 2007), even when such disclosure is selective based on the type of information disclosed (E. Xie, Teo, and Wan 2006). It retained the benefits construct that reflected more comprehensively the complexity of the air travel task environment and the potential motivations of travelers for using e-gates. This conceptualization allowed for a better examination of the distinct effects of benefits, privacy concerns, and security perceptions on willingness to disclose, and marks a substantial departure from previous studies where the benefits and costs were conceptualized as a dyad (Lee and LaRose 2011).

Fourth, this study conceptualizes a unique disclosure context, where disclosure is unmediated by humans and results in information that could be accessed only under highly restrictive conditions. In this respect, this study departs from the literature that conceptualizes disclosure, where information disclosed is designed to be accessed/analyzed by humans in order to enhance consumption experiences and increase value-in-use. Moreover, this study reflects a task where disclosure results in an enhancement of the core product, which contrasts with most literature that reflects enhancements in the augmented product, for example, via personalization (Awad and Krishnan 2006). Moreover, this study also positions against a vast body of literature on disclosure that reflects information that remains public (e.g., social media) (Nemec Zlatolas et al. 2015), where information, while still irrevocable, remains publicly visible.

Finally, this study incorporates emotions as antecedents of perceived security, which marks an important step forward in the advancement of the IS research, which is based predominantly on cognitive artifacts that explain various aspects of IS utilization. By adopting a valenced and anticipatory conceptualization of emotions, this study contributes to the advancement of knowledge in an area of the IS literature pioneered by C.L. Anderson and Agarwal (2011) that focuses on the unique role of emotions in consumers’ calculative benefit-risk processes that they face when contemplating disclosure decisions. Thus, by focusing in premiere on the emotions related to the utilization of e-gates (i.e., upon disclosure of intimate and highly descriptive personal information such as biometrics), this study helps clarify the role of emotions and expands the body of knowledge beyond medical information (Loewenstein et al. 2001).

Practical Contributions

As one of the first studies focusing on the disclosure of biometric information to e-gates, this study offers several important actionable contributions for practitioners. First, the study provides a mapping of the factors that lead to biometric information disclosure to e-gates, which represents important insight for system developers and marketers. Such knowledge allows them to focus on certain system features that are critical to consumers and can be incorporated in the design and user interfaces of such systems. Second, the critical role of perceived security has been validated as the strongest antecedent of disclosure in the study. Accordingly, decision makers can specifically emphasize how security is addressed in the e-gates context, and how the systems are designed to protect the transfer of information from the point of data collection (disclosure) throughout the processing sequence until the consumers can appropriate the benefits of disclosure (e.g., access to security checks, boarding).

In addition, given the important role of emotions, the decision makers can use emotional appeals in the advertising and other commercial information that is dedicated to stimulating travelers’ use of e-gates. The relatively high impact of both positive and negative emotions on perceived security allow decision makers to choose from a wide range of emotions the most appropriate ones to emphasize in public communication in order to stimulate the perceptions of security and overall willingness to disclose biometric information. This is especially important as emotions could play a critical role and disclosing this specific type of information—biometric—as (1) they are still not widely adopted in travel in the United States, especially domestically, and (2) biometric information can be associated with negative emotional responses due to factors such as previous use in domains such as law enforcement or culturally nuanced behavioral responses to collection or dissemination of biometric information.

Finally, general privacy concerns only had a low impact and the willingness to disclose. The current society, in which information security breaches and their negative consequences become increasingly common, can, in theory, contribute to an intensification of consumers’ general privacy concerns regarding IS. As disclosure of biometric information involves information that has critical implications if compromised, such general privacy concerns may influence the manner in which consumers react to biometric IS. However, this study shows that this impact is only minor on the willingness to disclose biometric information to e-gates, which is encouraging for both developers and decision makers in air travel.

Limitations and Directions for Further Research

Because of its nature (e.g., conceptualization of an IS that is in its incipient phases of deployment in the United States, self-reported data), the study has several limitations. Thus, the results should be considered in their appropriate context.

The main dependent variable was willingness to disclose, which represents a limitation because of the construct’s focus on capturing the manner in which individuals are open to the opportunity of disclosure. Thus, while important in understanding disclosure, willingness to disclose remains a surrogate measure of behavior (Fishbein 2008). Yet, this approach was necessary in this study given the restrictive and voluntary e-gate disclosure context. If contextual restrictions allow, future research should investigate additional behavioral variables (e.g., behavioral expectations, actual behavior) that, together with willingness to disclose, form a comprehensive theoretical foundation for understanding consumers’ disclosure behaviors.

This study was built on theoretical grounds that considered only several antecedents of willingness to disclose. While this approach was necessary to enhance the feasibility of the research setting, it represents a limitation because of the possibility of omitting constructs that may provide insight into the formation of willingness to disclose. Thus, future research should validate additional antecedents of willingness to disclose. For example, as utilization of e-gates requires multiple disclosure episodes, future research should examine the role of consumers’ experience in disclosure and how disclosure and utilization of e-gates can be linked to other benefits that rely on the same legacy repetitive processes (e.g., trusted traveler programs, membership in airline loyalty/rewards programs).

A significant relationship between convenience orientation and willingness to disclose was not validated in the context of e-gates. Thus, this study is limited in explaining how consumers’ convenience orientation—that may develop in response to current business models (e.g., fast check-in, self-boarding) and social norms in which convenience is emphasized—influences IS use that requires disclosure. Thus, future research should focus on validating possible moderators of the relationship between convenience and disclosure (e.g., benefits, trust, or innovativeness), which could influence behavior (W. Wang, Cole, and Chen 2017). This way, this study and its follow-up research can build an extensive basis for explaining how convenience-driven behaviors manifest in contemporary air travel.

This study was restricted to the biometric modalities most commonly used in e-gates (e.g., fingerprints, face images), in addition to biographic information. This measure was taken to allow for more control over the factors that are extraneous to this research setting. Yet, it represents a limitation, given the potential different perceptions that consumers may develop with respect to other possible modalities (e.g., vascular patterns, iris scans) that may be used in future multimodal biometric systems. Thus, as multimodal biometric systems develop, an important direction for further research is to examine how disclosure occurs across specific biometric modalities. Consequently, this study could stay at the foundation of research streams that elucidate the differential roles of various biometric modalities in shaping disclosure behavior.

A final limitation is that the sample’s age structure was slightly different from that of the general US population. Future research should focus on highly generalizable samples, where sampling can be done in line with the US general population structures. In addition, a relatively smaller percentage of this study’s respondents had used biometric systems, which limited the possibility of examining the differences between users and nonusers. Yet, as biometric systems are increasingly penetrating day-to-day life (e.g., mobile device access, time/attendance systems), opportunities could arise for research investigating the actual biometric system users and user–nonuser differences, thus providing insight into the dynamics of biometric system adoption. Such research directions can facilitate the development of important research streams (e.g., segmentation, latent class/profile analyses), which together can provide the theoretical insight that is necessary to explicate the differential adoption patterns of various population groups.

This study uncovers additional important directions for further research. For example, the role of consumers’ education may serve as an interesting avenue for further research, which can help gauge how consumers with various levels of education form IS perceptions and disclosure behaviors. Additionally, it would be critical to examine the role of communication between organizations managing the e-gates and consumers. For example, information provided prior to travel (e.g., at the time of booking or online check in) may influence the perceptions that consumers form within the course of the airport experience and their general IS perceptions.

General Conclusion

As this study is among the first studies to examine US air travelers’ willingness to disclose biometric information to e-gates, it is important to recognize the significant role of travelers’ perceptions of security, and their evaluation of the benefits of disclosure. Positive and negative emotions also contributed to the development of travelers’ perceptions of security, while general privacy concerns had only a modest impact. By explaining the differential impacts of the various cognitive and affective processes that consumers go through when they contemplate disclosing biometric information to e-gates, this study marks a substantial step forward in understanding the social aspects of the utilization of biometric systems in air travel.

Footnotes

Appendix

Declaration of Conflicting Interests

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

Funding

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

Author Biography

Cristian Morosan is an associate professor in the Conrad N. Hilton College of Hotel & Restaurant Management at the University of Houston. His research interests include biometric systems, mobile-commerce, and e-marketing.

References

Adams

J. S.

1983. “Towards an Understanding of Inequity.” Journal of Abnormal and Social Psychology 67 (5): 422–35.

Agarwall

Malhotra

N. K.

2005. “An Integrated Model of Attitude and Affect: Theoretical Foundations and an Empirical Investigation.” Journal of Business Research 58:483–93.

Anderson

C. L.

Agarwal

2011. “The Digitization of Healthcare: Boundary Risks, Emotion, and Consumer Willingness to Disclose Personal Health Information.” Information Systems Research 22 (3): 469–90.

Anderson

W. T. J.

1972. “Convenience Orientation and Consumption Behavior.” Journal of Retailing 48:49–71.

Andrade

E. B.

Kaltcheva

Weitz

2002. “Self-Disclosure on the Web: The Impact of Privacy Policy, Reward, and Company Reputation.” Advances in Consumer Research 29:350–53.

Ary

Jacobs

Razavieh

1996. Introduction to Research in Education, 5th ed. Ft. Worth, TX: Holt, Rinehart, and Winston.

Awad

N. F.

Krishnan

M. S.

2006. “The Personalization-Privacy Paradox: An Empirical Evaluation of Information Transparency and the Willingness to be Profiled Online for Personalization.” MIS Quarterly 30 (1): 13–28.

Bagozzi

R. P.

Baumgartner

Pieters

1998. “Goal-Directed Emotions.” Cognition and Emotion 12:1–26.

Bangwayo-Skeete

P. F.

Skeete

R. W.

2016. “Who Travels Visa-Free? Insights into Tourist Hassle-Free Travel.” Journal of Travel Research 56 (3): 407–18.

10.

Bansal

Zahedi

F. M.

Gefen

2010. “The Impact of Personal Dispositions on Information Sensitivity, Privacy Concern and Trust in Disclosing Health Information Online.” Decision Support Systems 49 (2): 138–50.

11.

Beaudry

Pinsonneault

2010). “The Other Side of Acceptance: Studying the Direct and Indirect Effect of Emotions on Information Technology Use.” MIS Quarterly 34 (4): 689–710.

12.

Benbasat

Barki

2007. “Quo vadis, TAM.” Journal of the Association of Information Systems 8 (4): 211–8.

13.

Bengaluri Airport. 2015. “News/Press Release: Kempegowda International Airport, Bengaluru (KIAB) Starts Trials for Introducing E-gates System in India.” http://www.bengaluruairport.com/bial/faces/pages/ourBusiness/mediaCenter.jspx?_adf.ctrlstate=uqonwig8p_284&_afrLoop=1473180950635418.

14.

Blanchette

Richards

2010. “The Influence of Affect on Higher Level Cognition: A Review of Research on Interpretation, Judgment, Decision Making and Reasoning.” In Cognition and Emotion: Reviews of Current Research and Theories, edited by de Houwer

Hermans

, 276–324. New York: Psychology Press.

15.

Bonsón Ponte

Carvajal-Trujillo

Escobar-Rodríguez

2015. “Influence of Trust and Perceived Value on the Intention to Purchase Travel Online: Integrating the Effects of Assurance on Trust Antecedents.” Tourism Management 47:286–302.

16.

Brown

S. A.

Fuller

R. M.

Vician

2004. “Who’s Afraid of the Virtual World? Anxiety and Computer-Mediated Communication.” Journal of the Association for Information Systems 5 (2): 79–107.

17.

Browne

M. W.

Cudeck

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

18.

Chen

2013. “Member Use of Social Networking Sites—An Empirical Examination.” Decision Support Systems 54 (3): 1219–27.

19.

Cropanzano

Mitchell

2005. “Social Exchange Theory: An Interdisciplinary Review.” Journal of Management 31 (6): 874–900.

20.

Cullen

1994. “Time, Tastes and Technology: The Economic Evolution of Eating Out.” British Food Journal 96 (10): 4–9.

21.

Dinev

Hart

2006. “An Extended Privacy Calculus Model for E-commerce Transactions.” Information Systems Research 17 (1): 61–80.

22.

Escobar-Rodríguez

Carvajal-Trujillo

2014. “Online Purchasing Tickets for Low Cost Carriers: An Application of the Unified Theory of Acceptance and Use of Technology (UTAUT) Model.” Tourism Management 43:70–88.

23.

Fishbein

. 2008. “A Reasoned Action Approach to Health Promotion.” Medical Decision Making 28:834–44.

24.

Fornell

Lacker

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

25.

Frye

N. E.

Dornisch

M. M.

2010. “When Is Trust Not Enough? The Role of Perceived Privacy of Communication Tools in Comfort with Self-disclosure.” Computers in Human Behavior 26 (5): 1120–27.

26.

Gerrard

Gibbons

F. X.

Stock

M. L.

Vande Lune

L. S.

Cleveland

M. J.

2005. “Images of Smokers and Willingness to Smoke among African American Pre-adolescents: An Application of the Prototype/Willingness Model of Adolescent Health Risk Behavior to Smoking Initiation.” Journal of Pediatric Psychology 30:305–18.

27.

Gohringer

2015. “The Application of Face Recognition in Airports.” Biometric Technology Today, July/August 2015:5–9.

28.

Graham

2013. “Understanding the Low Cost Carrier and Airport Relationship: A Critical Analysis of the Salient Issues.” Tourism Management 36:66–76.

29.

Hair

J. F.

Black

W. C.

Babin

B. B.

Anderson

R. E.

2009. Multivariate Data Analysis, 7th ed. Upper Saddle River, NJ: Prentice Hall.

30.

Hartog

J. D.

Munster

R. V.

2008. “Biometrics at the Turnstiles.” Computer Weekly 156:20–22.

31.

Huang

D. L.

Patrick Rau

P. L.

Salvendy

Gao

Zhou

2011. “Factors Affecting Perception of Information Security and Their Impacts on IT Adoption and Security Practices.” International Journal of Human-Computer Studies 69 (12): 870–83.

32.

Hui

K. L.

Tan

B. C. Y.

Goh

C. Y.

2006. “Online Information Disclosure: Motivators and Measurements.” ACM Transactions on Internet Technology 6 (4): 415–41.

33.

Hui

K. L.

Teo

H. H.

Lee

S. Y. T.

2007. “The Value of Privacy Assurance: An Exploratory Field Experiment.” MIS Quarterly 31 (1): 19–33.

34.

IATA. 2013. “Program Strategy. Fast Travel Program.” https://www.iata.org/whatwedo/passenger/fast-travel/Documents/Fast-Travel-Program-Strategy-V6.pdf.

35.

Jain

A. K.

Ross

A. A.

Nandakumar

eds. 2011. “Security of Biometric Systems.” In Introduction to Biometrics, edited by Jain

A. K.

Ross

A. A.

Nandakumar

, 259–306. New York: Springer-Verlag.

36.

Jain

A. K.

Ross

A. A.

Pankanti

2006. “Biometrics: A Tool for Information Security.” IEEE Transactions on Information Forensics and Security 1 (2): 125–43.

37.

Kim

D. J.

2008. “Self-Perception-Based versus Transference-Based Trust Determinants in Computer-Mediated Transactions: A Cross-Cultural Comparison Study.” Management Information Systems 24 (4): 13–45.

38.

Kim

H. W.

Chan

H. C.

Chan

Y. P.

Gupta

2004. “Understanding the Balanced Effects of Belief and Feeling on Information Systems Continuance.” ICIS 2004 Proceedings 24.

39.

Kim

Bernhard

2014. “Factors Influencing Hotel Customers’ Intention to Use a Fingerprint System.” Journal of Hospitality and Tourism Technology 5 (2): 98–125.

40.

Koufaris

2002. “Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior.” Information Systems Research 13 (2): 205–23.

41.

Lancelot Miltgen

Popovič

Oliveira

2013. “Determinants of End-User Acceptance of Biometrics: Integrating the ‘Big 3’ of Technology Acceptance with Privacy Context.” Decision Support Systems 56:103–14.

42.

Lazarus

R. S.

1991. Emotion and Adaptation. Oxford: Oxford University Press.

43.

Lee

C. H.

Cranage

D. A.

2011. “Personalisation–Privacy Paradox: The Effects of Personalisation and Privacy Assurance on Customer Responses to Travel Web Sites.” Tourism Management 32 (5): 987–994.

44.

Lee

LaRose

2011. “The Impact of Personalized Social Cues of Immediacy on Consumers’ Information Disclosure: A Social Cognitive Approach.” Cyberpsychology Behavioral Social Networks 14 (6): 337–43.

45.

Lee

D. H.

Taylor

C. R.

2008. “Voluntary Self-Disclosure of Information on the Internet: A Multimethod Study of the Motivations and Consequences of Disclosing Information on Blogs.” Psychology and Marketing 25 (7): 692–710.

46.

Lee-Wingate

S. N.

Corfman

K. P.

2011. “The Effect of Consumer Emotional Disclosure on Fairness Perceptions.” Psychology and Marketing 28 (9): 897–908.

47.

Lerner

J. S.

Keltner

2000. “Beyond Valence: Toward a Model of Emotion-Specific Influences on Judgement and Choice.” Cognition and Emotion 14 (4): 473–93.

48.

Sarathy

2011. “The Role of Affect and Cognition on Online Consumers’ Decision to Disclose Personal Information to Unfamiliar Online Vendors.” Decision Support Systems 51 (3): 434–45.

49.

Loewenstein

Weber

E. U.

Hsee

C. K.

Welch

2001. “Risk as Feelings.” Psychology Bulletin 127 (2): 267–86.

50.

Lui

T. W.

Piccoli

2010. “Integrating Self-Service Kiosks in a Customer-Service System.” Cornell Hospitality Report 10:4–20.

51.

Lwin

Wirtz

Williams

J. D.

2007. “Consumer Online Privacy Concerns and Responses: A Power–Responsibility Equilibrium Perspective.” Journal of the Academy of Marketing Science 35 (4): 572–85.

52.

Malhotra

N. K.

Kim

S. S.

Agarwal

2004. “Internet Users’ Information Privacy Concerns (IUIPC): The Construct, the Scale, and a Causal Model.” Information Systems Research 15 (4): 336–55.

53.

Malhotra

N. K.

Kim

S. S.

Patil

2006. “Common Method Variance in IS Research: A Comparison of Alternative Approaches and a Reanalysis of Past Research.” Management Science 52 (12): 1865–83.

54.

Mardia

K. V.

1970. “Measures of Multivariate Skewness and Kurtosis with Applications.” Biometrika 57:519–30.

55.

Metzger

M. J.

2006. “Effects of Site, Vendor, and Consumer Characteristics on Web Site Trust and Disclosure.” Communication Research 33 (3): 155–79.

56.

Morganosky

1986. “Cost- versus Convenience-Oriented Consumers: Demographic, Lifestyle, and Value Perspectives.” Psychology & Marketing 3:35–46.

57.

Morosan

2011. “Voluntary Steps toward Air Travel Security: An Examination of Travelers’ Attitudes and Intentions to Use Biometric Systems.” Journal of Travel Research 51 (4): 436–50.

58.

Morosan

2012. “Biometric Solutions for Today’s Travel Security Problems.” Journal of Hospitality & Tourism Technology 3 (3): 176–95.

59.

Morosan

2015. “Using Registered Traveler Biometric Systems to Co-create Value in Air Travel: Development of a Conceptual Model.” eReview of Tourism Research, 6: ENTER 2015 Conference on Information and Communication Technologies in Tourism short papers 1-5.

60.

Muthén

L. K.

Muthén

B. O.

2003. Mplus User’s Guide, 5th ed. Los Angeles: Muthén & Muthén.

61.

Nemec Zlatolas

Welzer

Heričko

Hölbl

2015. “Privacy Antecedents for SNS Self-Disclosure: The Case of Facebook.” Computers in Human Behavior 45:158–67.

62.

Olsen

S. O.

Mai

H. T. X.

2013. “Consumer Participation: The Case of Home Meal Preparation.” Psychology & Marketing 30 (1): 1–11.

63.

Paine

Reips

U. D.

Stieger

Joinson

Buchanan

2007. “Internet Users’ Perceptions of ‘Privacy Concerns’ and ‘Privacy Actions.’” International Journal of Human-Computer Studies 65 (6): 526–36.

64.

Perala

2016. “SITA ABC eGates Improve Passenger Processing at Naples Airport. Find Biometrics.” http://findbiometrics.com/sita-abc-egates-naples-airport-301224/.

65.

Pomery

E. A.

Gibbons

F. X.

Reis-Bergan

Gerrard

2008. “From Willingness to Intention: Experience Moderates the Shift from Reactive to Reasoned Behavior.” Personality and Social Psychology Bulletin 35 (7): 894–908.

66.

Rafaeli

Vilnai-Yavetz

2004. “Emotion as a Connection of Physical Artifacts and Organizations.” Organization Science 15 (6): 671–86.

67.

Rothstein

M. A.

Talbot

M. K.

2006. “Compelled Disclosure of Health Information: Protecting Against the Greatest Potential Threat to Privacy.” Journal of the American Medical Association 295:2882–85.

68.

Scholderer

Grunert

K. G.

2005. “Consumers, Food and Convenience: The Long Way from Resource Constraints to Actual Consumption Patterns.” Journal of Economic Psychology 26:105–28.

69.

Shaver

Schwartz

Kirson

O’Connor

1987. “Emotion Knowledge: Further Exploration of a Prototype Approach.” Journal of Personality and Social Psychology 52 (6): 1061–86.

70.

Smith

H. J.

Milberg

S. J.

Burke

S. J.

1996. “Information Privacy Measuring Individuals’ Concerns about Organizational Practices.” MIS Quarterly 20 (2): 167–96.

71.

Sprague

2006. “Personal Health Records: The People’s Choice?” NHPF Issue Brief 820:1–13.

72.

Stangl

Inversini

Schegg

2016. “Hotels’ Dependency on Online Intermediaries and Their Chosen Distribution Channel Portfolios: Three Country Insights.” International Journal of Hospitality Management 52:87–96.

73.

Stewart

K. A.

Segars

A. H.

2002. “An Empirical Examination of the Concern for Information Privacy Instrument.” Information Systems Research 13 (1): 36–49.

74.

Sun

Wang

Shen

X. L.

Zhang

J. X.

2015. “Location Information Disclosure in Location-Based Social Network Services: Privacy Calculus, Benefit Structure, and Gender Differences.” Computers in Human Behavior 52:278–92.

75.

Sutanto

Palme

Tan

C. H.

Phang

C. W.

2013. “Addressing the Personalization-Privacy Paradox: An Empirical Assessment from a Field Experiment on Smartphone Users.” MIS Quarterly 37 (4): 1141–64.

76.

“Ten Million Passengers Processed through Automated Border Control eGates in Australian Airports.” airport-technology.com. 2016. http://www.airport-technology.com/news/newsten-million-passengers-processed-through-automated-border-control-egates-in-australian-airports-4992926.

77.

Thatcher

J. B.

Perrewé

P. L.

2002. “An Empirical Examination of Individual Traits as Antecedents to Computer Anxiety and Computer Self-Efficacy.” MIS Quarterly 26 (4): 381–96.

78.

Transportation Security Administration. 2016. TSA Pre✓.tsa.gov. US Department of Homeland Security.

79.

US Census. 2013. “Age and Sex Composition in the United States, 2012.” US Census. https://www.census.gov/population/age/data/2012comp.html.

80.

US Customs and Border Protection. 2012. “Trusted Traveler Programs.” Paper presented at the Biometrics Consortium Conference, Tampa, FL.

81.

US Customs and Border Protection. 2016. “Global Entry.” https://www.cbp.gov/travel/trusted-traveler-programs/global-entry.

82.

Vaske

J. J.

Donnelly

M. P.

1999.“A Value–Attitude–Behavior Model Predicting Wildland Preservation Intention.” Society & Natural Resources 12:523–37.

83.

Venkatesh

2000. “Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model.” Information Systems Research 11 (4): 342–65.

84.

Venkatesh

Morris

M. G.

Davis

G. B.

Davis

F. D.

2003. “User Acceptance of Information Technology: Toward a Unified View.” MIS Quarterly 27 (3): 425–78.

85.

Wakefield

2013. “The Influence of User Affect in Online Information Disclosure.” Journal of Strategic Information Systems 22 (2): 157–74.

86.

Wang

Cole

S. T.

Chen

J. S.

2017. “Tourist Innovation in Air Travel.” Journal of Travel Research 1–16. doi:10.1177/00472875166867.

87.

Wang

K. K. F.

Sparks

B. A.

2016. “Technology Readiness and Customer Satisfaction with Travel Technologies: A Cross-Country Investigation.” Journal of Travel Research 1–15. doi:10.1177/0047287516657891.

88.

Westbrook

R. A.

1987. “Product/Consumption-Based Affective Responses and Postpurchase Processes.” Journal of Marketing Research 24:258–70.

89.

Westin

A. F.

2003. “Social and Political Dimensions of Privacy.” Journal of Social Issues 59 (2): 431–53.

90.

Wirtz

Lwin

M. O.

2009. “Regulatory Focus Theory, Trust, and Privacy Concern.” Journal of Service Research 12 (2): 190–207.

91.

Wood

S. L.

Moreau

C. P.

2006. “From Fear to Loathing? How Emotion Influences the Evaluation and Early Use of Innovations.” Journal of Marketing 70:44–57.

92.

Xie

Bagozzi

R. P.

Østli

2013. “Cognitive, Emotional, and Sociocultural Processes in Consumption.” Psychology & Marketing 30 (1): 12–25.

93.

Xie

Teo

H. H.

Wan

2006. “Volunteering Personal Information on the Internet: Effects of Reputation, Privacy Notices, and Rewards on Online Consumer Behavior.” Marketing Letters 17 (1): 61–74.

94.

Luo

Carroll

J. M.

Rosson

M. B.

2011. “The Personalization Privacy Paradox: An Exploratory Study of Decision Making Process for Location-Aware Marketing.” Decision Support Systems 51 (1): 42–52.

95.

Zhang

2013. “The Affective Response Model: A Theoretical Framework of Affective Concepts and Their Relationships in the ICT Context.” MIS Quarterly 37 (1): 247–74.

96.

Zimmer

J. C.

Arsal

R. E.

Al-Marzouq

Grover

2010. “Investigating Online Information Disclosure: Effects of Information Relevance, Trust and Risk.” Information & Management 47 (2): 115–23.