This study extended the technology acceptance model (TAM) by applying it to the context of biometric systems in the hotel industry and by introducing a consumer-oriented construct, perceived innovativeness, as an antecedent of perceived ease of use. Using data collected from hotel guests who traveled during a 12-month period prior to the study, the model explained 79% of the variability in guests’ intentions to use biometric systems in hotels. The results showed that the TAM is an appropriate theoretical framework for the examination of adoption of biometrics in hotels, and that hotel guests are ready to adopt biometric systems, especially if they are perceived as useful.
AdamsB. (2002). Hotels slow to embrace biometric security. Hotel & Motel Management, 217(8), 46.
2.
AdlerA. (2008). Biometric systems security. In JainA. K.FlynnP.RossA. A. (Eds.), Handbook of biometrics (pp. 381-402). New York, NY: Springer.
3.
AgarwalR.KarahannaE. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24, 665-694.
4.
AgarwalR.PrasadJ. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology, Information Systems Research, 9, 204-215.
5.
AgnvallE. (2007). Biometrics clock in. HR Magazine, 52(4), 103-105.
6.
AhnT.RyuS.HanI. (2007). The impact of web quality and playfulness on user acceptance of online retailing. Information & Management, 44, 236-275.
7.
AjzenI. (1985). From intentions to actions: A theory of planned behavior. In KuhlJ.BeckmannJ. (Eds.), Action-control: From cognition to behavior (pp. 11-39). Heidelberg, Germany: Springer.
8.
AjzenI. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211.
9.
AltermanA. (2003). A piece of yourself: Ethical issues in biometric identification. Ethics and Information Technology, 5, 139-151.
10.
AraujoC. L.SucupiraL. H. R.LizarragaM. G.LingL. L.Yabu-UtiB. T. (2005). User authentication through typing biometrics features. IEEE Transactions on Signal Processing, 53, 851-855.
11.
AryD.JacobsL.RazaviehA. (1996). Introduction to research in education (5th ed.). Fort Worth, TX: Holt, Rinehart & Winston.
BolleR. M.ConnellJ. H.PakantiS.RathaN. K.SeniorA. W. (2004). Guide to biometrics. New York, NY: Springer.
15.
BollenK. A. (1989). Structural equations with latent variables. New York, NY: Wiley.
16.
BrackettL. K.CarrB. N.Jr. (2001). Cyberspace advertising vs. other medial consumer vs. mature student attitudes. Journal of Advertising Research, 41, 23-32.
17.
BrunerG. C.KumarA. (2005). Explaining consumer acceptance of handheld Internet devices. Journal of Business Research, 58, 553-558.
18.
BuhalisD.LawR. (2008). Progress in information technology and tourism management: 20 years on and 10 years after the Internet: The state of e-tourism research. Tourism Management, 29, 609-623.
19.
CalderB. J.PhillipsL. W.TyboutA. M. (1981). Designing research for applications. Journal of Consumer Research, 8, 197-207.
20.
CarrollB.SiguawJ. (2003). Evolution in electronic distribution: Effects on hotels and intermediaries. Ithaca, NY: The Center for Hospitality Research at Cornell University.
21.
ChandraA.CalderonT. (2005). Challenges and constraints to the diffusion of biometrics in information systems. Communications of the ACM, 48(12), 101-106.
22.
CholletG.DorizziB.Petrovska-DelacretazD. (2009). Introduction: About the need of an evaluation framework in biometrics. In Petrovska-DelacretazD.CholletG.DorizziB. (Eds.), Guide to biometric reference systems and performance evaluation (pp. 1-10). New York, NY: Springer.
23.
ChristouE.KassianidisP. (2002). Consumers perception and adoption of online buying for travel products. Journal of Travel & Tourism Marketing, 12(4) 93-107.
24.
ChungC.LawR. (2009). Have the perceptions of the successful factors for travel web sites changed over time? The case of consumers in Hong Kong. Journal of Hospitality & Tourism Research, 33, 438-447.
25.
ChungJ.TanF. B. (2004). Antecedent of perceived playfulness: an exploratory study on user acceptance of general information-searching web sites, Information & Management, 41, 869-881.
26.
ConnollyD. J.OlsenM. D.MooreR. G. (1998). The Internet as a distribution channel. Cornell Hotel and Restaurant Administration Quarterly, 39(4), 42-54.
27.
ConnorsJ. J.ElliotJ. (1994). Teacher perceptions of agriscience and natural resources curriculum. Journal of Agricultural Education, 35(4), 15-19.
28.
CowartK. O.FoxG. L.WilsonA. E. (2008). A structural look at consumer innovativeness and self-congruence in new product purchases. Psychology & Marketing, 25, 1111-1130.
DavisF. D. (1986). Technology acceptance model for empirically testing new end-user information systems: Theory and results (Unpublished doctoral dissertation). Massachusetts Institute of Technology, Cambridge.
31.
DavisF. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319-340.
32.
DeaneF.BarrelleK.HendersonR.MaharD. (1995). Perceived acceptability of biometric security systems. Computers and Security, 14, 225-231.
33.
Den HartogJ.Van MunsterR. (2008). Biometrics at the turnstiles. Computer Weekly, 156, 20-22.
34.
DevarajS.FanM.KohliR. (2002). Antecedents of B2C channel satisfaction and preference: validating e-commerce metrics. Information Systems Research, 13, 316-333.
35.
FishbeinM. (1963). An investigation of the relationships between beliefs and object and the attitude toward that object. Human Relations, 16, 233-240.
36.
FishbeinM.AjzenI. (1975). Belief, attitudes, intentions, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
37.
FlynnL. R.GoldsmithR. E. (1993). A validation of the Goldsmith and Hofacker Innovativeness Scale. Educational and Psychological Measurement, 53, 1105-1116.
38.
FornellC.LarckerD. (1981). Structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39-50.
39.
GiarimiS.MagnussonH. (2002). Investigation of user acceptance for biometric verification/identification methods in mobile units (Unpublished master’s thesis). Stockholm University/Royal Institute of Technology, Stockholm, Sweden. Retrieved from http://www.dsv.su.se/research/seclab/pages/pdf-files/02-17.pdf
40.
GoldsmithR. E.HofackerC. F. (1991). Measuring consumer innovativeness. Journal of the Academy of Marketing Science, 19, 209-221.
41.
GoldsmithR. E.FlynnE. R.GoldsmithE. B. (2003). Innovative consumers and market mavens. Journal of Marketing Theory and Practice, 11, 54-64.
42.
HairJ. F.AndersonR. E.TathamR. L.BlackW. C. (1998). Multivariate data analysis with readings (5th ed.). New York, NY: Prentice Hall.
43.
HeracleousL.WirtzJ. (2006). Biometrics: The next frontier in service excellence, productivity and security in the service sector. Managing Service Quality, 16, 12-22.
44.
HsuC. L.LuH. P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information & Management, 41, 853-868.
45.
HuhH. J.KimT.LawR. (2009). A comparison of competing theoretical models for understanding acceptance behavior of information systems in upscale hotels. International Journal of Hospitality Management, 28, 121-134.
46.
HurtH. T.JosephK.CookC. D. (1977). Scales for the measurement of innovativeness. Human Communication Research, 4, 58-65.
47.
ImI.KimY.HanH. J. (2008). The effects of perceived risk and technology type on users’ acceptance of technologies. Information & Management, 45, 1-9.
IvesR. W.DuY.EtterD. M.WelchT. B. (2005). A multidisciplinary approach to biometrics. IEEE Transactions on Education, 48, 462-471.
50.
JacksonL. A. (2009). Biometric technology: The future of identity assurance and authentication in the lodging industry. International Journal of Contemporary Hospitality Management, 21, 574-582.
51.
JainA. K. (2007). Biometric recognition. Nature, 449, 38-40.
52.
JainA. K.RossA. A. (2008). Introduction to biometrics. In JainA. K.FlynnP.RossA. A. (Eds.), Handbook of biometrics (pp. 1-22). New York, NY: Springer.
53.
JamesT.PirimT.BoswellK.ReithelB.BarkhiR. (2006). Determining the intention to use biometric devices: An application and extension of the technology acceptance model. Journal of Organizational and End User Computing, 18(3), 1-24.
54.
JonesP.WilliamsP.HillierD.ComfortD. (2007). Biometrics in retailing. International Journal of Retail and Distribution Management, 35, 217-222.
KaplanidouK.VogtC. (2006). A structural analysis of destination travel intentions as a function of website features. Journal of Travel Research, 45, 204-216.
57.
KarahannaE.StraubD. W.ChervanyN. L. (1998). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23, 183-213.
58.
KimG. S.ParkS. B. (2008). An examination of factors influencing consumer adoption of short message service (SMS). Psychology & Marketing, 25, 769-786.
59.
KimJ. (2009). A comprehensive structural model of factors influencing customers’ intention to use biometrics in the hospitality industry (Doctoral dissertation). Retrieved from Proquest Dissertations & Theses (AAT 3383980).
60.
KimJ.BrewerP.BernhardB. (2008). Hotel customers perceptions of biometric door locks. Journal of Hospitality Marketing & Management, 17, 162-183.
61.
KimJ.ForsytheS. (2008). Sensory enabling technology acceptance model (SE-TAM): A multiple-group structural model comparison. Psychology & Marketing, 25, 901-922.
62.
KimK. H.KoE. J.TakahashiI.SchellhaseR.KimM. S.LeeC. H. (2008). A model of adoption of digital multimedia broadcasting (DMB) service: Comparisons in Korea, Japan, and Germany. Psychology & Marketing, 25, 806-820.
63.
KimS. H. (2008). Moderating effects of job relevance and experience on mobile wireless technology acceptance: Adoption of a smartphone by individuals. Information & Management, 45, 387-393.
64.
KimT. G.LeeJ. H.LawR. (2008). An empirical examination of the acceptance behaviour of hotel front office systems: An extended technology acceptance model. Tourism Management, 29, 500-513.
KleistV. F. (2007). Building technologically based online trust: Can the biometrics industry deliver the online trust silver bullet?Information Systems Management, 24, 319-329.
67.
KoltzschG. (2007). Biometrics: Market segments and applications. Journal of Business Economics and Management, 8, 119-122.
68.
KoufarisM. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13, 205-223.
69.
LamT.ChoV.QuH. (2007). A study of hotel employee behavioral intentions towards adoption of information technology. International Journal of Hospitality Management, 26, 49-65.
70.
LangenderferJ.LinnhoffS. (2005). The emergence of biometrics and its effects on consumers. Journal of Consumer Affairs, 39, 314-338.
71.
LauxD. D. (2007). A study of biometric authentication adoption in the credit union industry (Master’s thesis). Retrieved from Proquest Dissertations & Theses (AAT 1443068).
72.
LawR.ChenF. (2000). Internet in travel and tourism—part II: Expedia. Journal of Travel & Tourism Marketing, 9(4), 83-88.
73.
LawR.JogaratnamG. (2005). A study of hotel information technology applications. International Journal of Contemporary Hospitality Management, 17, 170-181.
74.
LeeH. Y.KimW. G.LeeY. K. (2006). Testing the determinants of computerized reservation system users’ intention to use via a structural equation model. Journal of Hospitality & Tourism Research, 30, 246-266.
75.
LeeY.KozarK. A. (2008). An empirical investigation of anti-spyware software adoption: A multitheoretical perspective. Information & Management, 45, 109-119.
76.
LegrisP.InghamJ.ColleretteP. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40, 191-204.
77.
LewisW.AgarwalR.SambamurthyV. (2003). Sources of influence on beliefs about information technology use: An empirical study of knowledge workers. MIS Quarterly, 27, 657-679.
78.
LinC. H.ShihH. Y.SherP. J. (2007). Integrating technology readiness into technology acceptance: The TRAM model. Psychology & Marketing, 24, 641-657.
79.
LinH. F. (2007). Predicting consumer intentions to shop online: An empirical test of competing theories. Electronic Commerce Research and Applications, 6, 433-442.
80.
Lopez-NicolasC.Molina-CastilloF. J.BouwmanH. (2008). An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models. Information & Management, 45, 359-364.
81.
LuJ.LiuC.YuC. S.WangK. (2008). Determinants of accepting wireless mobile data services in China. Information & Management, 45, 52-64.
82.
LuJ.YaoJ. E.YuC. S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. Journal of Strategic Information Systems, 14, 245-268.
83.
LucasP. (2005). Biometrics come into focus. Collections and Credit Risk, 10(3), 18-21.
84.
LumsdenS. A. M.BeldonaS. (2006, June). Biometrics technology applications in the lodging industry. Paper presented at the 2006 Hospitality Information Technology Association (HITA) Conference, Minneapolis, MN.
85.
MaQ.LiuL. (2004). The technology acceptance model: A meta-analysis of empirical findings. Journal of Organizational and End User Computing, 16, 59-72.
MoonJ. W.KimY. G. (2001). Extending the TAM model for the World Wide Web context. Information & Management, 38, 217-230.
90.
MordiniE. (2007). Technology and fear: Is wonder the key?Trends in Biotechnology, 25, 544-546.
91.
MorosanC.JeongM. (2008). Users’ perceptions of two types of hotel reservation websites. International Journal of Hospitality Management, 27, 284-292.
92.
MurphyH. C.RottetD. (2009). An exploration of the key hotel processes implicated in biometric adoption. International Journal of Contemporary Hospitality Management, 21, 201-212.
93.
NunnallyJ. C. (1978). Psychometric theory. New York, NY: McGraw-Hill.
94.
OhS. H.KimY. M.LeeC. W.ShimG. Y.ParkM. S. (2009). Consumer adoption of virtual stores in Korea: Focusing on the role of trust and playfulness. Psychology & Marketing, 26, 652-668.
95.
PonsA.PolakP. (2008). Understanding user perspectives on biometric technology. Communications of the ACM, 51, 115-118.
96.
PremkumarG.RamamurthyK.LiuH. N. (2008). Internet messaging: An examination of the impact of attitudinal, normative, and control belief systems. Information & Management, 45, 451-457.
97.
RogersE. M. (1983). Diffusion of innovations (3rd ed.). New York, NY: Free Press.
98.
RogersE. M.ShoemakerF. F. (1971). Communication of innovations: A cross-cultural approach. New York, NY: Free Press.
99.
RuthC. (2000). Applying a modified technology acceptance model to determine factors affecting behavioral intention to adopt electronic shopping on the World Wide Web: A structural equation modeling approach (Unpublished doctoral dissertation). Drexel University, Philadelphia, PA.
100.
SaeedK. A.Abdinnour-HelmS. (2008). Examining the effects of information system characteristics and perceived usefulness on post adoption usage of information systems. Information & Management, 45, 376-386.
101.
SchepersJ.WetzelsM. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44, 90-103.
102.
SchoutenB.JacobsB. (2009). Biometrics and their use in e-passports. Image and Vision Computing, 27, 305-312.
103.
ShihH. P. (2004). Extended technology acceptance model of Internet utilization behavior. Information & Management, 41, 719-729.
104.
ShinD. H. (2009). Determinants of customer acceptance of multi-service network: An implication for IP-based technologies. Information & Management, 46, 16-22.
105.
SimonH. (2004). Company seeks public acceptance for iris biometrics in posh hotel. Aviation Weeks Homeland Security and Defense Report, 3(32), 4.
106.
SinghA. J.KasavanaM. L. (2005). The impact of information technology on future management of lodging operations: A Delphi study to predict key technological events in 2007 and 2027. Tourism and Hospitality Research, 6, 24-37.
107.
Starwood Hotels Resorts goes live with Global TelePresence meetings suites allowing guests to meet across continents. (2010). Hospitalitynet.com. Retrieved from http://www.hospitalitynet.org/news/4045573.print
108.
SternB. B.RoyneM. B.StaffordT. F.BienstockC. C. (2008). Consumer acceptance of online auctions: An extension and revision of the TAM. Psychology & Marketing, 25, 619-636.
TaylorS.ToddP. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6, 144-176.
111.
TsaiR. C. (2007). Biometrics in the private sector: Trends and case studies. In CoatsW. S.BagdasarianA.HelouT. J.LamT. (Eds.), The practitioner’s guide to biometrics (pp. 139-146). Chicago, IL: American Bar Association.
112.
VijanL. (2004). Corporate America slow to adopt biometric systems. Computer World Magazine, 38(32), 32-34.
113.
VijayasarathyL. R. (2004). Predicting consumer intentions to use on-line shopping: the case for augmented technology acceptance model. Information & Management, 41, 747-762.
WalczuchR.LemminkJ.StreukensS. (2007). The effect of service employees’ technology readiness on technology acceptance. Information & Management, 44, 206-215.
116.
WangY.QuallsW. (2007). Towards a theoretical models of technology adoption in hospitality organizations. International Journal of Hospitality Management, 26, 560-573.
117.
WoberK.GretzelU. (2000). Tourism managers’ adoption of marketing decision support systems. Journal of Travel Research, 39, 172-181.
118.
WolfeD. (2008). User milestone for a biometric system. American Banker, 173(190), 10.
119.
YuJ.HaI.ChoiM.RhoJ. (2005). Extending the TAM for a t-commerce. Information & Management, 42, 965-976.
