Consumer Acceptance of Identification Technology

Transcriptie

1 Ghent University Faculty of Economics and Business Administration Academic Year Consumer Acceptance of Identification Technology Master thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Business Economics Hylke Huys Supervisor Prof. Dr. Patrick Van Kenhove

2

3 Ghent University Faculty of Economics and Business Administration Academic Year Consumer Acceptance of Identification Technology Master thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Business Economics Hylke Huys Supervisor Prof. Dr. Patrick Van Kenhove

4 Permission I declare that the contents of this master's thesis may be consulted and/or reproduced if cited.

5 Dutch Summary Het onderwerp van deze paper (Acceptatie van identificatie technologie door consumenten) wint elk jaar meer en meer aan populariteit. Ondanks het feit dat mensen deze technologieën lijken te aanvaarden in een verplichtte omgeving (casino s, grenscontroles, toegang tot bepaalde sites) hebben toch weinig onderzoeken de acceptatie van consumenten in een vrijwillige of in hun dagelijkse omgeving onderzocht. Deze studie onderzoekt de acceptatie van biometrics voor consumenten in een Retail kader, waarbij drie typen identificatie technologieën worden benadrukt: fingerprint recognition, iris scanning en facial recognition. Fingerprint recognition identificeert mensen door de afdrukken of patronen, gevonden op hun vingertoppen, te gebruiken. (Human Recognition Systems, 2014). Iris camera s herkennen een persoon zijn identiteit via een mathematische analyse van de willekeurige patronen. Deze patronen zijn zichtbaar in de iris van een oog en vanop een zekere afstand (findbiometrics, 2014). Het opgestelde theoretische kader van dit onderwerp (zie p.19) levert niet enkel een academisch model ten voordele van theoretisch onderzoek, maar levert ook een praktisch inzicht over hoe retailers fingerprint recognition (als betaalmiddel) en iris scanning en facial recognition scanners (als marketing tool) kunnen implementeren in hun winkels, terwijl ze de invloedrijke variabelen op consumenten hun acceptatie in gedachten houden. Verschillende hypothesen zijn getest over een steekproef van 187 fingerprint en 151 iris scanning en facial recognition respondenten en hieruit zijn ook significante resultaten gekomen. De behavioral intention om deze identificatie technologieën te accepteren hangt significant af van de volgende factoren: perceived usefulness van de technologie (β: 0.67 voor IS&FR, 0.70 voor FP), de perceived ease of use (β: 0.24 voor IS&FR, 0.48 voor FP), de relative advantages van de technologie, vergeleken met andere betaalmethodes of marketing tools (β: 0.63 voor IS&FR, 0.77 voor FP), de privacy concerns van consumenten ten opzichte van de technologieën (β: voor IS&FR, voor FP), de anxiety van consumenten ten opzichte van deze technologieën (β: voor IS&FR, voor FP), de innovativeness van consumenten in het algemeen (β: 0.34 voor IS&Fr, 0.16 voor FP), de omliggende facilitating conditions (β: 0.49 voor IS&FR en voor FP) en de experience dat consumenten hebben met deze technologie (β: 0.27 voor IS&FR, 0.19 voor FP). Retailers zouden dus daarom zeker de privacy concerns van consumenten moeten proberen te reduceren. Retailers moeten genoeg informatie leveren over wat er gebeurt met de data, die ontstaat door het pay by toch mechanisme en die geobserveerd wordt door de iris en facial scanner. Retailers zouden ook voornamelijk in de eerste fases van implementatie experten of I

6 extra werknemers moeten inhuren om consumenten te assisteren en om hun vragen te beantwoorden over de technologie. Dit met het doet om de ervaring van mensen te vergemakkelijken. Hoewel er niet veel respondenten deelnamen, die ervaring hadden met deze technologieën, toonde het onderzoek aan dat de intentie van consumenten om biometrics te accepteren, verhoogde wanneer hun algemene ervaring steeg. Daarom zal het zeer moeilijk zijn om mensen de technologie in deze situatie te laten accepteren, zolang de technologie niet zal gebruikt worden bij omliggende retailers. Aangezien er twee verschillende identificatie technologieën worden toegepast in dit onderzoek, is het interessant om de verschillen tussen de twee te ontdekken. De resultaten tonen aan, via het gebruik van t-testen, dat consumenten significant meer comfortabel zijn met het fingerprint recognition systeem. Deze conclusie is niet enkel gebaseerd op het construct behavioral intention, maar op alle constructen van de scenario questions, behalve voor experience. Daarom zou het een aanbeveling aan retailers kunnen zijn, om, als ze customer experience willen verbeteren door middel van een biometric systeem, te beginnen met fingerprint. Iets om in gedachten te houden is dat deze studie het fingerprint systeem direct linkte als betaalmiddel en het iris scanning en facial recognition systeem als marketing tool. Het zou kunnen dat consumenten andere reacties hebben indien de technologieën in de omgekeerde situatie zouden gebracht worden: facial recognition aan de kassa bijvoorbeeld. Deze opmerking zou een bijdrage kunnen zijn voor verder onderzoek. Het effect van demografische variabelen op de constructen is ook onderzocht via een General Linear Model. Hoewel er geen duidelijke interactie-effecten zijn gevonden, heeft leeftijd een sterk hoofdeffect op BI, PU, PEOU, RA, PC en FC. De effecten zijn voornamelijk dezelfde: de uiterste leeftijdscategorieën hebben meer intentie tot acceptatie, zien meer usefulness (nuttigheid), meer ease of use (gebruiksgemak), meer relative advantages (voordelen), meer facilitating conditions (gemakkelijker makende condities) en hebben minder privacy concerns (zorgen over de privacy) dan de leeftijdscategorie jaar. Deze trend is wel limitatief. Ten eerste zijn er zeer weinig respondenten in de leeftijdscategorie - 18 (4.7 % van alle respondenten). Ten tweede geloven we dat de 60-plussers in deze studie de enquête meer theoretisch dan praktisch benaderden. Ten derde zijn de correlaties tussen leeftijd en de variabelen eerder laag en niet significant (zie appendix 10). II

7 Ondanks dat het onderzoek veel hypothesen en veel variabel bevat, is ze in bepaalde opzichten limitatief en zijn er mogelijke suggesties voor toekomstig onderzoek. Bijvoorbeeld, het onderzoek is uitgevoerd in België, maar misschien bestaan er andere coëfficiënten voor andere landen of culturen. Een andere limitatie is dat het voor respondenten moeilijk is om zich iets voor te stellen dat ze nog nooit eerder hebben meegemaakt in praktijk. Men zou de hele studie kunnen herdoen, maar onder praktische omstandigheden. Ook focus groepen, laboratoria en experimenten zullen voor de retailer meer inzicht bieden dan een enquête. In het algemeen geeft de studie een goed inzicht in de behavioral intention van consumenten om identificatie technologieën te accepteren. Ze is gebaseerd op verschillende leeftijdscategorieën, op een dagelijkse omgeving en op verschillende biometrics. Retailers begrijpen hierdoor meer en beter de achterliggende redenen van consumenten om biometric systemen te accepteren (of te weigeren). Maar indien het implementatie proces van zulke technologieën niet met voorzichtigheid en niet doordacht wordt uitgevoerd, zal deze technologie als bedreigend worden opgevat. Dit zou zeer teleurstellend zijn, voornamelijk voor een technologie die zoveel te bieden heeft in de toekomst. III

8 Acknowledgments This dissertation would not have been as it is now without the help of certain people. First of all, I like to thank the thirty respondents, who filled in the pretest and placed critical, but useful recommendations to form the final survey. Next, I would like to thank my parents, sister, friends and family who helped me distributing the survey to as many age categories as possible, both online and offline. I would also like to thank the readers of this final dissertation, for their time, input and critical view. Finally, I would like to thank my supervisor, Prof. Dr. Patrick Van Kenhove. He was honestly interested in the topic and helped me through the set-up of this final dissertation. IV

9 Table of Contents Dutch Summary... I Acknowledgments... IV Table of Contents... V List of Abbreviations... IX List of Tables... X List of Figures... XI Chapter I: Introduction... 1 Chapter II: Background and Literature Review... 2 Biometrics... 3 Fingerprint recognition... 3 Iris scanning... 5 Facial recognition... 5 Technology Acceptance Models... 7 Ethical ideologies Chapter III: Methodology Concepts, variables and specific hypotheses Dependent variables Independent variables Survey Content Sample and Procedure Measurement of variables Scenario Questions Fingerprint recognition Iris scanning and facial recognition General Questions Demographic Questions Chapter IV: Results and Interpretation Measurement Model Results Hypothesis testing The differences between the identification technologies Significant differences between the biometric types: t-tests V

10 Main and interaction effects of demographic variables on behavioral intention to accept identification technologies General Linear Model Chapter V: Limitations and Suggestions for Future Research Chapter VI: Conclusion & Pragmatically implications References... I Appendices Appendix 1: Survey Dutch version Appendix 1.1 Fingerprint recognition Vingerafdruk herkenning Inleiding Deel 1: Scenariovragen Deel 2: Algemene Vragen Deel 3: Demografische vragen Appendix 1.2 Iris scanning en facial recognition ( iris scanning en gezichtsherkenning) Inleiding Deel 1: Scenariovragen Deel 2: Algemene Vragen Deel 3: Demografische vragen Appendix 2: Survey English version (translation) Appendix 2.1 Fingerprint recognition Introduction Part 1: Scenario questions Part 2: General questions Part 3: Demographic Questions Appendix 2.2 Iris scanning and facial recognition Introduction Part 1: Scenario questions Part 2: General questions Part 3: Demographic Questions Appendix 3: Sources Instruments measure Appendix 4: Reliability Analysis output Appendix 4.1 Fingerprint recognition Appendix 4.2 Iris scanning and facial recognition Appendix 5: Factor Analysis constructs Question 3 & VI

11 Appendix 5.1 Fingerprint recognition Appendix 5.2 Iris scanning and facial recognition Appendix 6: Final list of items per construct (original: Dutch) Appendix 6.1 Fingerprint recognition Appendix 6.2 Iris scanning and facial recognition Appendix 7: Correlation Diagnostics and table Appendix 7.1 Fingerprint recognition Appendix 7.2 Iris scanning and facial recognition Appendix 8: mediator and moderator relations regression analysis Appendix 8.1 mediation hypothesis 3b Iris scanning and facial recognition Fingerprint recognition Appendix 8.2 mediation hypothesis Iris scanning and facial recognition Fingerprint recognition Appendix 8.3 moderation hypothesis 13b Iris scanning and facial recognition Fingerprint recognition Appendix 8.4moderation hypothesis Iris scanning and facial recognition 14a Iris scanning and facial recognition 14b Iris scanning and facial recognition 14c Fingerprint recognition 14a Fingerprint recognition 14b Fingerprint recognition 14c Appendix 9: T-tests between independent identification technologies Appendix 10: General Linear Model: interaction effects demographic variables on different identification technologies Appendix 10.1 Demographic variables on BI of IS&FR and FP Gender Age Highest obtained education Social status VII

12 Profession Appendix 10.2 Main and interaction effect of age regarding independent variables Perceived Usefulness Perceived Ease Of Use Relative Advantage Privacy concerns Technology Anxiety Facilitating Conditions Appendix 10.3 Correlations age independent variables VIII

13 List of Abbreviations IS FR FP TAM CAT PAD TR TRAM DOI UTAUT BI BR PU PEOU RA PC TA FC Exp Innov SI ID RE RC SE Iris Scanning Facial Recognition Fingerprint recognition Technology Acceptance Model Consumer Acceptance of Technology Pleasure, Arousal and Dominance (model) Technology Readiness Technology Readiness Acceptance Model Diffusion of Innovations Model Unified Theory of Acceptance and Use of Technology (model) Behavioral Intention to accept the identification technology Behavioral intention to Recommend the identification technology Perceived Usefulness Perceived Ease Of Use Relative Advantage Privacy Concerns Technology Anxiety Facilitating Conditions Experience Innovativeness Social Influence Idealism Relativism Resultant Conservation Self-Enhancement IX

14 List of Tables Table 1 Taxonomy of Ethical Ideologies (Forsyth, 1980, p.176) Table 2 Demographic characteristics of the sample Table 3 Cronbach's Alpha, number of items, mean, standard deviation of constructs Table 4 Results for the independent variables on Behavioral Intention and hypothesis testing Table 5 Differences between constructs and biometric applications: mean, standard deviation, p- value Table 6 Main effect of age on independent variables (PU, PEOU, RA, PC, FC) X

15 List of Figures Figure 1 Technology Acceptance Model (TAM) (Davis et al., 1989, p.985) Figure 2 Consumer Acceptance of Technology (CAT) model (Kulviwat et al., 2007, p.1064) Figure 3 Research model UTAUT (Venkatesh et al., 2003, p.447) Figure 4 Summary of Findings (Venkatesh et al., 2003, p. 468) Figure 5 Proposed Theoretical Framework (Miltgen et al., 2013, p.106) Figure 6 Theoretical model of relations among 10 personal values (Steenhaut and Van Kenhove, 2006, p.140) Figure 7 Proposed Theoretical Framework Figure 8 Picture pay by touch: Mindfully.org, 2005, Piggly Wiggly Fingerprint Scanners, Figure 9 Facial Recognition camera ( Marieclaire.com, 2013, How Do You Shop? A Technology May Already Know. Hearst Communication, Inc., < )... Appendix Figure 10 Picture pay by touch: Mindfully.org, 2005, Piggly Wiggly Fingerprint Scanners, Figure 11 Facial Recognition Camera (Marieclaire.com, 2013, How Do You Shop? A Technology May Already Know. Hearst Communication, Inc., < ) Appendix XI

16 Chapter I: Introduction There has been a lot of research regarding identification technology or biometrics. The existing research deals primarily with the systems of biometrics, the advantages and disadvantages, and looks at it particularly from a corporate point of view, rather than from a consumer s perspective. But the latter is equally important. The increasing identification technologies used in other industries and sectors make the question Why will consumers accept or reject identification technologies? more and more important. Furthermore, the arise of these type of technologies is not something that is happening miles and miles away from the consumer s daily live. On the contrary, it moves closer! It is already known that people accept these technologies when they are mandatory and when they are used in a logical situation (Casinos, airports, border controls ), but are people also willing to accept them in a voluntary situation, for instance: at their department store? Piggly Wiggly and Wall Mart (US) are already testing these applications, but it is important to know for retailers (and managers) how they should approach consumers to facilitate and enhance their acceptance of biometric technologies. This dissertation discusses the acceptance of identification technology by consumers, specifically in a retail setting. It investigates the acceptance of three well-known types: fingerprint recognition, iris scanning and facial recognition. The first one is investigated in a check-out situation: pay by touch. The two other, iris scanning and facial recognition are addressed from a marketing perspective: at the point-of-sale (in the store). First, we investigate the biometric applications separately to see which factors influence the consumers choice to accepting or rejecting them. Next, we examine if there is a difference in how consumers perceive the different biometrics systems: do they see them as similar technologies or do they have different opinions about each biometric technology? The purpose and the research of this dissertation is therefore twofold. First, it offers a theoretical framework, containing factors related to technology acceptance, discovered by other researchers and by ourselves. Secondly, it gives a pragmatic answer (for retailers) to the question which factors have an important impact on the consumer s acceptance of identification technology in a retail setting. We offer a solution by formulating several hypotheses regarding the dependent variable (the behavioral intention of consumers to accept identification technology) and test them with an appropriate survey and SPSS analysis. An answer on this is also provided based on the results of the survey, but is more pragmatically discussed in the conclusion. 1

17 The next section provides a detailed background and literature overview of the research topic in order to offer the reader a compact amount of background (literature and frameworks) to fully understand the research topic and the used concepts. Next, in chapter III, there is a detailed overview of the used dependent and independent variables. Related to each variable, an appropriate hypothesis is described. All the variables and hypotheses are summarized in one unified theoretical framework. Furthermore, the survey is outlined in detail: its content, the sample, the procedure and the measurement of the variables. In Chapter IV the outcome of the surveys is describes and the hypotheses and research questions are tested. Interpretations about the data are given, and fully explained with summarized tables and figures. Chapter V defines the limitations of the dissertation and contains suggestions for future research. The last chapter, chapter VI, gives a general conclusion on this research and its results. Chapter II: Background and Literature Review The literature review gives a clear picture of what identification technology or biometrics is today and how it evolved over the past decades. Only few older background studies are used, in favor of more recent published papers and articles that are written since the beginning of the 21 st century. First there is a review about the content of biometrics, the use of different identification technologies in practice and the advantages or disadvantages of these applications. Secondly, the literature of technology acceptance is applied to form an idea how and why consumers would be likely to accept identification technologies. A summary of the useful technology acceptance, adoption and readiness models is provided. For each applicable model, there is clearly indicated which constructs or items are applied in the research. Thirdly, since Miltgen et al. (2013) recently discussed that the most important drivers to explain biometrics acceptance and recommendation are not from the traditional adoption models, but from the trust and privacy literature, a complementary subchapter is included on the ethical ideologies exploring whether consumers, who are likely to accept biometrics, are more idealistic or relativistic oriented. In addition, an extension on the ethical ideologies is given by introducing the personal values of Schwartz (1992) in the model. 2

18 Biometrics The application of biometrics is an increasing phenomenon as a result of the recent technological evolutions of the past decades. When searching Biometrics in a dictionary, the following definition is found: the process by which a person's unique physical and other traits are detected and recorded by an electronic device or system as a means of confirming identity. (dictionary.com, 2014). Or, the definition of Miltgen et al. (2013): Biometric identification systems recognize and authenticate people based on a person s unique physical or behavioral features. Biometrics can be seen as a form of identification. Today, there are three main possible processes of identification. The first is something one has or owns, like a card. Secondly, a person can be identified by something that he or she knows, like a password or a PIN. And a third way of identification is by something that is part of an individual o that is done by the individual. This third category is called biometrics (Davies, 1994). There are different types of biometrics and with these different types, different applications and different patterns of acceptance. First, a well-known universal type (fingerprint recognition) is discussed in this dissertation, looking at it as a paying system at the check-out in a retail environment. Secondly, two other types in this study, namely iris scanning and facial recognition, are considered. These two types are less known in retail, but can be applied when searching for consumer behaviors in the store itself. Each paragraph gives a brief description of the biometric system, supported by practical examples, and describes some advantages or opportunities and disadvantages or concerns. Fingerprint recognition Fingerprint recognition identifies people by using the impressions made by the minute ridge formations or patterns found on the fingertips. (Human Recognition Systems, 2014). This dissertation concentrates on the use of fingerprint recognition in a retail setting, at point-of-sale and more specifically, at the check-out. Although we will pursue our research in a retail setting, fingerprint recognition is a worldwide phenomenon, applied in different sectors. Disney World uses fingerprint scanners at the entrance to recognize season pass holders and resort guests. Retrieving cash at ATMs is another example of how fingerprint recognition is being used today. Grocery stores like Piggly Wiggly are among the first retailers where scanning your finger is as normal as searching for your credit card. According to Garf (2005), a retail analyst at the Bostonbased research firm AMR Research Inc., consumers do not express their concerns about privacy rights at Piggly Wiggly, but they embrace the fingerprint recognition system as a safer, more secure and more convenient checkout process. 3

19 Fingerprint scanning is one of the cheapest, most prolific and most accurate tools of biometrics (Brass 2003, Brownstein 2004, and Middlemiss 2004). The advantages of fingerprint recognition are multiple, both for the retail manager as well as for the consumer. In comparison to other biometric technologies, fingerprint identification has a low cost, a high accuracy and is easy to use. This dissertation concentrates on the consumer s benefits. Fingerprint recognition can create an overall customer convenience: it can reduce queuing times (because there is no need to search for any loyalty or credit card) and it eliminates the burden to remember any password or PIN. Replacing loyalty cards by storing the consumer s fingerprint patterns in an independent database is both a reduction of plastic waste as well as a simplification for the consumer. Fingerprint recognition guards the integrity of information and identification: identity theft is not possible despite several assumptions of amputated fingers in movies (Davies, 1994). Potentially, the storage of customers identification can lead to improved customer service, as management has a faster and better understanding of their customers. For the customer, enhanced security is another advantage of fingerprint recognition: no fraudulent use of credit cards and no need to take an amount of cash to the store. Furthermore, the decrease of queuing time consequently increases customer satisfaction (Jones et al., 2007). As mentioned before (see p.2), the arguments against the use of fingerprint recognition (or any other form of identification technology) are privacy concerns and trust issues (Miltgen et al., 2013). People seem to have fewer problems with opening up their lives on social networking platforms, than with exchanging private information for an enhanced security. The storage of consumers identities in databases is not well accepted by the public. People still associate the scanning of fingerprints with criminal convictions. Some people are concerned with hygiene. Others perceive the identification process as a cultural or religious taboo (Bleicher, 2005). In practice, some people can simply not or are difficult to be scanned: senior citizens and blue collar workers whose fingertips are worn down or people with a thin skin, injured fingers or limited movement can have problems with fingerprints scanning. (Clodfelter, 2010). Trocchia and Ainscoogh (2006) identified eight categories of concerns towards identification technology, summarized in three general themes (technology itself, security of collected data and issues with the technology). The eight categories are the following: convenience concerns (time and effort for consumers), health concerns, personal security concerns (assault fear), cost concerns, identity theft concerns, privacy concerns (where is the 4

20 personal information shared or is it used in an appropriate manner?), humanity concerns and morality concerns. Iris scanning Iris scanning is another form of identification technology. Iris cameras perform recognition detection of a person s identity by mathematical analysis of the random patterns that are visible within the iris of an eye from some distance. It combines computer vision, pattern recognition, statistical inference and optics. (findbiometrics, 2014). Iris scanning applications are mostly used in a governmental setting. Also, airports border controls (Canada and the Netherlands) and entrance controls at particular sites are situations where iris scanning is applicable. A typical advantage of iris scanning is that it is immune to environmental issues, except for its pupillary response to light (Lawson, 2003). Borre et al. (2004) claim that iris scanning belongs to the most secure identification technologies. Iris recognition supplies a person s identity at an enormous speed and resistance to False Matches. It eliminates the cultural and religious taboos, as well as the hygiene concerns related to fingerprint recognition. However, iris scanning has its disadvantages. It is a relatively new technology and therefore quiet expensive. Although the iris cannot be copied, the pupil can dilate due to alcohol consumption, causing deformation in the iris pattern (Arora, Vatsa, Singh and Jain, 2012). Border controls in several countries and Google s iris scanners to access databases are examples of the practical use of iris scanning. Few studies have researched the acceptance of iris scanning, but one study (Miltgen et al, 2013) introduces the acceptance of iris scanning in a consumer context. The results show that people are more inclined to accept the iris scanning when they see a greater perceived usefulness in the system, when they have a greater perceived compatibility, when there are facilitating conditions, when they are more trustworthy regarding to technology and when they have a higher personal innovativeness. People, who put greater value in perceived risks, are less inclined to accept iris scanning systems. Apart from the latter, the examples used in this subchapter are more applied in a non-retail setting. Facial recognition The third identification technology discussed in this dissertation is facial recognition. Facial recognition is, like fingerprint recognition, more common and more known in a marketing environment. As Weinzierl (2010) describes in his article The body as password, facial 5

21 recognition knows a strong trend towards a non-intrusive system, while it is, on the contrary, no longer immediately recognized by customers. Despite some cultural or religious taboos (Lawson, 2003), facial recognition is an acceptable biometric tool and is applied in various situations: surveillance in high crime neighborhoods, tracking or locating suspected criminals, law enforcement and the entertainment sector (both in social networking as well as in casinos). Facial recognition is widely accepted because of its use in voluntary situations: Facebook, for example, recently launched a facial recognition tool to help users tag photographs uploaded to Facebook (Buckley and Hunter, 2011). Facial recognition is not only used to identify people, it can also be useful in marketing applications to identify characteristics about an individual by observing the (potential) customer s behavior (Buckley and Hunter, 2011). There are already some examples of companies using facial recognition to enhance their marketing objectives: Kraft announced a joint project with an unidentified supermarket chain using the technology to identify and target those shoppers most likely to buy its products. Adidas is working with Intel, creating digital walls to recognize the gender and age of individuals walking by, followed by a presentation of advertisements of products that these people probably would buy (Buckley and Hunter, 2011). Tesco launches digital advertising screens at petrol stations in the UK. Using a facial detection technology, they are able to determine basic demographics. As a result, Tesco can deliver more measurable campaigns and reveals more relevant screen content to the customer. Although it seems to be futuristic, an Italian company is already producing shop dummies to observe the customer and report his behavior back to the retail manager. Being a passive biometric system, it is expected that privacy concerns may arise, so companies will have to inform individuals about the use of the facial recognition system. Although consumers seem to accept some loss of privacy in exchange for enhanced security (Davis and Silver, 2004), several perceived concerns and disadvantages create a lack of consumer acceptance of identification technologies. 6