Consumer Acceptance of Identification Technology

Ghent University

Faculty of Economics and Business Administration Academic Year 2013-2014

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

Ghent University

Faculty of Economics and Business Administration Academic Year 2013-2014

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

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

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: -0.45 voor IS&FR, 0.54 voor FP), de anxiety van consumenten ten opzichte van deze technologieën (β: -0.55 voor IS&FR, - 0.76 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

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 25 – 40 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).

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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.

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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.

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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 .......................................................................................................................... 11 Chapter III: Methodology.............................................................................................................. 14 Concepts, variables and specific hypotheses ................................................................................. 14 Dependent variables .................................................................................................................. 14 Independent variables ................................................................................................................ 15 Survey Content .............................................................................................................................. 20 Sample and Procedure ................................................................................................................... 21 Measurement of variables ............................................................................................................. 23 Scenario Questions .................................................................................................................. 23 Fingerprint recognition .......................................................................................................... 23 Iris scanning and facial recognition ...................................................................................... 23 General Questions ................................................................................................................... 25 Demographic Questions .......................................................................................................... 26 Chapter IV: Results and Interpretation .......................................................................................... 27 Measurement Model ...................................................................................................................... 27 Results ........................................................................................................................................... 30 Hypothesis testing .................................................................................................................... 30 The differences between the identification technologies ...................................................... 36 Significant differences between the biometric types: t-tests ...................................................... 36 V

Main and interaction effects of demographic variables on behavioral intention to accept identification technologies – General Linear Model ................................................................ 39 Chapter V: Limitations and Suggestions for Future Research ...................................................... 41 Chapter VI: Conclusion & Pragmatically implications ................................................................. 42 References ........................................................................................................................................I Appendices ................................................................................................................................. - 1 Appendix 1: Survey – Dutch version ......................................................................................... - 1 Appendix 1.1 Fingerprint recognition – Vingerafdruk herkenning ....................................... - 1 Inleiding ............................................................................................................................. - 1 Deel 1: Scenariovragen ...................................................................................................... - 2 Deel 2: Algemene Vragen .................................................................................................. - 7 Deel 3: Demografische vragen ......................................................................................... - 12 Appendix 1.2 Iris scanning en facial recognition ( iris scanning en gezichtsherkenning) ... - 13 Inleiding ........................................................................................................................... - 13 Deel 1: Scenariovragen .................................................................................................... - 14 Deel 2: Algemene Vragen ................................................................................................ - 18 Deel 3: Demografische vragen ......................................................................................... - 23 Appendix 2: Survey – English version (translation) ................................................................ - 24 Appendix 2.1 Fingerprint recognition .................................................................................. - 24 Introduction ...................................................................................................................... - 24 Part 1: Scenario questions ................................................................................................ - 25 Part 2: General questions.................................................................................................. - 29 Part 3: Demographic Questions........................................................................................ - 34 Appendix 2.2 Iris scanning and facial recognition ............................................................... - 35 Introduction ...................................................................................................................... - 35 Part 1: Scenario questions ................................................................................................ - 36 Part 2: General questions.................................................................................................. - 40 Part 3: Demographic Questions........................................................................................ - 45 Appendix 3: Sources Instruments measure .............................................................................. - 46 Appendix 4: Reliability Analysis output .................................................................................. - 47 Appendix 4.1 Fingerprint recognition .................................................................................. - 47 Appendix 4.2 Iris scanning and facial recognition ............................................................... - 55 Appendix 5: Factor Analysis constructs Question 3 & 4 ......................................................... - 63 VI

Appendix 5.1 Fingerprint recognition .................................................................................. - 63 Appendix 5.2 Iris scanning and facial recognition ............................................................... - 65 Appendix 6: Final list of items per construct (original: Dutch) ............................................... - 67 Appendix 6.1 Fingerprint recognition .................................................................................. - 67 Appendix 6.2 Iris scanning and facial recognition ............................................................... - 69 Appendix 7: Correlation Diagnostics and table ....................................................................... - 71 Appendix 7.1 Fingerprint recognition .................................................................................. - 71 Appendix 7.2 Iris scanning and facial recognition ............................................................... - 73 Appendix 8: mediator and moderator relations regression analysis ........................................ - 75 Appendix 8.1 mediation hypothesis 3b ................................................................................ - 75 Iris scanning and facial recognition ................................................................................. - 75 Fingerprint recognition ..................................................................................................... - 76 Appendix 8.2 mediation hypothesis 6 .................................................................................. - 77 Iris scanning and facial recognition ................................................................................. - 77 Fingerprint recognition ..................................................................................................... - 78 Appendix 8.3 moderation hypothesis 13b ............................................................................ - 79 Iris scanning and facial recognition ................................................................................. - 79 Fingerprint recognition ..................................................................................................... - 80 Appendix 8.4moderation hypothesis 14 ............................................................................... - 81 Iris scanning and facial recognition 14a ........................................................................... - 81 Iris scanning and facial recognition 14b .......................................................................... - 82 Iris scanning and facial recognition 14c ........................................................................... - 83 Fingerprint recognition 14a .............................................................................................. - 84 Fingerprint recognition 14b .............................................................................................. - 85 Fingerprint recognition 14c .............................................................................................. - 86 Appendix 9: T-tests between independent identification technologies .................................... - 87 Appendix 10: General Linear Model: interaction effects demographic variables on different identification technologies ....................................................................................................... - 90 Appendix 10.1 Demographic variables on BI of IS&FR and FP ......................................... - 90 Gender .............................................................................................................................. - 90 Age ................................................................................................................................... - 91 Highest obtained education .............................................................................................. - 92 Social status ...................................................................................................................... - 93 VII

Profession ......................................................................................................................... - 94 Appendix 10.2 Main and interaction effect of age regarding independent variables........... - 95 Perceived Usefulness ........................................................................................................ - 95 Perceived Ease Of Use ..................................................................................................... - 96 Relative Advantage .......................................................................................................... - 97 Privacy concerns .............................................................................................................. - 98 Technology Anxiety ......................................................................................................... - 99 Facilitating Conditions ................................................................................................... - 100 Appendix 10.3 Correlations age – independent variables .................................................. - 100 -

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List of Abbreviations IS

Iris Scanning

FR

Facial Recognition

FP

Fingerprint recognition

TAM

Technology Acceptance Model

CAT

Consumer Acceptance of Technology

PAD

Pleasure, Arousal and Dominance (model)

TR

Technology Readiness

TRAM

Technology Readiness Acceptance Model

DOI

Diffusion of Innovations Model

UTAUT

Unified Theory of Acceptance and Use of Technology (model)

BI

Behavioral Intention to accept the identification technology

BR

Behavioral intention to Recommend the identification technology

PU

Perceived Usefulness

PEOU

Perceived Ease Of Use

RA

Relative Advantage

PC

Privacy Concerns

TA

Technology Anxiety

FC

Facilitating Conditions

Exp

Experience

Innov

Innovativeness

SI

Social Influence

ID

Idealism

RE

Relativism

RC

Resultant Conservation

SE

Self-Enhancement

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List of Tables Table 1 Taxonomy of Ethical Ideologies (Forsyth, 1980, p.176). ............................................... 12 Table 2 Demographic characteristics of the sample ...................................................................... 22 Table 3 Cronbach's Alpha, number of items, mean, standard deviation of constructs ................. 28 Table 4 Results for the independent variables on Behavioral Intention and hypothesis testing ... 35 Table 5 Differences between constructs and biometric applications: mean, standard deviation, pvalue .............................................................................................................................................. 38 Table 6 Main effect of age on independent variables (PU, PEOU, RA, PC, FC) ......................... 40

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List of Figures Figure 1 Technology Acceptance Model (TAM) (Davis et al., 1989, p.985). ................................ 7 Figure 2 Consumer Acceptance of Technology (CAT) model (Kulviwat et al., 2007, p.1064). .... 9 Figure 3 Research model UTAUT (Venkatesh et al., 2003, p.447). ............................................. 10 Figure 4 Summary of Findings (Venkatesh et al., 2003, p. 468). ................................................. 10 Figure 5 Proposed Theoretical Framework (Miltgen et al., 2013, p.106). .................................... 11 Figure 6 Theoretical model of relations among 10 personal values (Steenhaut and Van Kenhove, 2006, p.140)................................................................................................................................... 13 Figure 7 Proposed Theoretical Framework ................................................................................... 19 Figure 8 Picture pay by touch: Mindfully.org, 2005, Piggly Wiggly Fingerprint Scanners, http://www.mindfully.org/Technology/2005/Piggly-Wiggly-Fingerprint11feb05.htm.).Appendix 1.1-2Figure 9 Facial Recognition camera ( Marieclaire.com, 2013, How Do You Shop? A Technology May Already Know. Hearst Communication, Inc., < http://www.marieclaire.com/blog/facialrecognition-shopping-technology>. ) ...................................................................Appendix 1.2 -14Figure 10 Picture pay by touch: Mindfully.org, 2005, Piggly Wiggly Fingerprint Scanners, http://www.mindfully.org/Technology/2005/Piggly-Wiggly-Fingerprint11feb05.htm.Appendix 2.1 -25Figure 11 Facial Recognition Camera (Marieclaire.com, 2013, How Do You Shop? A Technology May Already Know. Hearst Communication, Inc., < http://www.marieclaire.com/blog/facial-recognition-shopping-technology>. ) Appendix 2.2 -36-

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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.

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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 21st 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.

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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

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

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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

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.

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Technology Acceptance Models During the past years (and decades) several models are created, combined and adjusted in order to find an answer to the technology acceptance of consumers. In this study, the consumer acceptance of identification technology, different constructs and factors of different models are used to determine the main drivers that influence the decision to accept identification technology. Davis et al (1989) created the Technology Acceptance Model (TAM), measuring the intentions to use a specific system. Originated from the Theory of Reasoned Action (TRA) from Fishbein and Ajzen (1975), the TAM model’s behavioral intention is influenced by two beliefs: perceived usefulness and perceived ease of use. “Perceived usefulness is defined as the prospective user’s subjective probability that using a specific application system will increase his or her job performance within an organizational context” (Davis, 1989, p.985). “Perceived ease of use refers to the degree to which the prospective user expects the target system to be free of effort” (Davis, 1989, p.985). These concepts will also be used in this study, but in a consumer context. Both are significant indicators of people’s intentions. Perceived ease of use is both a direct indicator of behavioral intention to use, as well as an indirect indicator through perceived usefulness. As the model shows, external variables have an impact on the perceived usefulness and on the perceived ease of use of a technology. According to Davis (1989), perceived usefulness is a major factor of people’s intention to use new technologies (in his study the new technology was ‘using computers’) and perceived ease of use is a second significant determinant of these intentions. The following figure explains the Technology Acceptance Model.

Figure 1 Technology Acceptance Model (TAM) (Davis et al., 1989, p.985).

Several extensions of the TAM model exist. The study of Venkatesh and Morris (2000), for example, suggests that different genders are more or less impacted by the former indicators. 7

Men’s technology usage decisions are more strongly influenced by perceptions of usefulness, while women are more influenced by perceptions of ease of use. During the years, subjective norm knows little significant evidence of influence on behavioral intention to use technology (Venkatesh et al., 2003). Others claim there is a relation between subjective norm and perceived usefulness and behavioral intention to use (Schepers and Wetzels, 2007, Robinson 2006, Kulviwat 2008).

Kulviwat et al. (2007) discussed a unified theory of consumer acceptance technology, namely the Consumer Acceptance of Technology (CAT) model. They merge the TAM and PAD (Pleasure, Arousal and Dominance) (Mehrabian – Russell, 1974) models into one unified model, supporting their belief that cognitive factors are not enough to predict consumer acceptance of technology. According to them, the integration of affect cannot be neglected. Their findings indicate that pleasure (β = 0.41, p < 0.01) and arousal (β = 0.19, p < 0.01) are significant predictors related to the attitude to accept, while dominance has no significant impact (β = - 0.01, p = n.s.). These factors are not included in the model of this study; otherwise the model would become too crowded. However, it could be a suggestion for future research to implement the PAD model. They also implement another factor, relative advantage, and this item should have an indirect effect on the attitude towards technology via perceived usefulness (β = 0.95, p < 0.01), but no direct effect on the adoption intention (β = - 0.35, p = n.s.). “Relative Advantage is the degree to which an innovation is perceived as being better than its precursor” (Moore and Benbasat, 1991, p. 195). Individuals are therefore more likely to use new technologies when these technologies have a relative advantage over the alternatives. This variable is included in this study, because we believe relative advantage interacts directly with the intention to accept identification technologies. The next figure provides an overview of the CAT model.

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Figure 2 Consumer Acceptance of Technology (CAT) model (Kulviwat et al., 2007, p.1064).

Another factor to measure how consumers look upon technology is the Technology Readiness (TR). Technology readiness is the customer’s mental readiness to accept new technologies (Lilijander, 2006). TR consists of four dimensions: discomfort, insecurity, optimism and innovativeness. In order to integrate the TR into technology acceptance theory, Linc et al (2007) implemented it into the technology acceptance model (TRAM). Next, the UTAUT model was introduced and invented by Venkatesh, Morris, Davis GB and Davis FD (2003). These authors reviewed different technology acceptance models and formulated the Unified Theory of Acceptance and Use of Technology model. The UTAUT model defines three direct determinants of intention to use (performance expectancy, effort expectancy and social influence) and two direct determinants of usage behavior (intention and facilitating conditions). It also indicates the moderating influence of gender, age, experience and voluntariness on social influence (Venkatesh et al, 2003). The following two figures describe the proposed framework of the UTAUT and give a summary of the findings of the authors. In this study several determinants of the UTAUT model are used, namely facilitating conditions, social influence, anxiety and experience.

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Figure 3 Research model UTAUT (Venkatesh et al., 2003, p.447).

Figure 4 Summary of Findings (Venkatesh et al., 2003, p. 468).

Finally, Miltgen, Popovic and Oliveira (2013) proposed a new model by integrating the ‘Big 3’ models of technology acceptance together within a privacy context. Their study is particularly applicable on the acceptance of biometrics by end-users and is therefore an important basis for this study. The drivers to explain biometric acceptance come from the ‘Big 3’: the Technology acceptance model (TAM), the Diffusion Of Innovations model (DOI) of Rogers (1995) and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003) and 10

more importantly, form the trust and privacy literature. Not all the factors in this framework are used in this study, neither are the hypotheses, but it is a good basic model to introduce the theoretical framework. The following figure gives an image of their proposed framework:

Figure 5 Proposed Theoretical Framework (Miltgen et al., 2013, p.106).

This dissertation applies the constructs of the TAM model (Perceived ease of use and Perceived usefulness), the constructs of the UTAUT model (Social influence, Facilitating conditions, Anxiety and Experience) and the consequent factors: Behavioral intention to accept the technology (Biometrics) and Behavioral intention to recommend using the technology (Biometrics). Also, it includes constructs like innovativeness, trust in technology (Biometrics) and privacy concerns.

Several drivers of technology acceptance are implemented. It includes new factors and factors from the mentioned models to create a new model and to potentially define the main drivers of consumer acceptance.

Ethical ideologies As the evolution of biometrics grows, so do privacy concerns. People often have doubts about the storage of fingerprints and the invasion of their autonomy and identity through the use of iris scanning and facial recognition. Nowadays, the ethics of a company, product or technology 11

become increasingly important for consumers. Although biometric scanning systems do not record the entire imprint of a physical feature, but a template, time-invariant within some statistical limit (Alterman, 2003), people still concern about their privacy and the abuse of stored database information. This study examines if there is a relation between the ethical principles or ideologies of people and their intention to accept types of identification technology. The ethical ideologies of Forsyth (1980) are used, distinguishing two scales: idealism and relativism. “Idealism refers to the degree to which an individual believes that the right decision can be made in an ethically questionable situation” (Steenhaut and Van Kenhove, 2006, p.141). “Relativism, on the other hand, refers to the rejection of universal rules in making ethical judgments and focuses on the social consequences of behavior” (Steenhaut and Van Kenhove, 2006, p.141). The difference between the two constructs is adequately explained by Davis, Andersen and Curtis (2001, p.35): “Highly relativistic individuals ostensibly question the value of universal moral principles, while those adopting a less relativistic stance emphasize the importance of universal moral rules. Individuals high in idealism seek always to avoid harm by assuming that good is often mixed with bad. Less idealistic persons pragmatically assume good is often mixed with bad.” Both items are measured and scaled by the Ethics Position Questionnaire (EPQ). The table below details the four ethical perspectives based on these two dimensions. RELATIVISM IDEALISM

High

Low

High

Situationists

Absolutists

Reject moral rules:

Assume that the best

advocates individualistic

possible outcome can

analysis of each activity in

always be achieved by

each situation.

following universal moral rules.

Low

Subjectivists

Exceptionists

Appraisals based on

Moral absolutes guide

personal values and

judgments but pragmatically

perspective rather than

open to exceptions to these

universal moral principles.

standards, utilitarian.

Table 1 Taxonomy of Ethical Ideologies (Forsyth, 1980, p.76).

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Steenhaut and Van Kenhove (2006) extend the vision of Forsyth and examine the relationships among an individual’s personal values, ethical ideology and ethical beliefs. For this study, the constructs used to measure an individual’s personal values are important. On the one hand, dimension conservatism versus openness to change indicates a person’s ethical ideology and belief. Briefly, the conservation versus openness to change dimension defines “values in terms of the extent to which they motivate people to preserve the status quo and the certainty it provides in relationships with close others, institutions, and traditions versus following their own emotional and intellectual interests in unpredictable and uncertain directions” (Schwartz, 1992, p.43). Resultant conservation relates to terms like tradition, conformity and security. On the other hand, openness to change relates to terms like self-direction and stimulation. The index resultant conservation is formed by the items’ scores of conservatism minus the items’ scores of openness to change. Self-enhancement versus self-transcendence are “values in terms of the extent to which they motivate people to enhance their own personal interests (even at the expense of others) versus to transcend selfish concerns and promote the welfare of others, close and distant, and of nature” (Schwartz, 1992, p. 43). Self-enhancement refers to power, achievement and hedonism; self-transcendence on the other hand refers to the value types universalism and benevolence. Index self-enhancement is formed by the items’ scores of selfenhancement minus the items’ scores of self-transcendence.

Figure 6 Theoretical model of relations among 10 personal values (Steenhaut and Van Kenhove, 2006, p.140).

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The next chapter offers the methodology of this study: concepts, variables hypotheses, the model’s framework, survey content, sample and procedure and measurement variables.

Chapter III: Methodology Concepts, variables and specific hypotheses As mentioned before, the study mainly contains determinants of the theoretical framework of Miltgen et al. (2013) and of the unified view of technology acceptance model (UTAUM) of Venkatesh et al. (2003). The variables are categorized in two large groups: dependent variables and independent variables. The latter consists of potential enablers and barriers of the acceptance of identification technology in a real life setting. Below every concept and variable used, specific hypotheses are formulated. Each hypothesis is separately analyzed for iris scanning and facial recognition and for fingerprint recognition.

Dependent variables Behavioral Intention to accept the identification technology (BI) In many of the models that we discussed, behavior is the result of, on the one hand, beliefs about the technology, and on the other hand, a set of affective responses to the behavior (Miltgen et al., 2013). This study questions the behavioral intention to accept identification technology based on background literature and self-composed questions.

Behavioral intention to Recommend the identification technology (BR) Whether a model is successful or not, does not only depend on the intention to accept or use the technology but also on the intention to recommend a used technology. Behavioral intention to recommend the identification technology is the second dependent variable. Now more than ever, social network sites and fora increase the opportunity for people to discuss their opinions with fellow peers. To measure the success of a product, companies often use the Net Promotor Score1. The questions about the intention to recommend the identification technology are developed by ourselves, but based on previous literature.

1

Net promotors are defined as the percentage of customers who are promotors of a brand or company (score 9/10). The Net promotor score are those promotors minus the percentage who are detractors (score 0-6).

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H1: The intention to accept the biometric system positively influences the intention to recommend this technology to others.

Independent variables Perceived Usefulness (PU) Perceived usefulness is defined as “the degree to which a person believes that using a particular system would enhance his or her job performance” (Davis, 1989, p.320). Perceived usefulness is a well-known determinant of the TAM model and, as mentioned before (see p.7), has the most significant impact on the behavioral intention to accept a new technology and has been proven in many previous studies. H2: The greater the perceived usefulness, the greater the intention to accept a biometric system.

Perceived Ease Of Use (PEOU) Perceived ease of use refers to ‘The degree to which a person believes that using a particular system would be free of effort.’(Davis, 1989, p.320). Perceived ease of use is a significant indicator and originates directly from the TAM from Davis (1989). Perceived ease of use belongs to the possible benefits of identification technology. As seen before (see p.7), perceived ease of use has both a direct and an indirect effect on behavioral intention to use. (Davis, 1989). H3a: The greater the perceived ease of use, the greater the intention to accept a biometric system. H3b: The perceived ease of use has an indirect, positive effect on the intention to accept a biometric system, through perceived usefulness. Thus, perceived usefulness will be the mediator in the relation perceived ease of use – behavioral intention to accept.

Relative Advantage (RA) Relative advantage belongs, besides to other models (Venkatesh et al., 2003), also to the Innovation Diffusion Theory (IDT): a model used since the 1960s. The IDT was namely applicable in different industries, but Moore and Benbasat (1991) refined the set of constructs used in Roger’s IDT to fit with their study on individual technology acceptance. They describe relative advantage as “the degree to which an innovation is perceived as being better than its precursor” (Moore and Benbasat, 1991, p.195). Relative advantage can be compared with perceived usefulness, where the perceived job enhancement of using the technology is similar to 15

applying the technology instead of an alternative form to obtain a better performance. That is why we perceive relative advantage to have a direct, positive effect on the behavioral intention to accept a technology. H4: The more perceived relative advantage the identification technology offers, the more an individual will have the intention to accept the identification technology.

Privacy Concerns (PC) Miltgen et al. (2013) recognized that nowadays, the research to consumer acceptance of a technology is not complete without the implementation of determinants of the privacy and trust literature. Privacy concerns are an important construct since biometric identification is a relatively new concept in retail setting and the public does not know for sure where the data storage takes place, who can access their personal information and whether these systems do not increase possible fraud and identity theft. Miltgen et al. (2013) indicated that the privacy concerns about biometrics have an indirect impact on behavioral intention to use it, through perceived risk. This study does not use the term perceived risks, but directly links privacy concerns to intention to acceptance. H5: Individuals with higher privacy concerns will have less intention to accept a biometric system.

Technological Anxiety & Technological Trust (TA) In this study, the above constructs are clearly opposites. Technological Anxiety is the anxious reaction towards the use of technology or towards the performance of a behavior using the technology. Technological trust is the opposite. Trust reduces the uncertainty of a behavior and makes us familiar with the situation. As a consequence, trust in an identification technology reduces the need to be anxious; the more trustworthy, the less anxious an individual is. Therefore, technological trust items are recoded and used in the technology anxiety construct. H6: The more anxious an individual is towards technology, the more privacy concerns the individual adopts and thus the less inclined he or she is to accept the identification technology. Therefore, privacy concerns will be a mediator between technology anxiety and behavioral intention to accept the biometric system. H7: Consumer’s anxiety in the technology has a negative influence on the intention to accept a biometric system.

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Resultant Conservation and Self-Enhancement (RC) (SE) Although Schwartz (1993) investigated resultant conservation and self-enhancement in an ethical study, we will relate these determinants to this marketing-related study. In the literature review (see p.13), a figure adapted and modified from Schwartz (1992, p.45) is implemented, explaining the four dimensions in this model. H8: The more resultant conservation oriented a consumer is, the more he or she is inclined to reject the identification technology. H9: The more self-enhanced a consumer is, the more he or she is inclined to accept the identification technology.

Idealism and Relativism (ID) (RE) Forsyth (1980) constructed taxonomy of ethical ideologies. Based on two dimensions (idealism and relativism), he developed four categories of individuals regarding ethical perspectives. These categories are not further discussed in this study, but it is investigated whether the behavioral intention to accept identification technologies is influenced by the fact that individuals are idealistic or relativistic oriented. “Idealism refers to the concern of the individual to the welfare of others, while relativism refers to the extent that individuals base their beliefs on moral judgments more on universal ethical rules” (Forsyth et al., 1988, p. 244). The coherent hypotheses are: H10: The more idealistic oriented a consumer is, the less he or she will have the intention to accept the identification technology. H11: The more relativistic oriented a consumer is, the more he or she will have the intention to accept the identification technology.

Innovativeness (Innov) The determinant ‘innovativeness’ comes from the Technology Readiness model. It refers to “people’s propensity to embrace and use new technologies for accomplishing goals in home life and at work.” (Parasuraman, 2000, p. 308). The most important drivers of technology readiness are optimism and innovativeness. As Technology Readiness is included in The Technology Acceptance Model by Lin et al. (2007), innovativeness, as a determinant of technology readiness, can have a direct impact on the behavioral intention to accept a new technology. Also, Miltgen et al. (2013) confirm that innovativeness directly influences perceived ease of use. The coherent hypotheses will be: 17

H12a: Higher innovative consumers intend to perceive ease of use more positively. H12b: The more personal innovativeness a consumer has, the more willingly he or she will accept identification technology.

Facilitating Conditions (FC) Facilitating conditions are defined as “the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system.” (Venkatesh et al, 2003, p.453). Examples of facilitating conditions in this research are: the fact that you only need your finger and no other resources to access your bank account, assistive explanations on how to use the technology and the widespread use of the technology (one loyalty card for all stores). H13a: The more facilitating conditions consumers have, the more they will intend to accept the identification technology. H13b: The influence of facilitating conditions on behavioral intention to accept biometrics will be moderated by age, such that the effect will be stronger for older consumers.

Subjective norm or Social influence (SI) Subjective norm or social influence is defined as “the person’s perception that most people who are important to him think he should or should not perform the behavior in question” (Fishbein and Ajzen, 1975, p.302). Social influence is investigated in many studies, but implemented as a real indicator in the UTAUT by Venkatesh et al. (2003). This study will use the term ‘Social Influence’. In former models, there was no significant relationship between social influence and the intention, at least not in a voluntary situation. This study does also not perceive that social influence has a direct impact on behavioral intention, but that the determinant will be moderated by gender, age and experience. (Venkatesh et al., 2003). Venkatesh and Morris (2000) discussed that women consider more the opinions of peers and especially in the early stages of the technology introduction. H14: The impact of social influence on behavioral intention in a voluntary retail situation will be moderated by gender (Gen)(a), age (Age)(b) and experience (Exp)(c), such that the effect will be stronger for women, older people and when the individual lacks experience.

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Experience with the technology (Exp) Experience will be discussed as a direct determinant on behavioral intention, and as a moderator between independent and dependent variables. Individuals having an Iphone 5s already have a certain experience with biometrics: they can unlock their phone by using a fingerprint recognition system. That is just one example of the widespread increasing use of fingerprint recognition. People become familiar with the technology in different sectors: they are becoming experienced. The prediction is that an enhanced experience with biometrics will enhance the likelihood of acceptance. H15: The more experienced a consumer is with identification technology, the more intention he or she has to accept the technology in a retail setting. Previous research has confirmed the moderating effect of experience. Although experience was not directly incorporated in most of the models, Karahanna et al. (1999) discovered that subjective norm became less important with increasing experience and Davis et al. (1989) discovered that ease of use becomes non-significant with increased experience. This study only investigates the former.

The following figure shows the proposed theoretical framework.

Figure 7 Proposed Theoretical Framework

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Survey Content The prime goal of this study is to investigate the behavioral intention to accept biometrics in a retail environment. Therefore the survey has been distributed among different age categories. The original survey (Pretest) was one relatively large survey, distributed offline among several persons. Of each age category, three well-known but critical respondents were asked to fill in this questionnaire and to write down their remarks on the paper version of the survey. It contained four parts: one about a scenario regarding fingerprint recognition, one about a scenario regarding iris scanning and facial recognition, the third part were general questions (to measure several independent variables) and the final part contained demographic questions. As the remarks were collected and analyzed, it became clear that several people had difficulties filling in the general questions due to a different perspective on fingerprint recognition and on iris scanning and facial recognition. Furthermore, there were some remarks about the lay-out, the ambiguity of some questions and the length of the entire survey. As a consequence, the survey was split into two separate surveys: one about fingerprint recognition (and an adapted scenario question) and one about iris scanning and facial recognition with an adapted scenario. Thus, the two final surveys each contained one scenario section, one section with general questions and one section with demographic questions. Furthermore, some questions were made more comprehensive for the younger and older age categories (or were simply deleted). One additional question about personal values was added as well. The final adaptation was the re-categorization of the age classes and the inclusion of more social statuses and professions. There were four large questions in the scenario section and the following variables were measured: behavioral intention to accept the biometric application (6 items), behavioral recommendation to accept the biometric application (3 items) , the perceived usefulness (5 items in the iris & facial survey, 6 items in the fingerprint survey), the perceived ease of use (3 items), the relative advantage (3 items), privacy concerns (5 items), technology anxiety & technology trust (5 items), facilitating conditions (3 items) and experience (3 items). The general questions section is formed by four questions about: innovativeness (3 items), social influence (3 items), idealism and relativism (20 items) and personal values (10 items). The last demographic part analyzed gender, age, highest obtained education, social status and profession.

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Sample and Procedure Previous studies have examined the acceptance of biometrics (in a retail setting) among students. The aim of this study was to go beyond this age category and to examine the acceptance of biometrics among every person able to go to a retail setting, in other words, among all age categories. In order to fill this age gap, the survey was distributed both online and offline. The two surveys were distributed in Dutch to reach respondents of all age categories, educations and professions. The online surveys were made on Qualtrics and distributed via social network sites (Facebook, LinkedIn) and via email. The offline versions were shared among friends, family and acquaintances and were handed out in a hair salon, while people were waiting. In total there were 252 respondents collected regarding the online and offline fingerprint questionnaire and 198 respondents online and offline regarding the iris scanning & facial recognition questionnaire. A lot of respondents did not fill in the complete survey. Therefore, only 187 respondents of the fingerprint recognition survey and 151 respondents of the iris scanning and facial recognition survey remained as valuable. Table 2 (see p.22) gives an overview of the characteristics of the sample.

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Fingerprint recognition

Iris scanning &

(%)

facial recognition (%)

Gender Male

35.3

33.1

Female

64.7

66.9

< 18

5.3

5.8

18 – 24

31.1

34.8

25 – 34

7.9

10.1

35 – 44

16.6

13.8

45 – 54

17.2

18.1

55 – 65

11.9

11.6

> 65

9.9

5.8

Primary Education

0.7

1.4

Secondary Education short-type

11.9

7.2

Secondary Education long-type

29.8

31.9

Professional Bachelor

32.5

31.2

Academic Bachelor

10.6

13.0

Academic Master

14.6

15.2

Single

21.2

22.5

In a relationship

19.2

25.4

Cohabiting

7.9

9.4

Married

47.7

39.9

Divorced

2.0

1.4

Widow/Widower

2.0

1.4

Student

31.5

39.1

Unemployed/Job Seeker

4.7

2.9

Worker

2.7

7.2

Employee

22.8

24.6

Manager

4.7

3.6

Official

7.4

5.8

Self-employed

10.7

6.5

Retired

15.4

10.1

Age

Highest obtained education

Social Status

Profession

Table 2 Demographic characteristics of the sample

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Measurement of variables Scenario Questions As mentioned before, the goal of the scenario question was to measure specific variables regarding the biometric application itself. The two scenarios are included in the surveys in appendix 1(Dutch version) and 2 (English version), but because of their importance, they are also outlined here.

Fingerprint recognition The scenario is as follows: you decide to do some grocery shopping in your favorite department store. After you have – again – loaded too many items in your shopping cart, you finally arrive at the check-out. Everything seems the same, but your store has changed one element. You can still pay by credit or debit card or cash, but now there is also an additional device at the check-out: a fingerprint scanner. With this device, you can pay by just putting your finger on the device. The latter recognizes you (and identifies you) and automatically deduct your payable amount from your bank account. Thus, no need for a credit/debit card, a PIN or for cash money to finish your payment. You just need that object that you always have with you: your finger. Only at your first visit to the store, you probably would have to go through a registration procedure, but once this is done, your finger is all you need to pay. What is your decision when you arrive at the check-out and how do you view this payment method? Please, fill in the next questions.

Iris scanning and facial recognition The situation is as follows: you arrive at one of your favorite department stores. When entering the store, you see a sign mentioning the following text: ‘We use identification technology to keep you more satisfied and to let you go home with a good feeling.’ What does this mean? There have been placed several cameras in the department store. Cameras, which can scan your irises and facial expressions. The scanning of the irises is to recognize you as a person, the scanning of your facial expression is to identify your feelings about certain products. 23

For example: when you would stop at a certain champagne and your facial expression seems positive, the camera will identify you (by scanning your face) and in the next array you will receive a personal message on a display about certain appetizers that would perfectly match with that same champagne. You will receive an explanation about those appetizers, where they are located in the store, how much they cost: hence, a personalized advertising.

Thus, you are being watched during your visit in the department store, but you receive useful information about products, chosen by you. How do you view this new technology?

An explanation of the measurement of each variable is given in the following paragraph. The questions originate from other authors, but were transformed to fit into this study and to match with these biometric applications (see appendix 3). An example of the entire survey is submitted at the end of this dissertation in appendices 1 and 2. All scenario questions except one were measured on a 5-point Likert scale, ranging from 1 ‘totally disagree’ to 5 ‘totally agree’. The detailed items can be found in appendix 6. Behavioral intention was measured in question 1 and 2, but under different scales. Question 1 asked directly whether the respondent would accept the technology in his or her favorite department store. The semantic differential bipolar scale measured the intention of the respondent to engage in a specified behavior (on a scale from 1 to 5). For example: highly unlikely (1), unlikely (2), no opinion (3), likely (4), highly likely (5). Question 2 measured the behavioral intention on a 5-point Likert scale, based on the research of Venkatesh and Davis (2000) and Venkatesh et al. (2003). Behavioral recommendation was also measured in question 2 by three sub questions (5-point Likert scale) by Price and Arnould (1999), describing word - of - mouth intentions. Perceived usefulness was measured by item 1, 3, 4 and 5 of question 3 of the iris scanning and facial recognition survey and by item 1, 3, 4, and 6 of question 3 of the fingerprint recognition survey. Question 3 was also measured by a 5-point Likert scale regarding both surveys. Two of the five items were determined by Miltgen et al. (2013), who deducted them from Davis (1989). The other two items were directly deducted from Davis (1989). Some sub questions were left out in the analysis because there was no match with the other items (Cronbach Alpha & factor analysis: see appendix 4, 5 and 6), as explained later in this dissertation (see p. 27).

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Perceived ease of use was obtained by item 6 and 7 of question 3 in the iris scanning and facial recognition survey and question 7 and 8 in the fingerprint recognition survey. One of them was determined by Miltgen et al. (2013) who again deducted them from Davis (1989). The other item originated from Venkatesh and Davis (2000). Relative advantage was also measured in question 3 and contained the last four items of this question (in both surveys). Although the last three originated from Walker et al. (2002), the first item was primitively measured for the perceived ease of use construct by Miltgen et al. (2013), but had a better match with the relative advantage construct (see p.27 ). Privacy concerns were measured by the first five items in question 4. Question 4 was also measured by a 5-point Likert scale. The first three were deducted from Miltgen et al. (2013), while this paper took the items from Fogel and Nehmad (2009). The other two items are self – developed. Technology anxiety and technology trust were combined into one construct (high cronbach Alpha, see p.28). The first three items were technology anxiety related, while the other two were trust in technology. The items for technology anxiety were created by the author of this study, but the reversed technology trust items were subtracted from Miltgen et al. (2013), who took the items from Pavlou (2003). Facilitating conditions consisted out of the items 11 to 13 from question 4. For fingerprint recognition, only items 11 and 13 were valuable (see p.29). The first two were determined by the dissertation of Venkatesh et al. (2003), but the third was invented in this study Experience was measured by the last three items of question 4 and these items were constructed by common sense. General Questions The general questions were measured in question 5, 6 and 7. In question 5, the following two items were analyzed: innovativeness and social influence, both on a 5-point Likert Scale. Innovativeness was measured by the first three items of question 5, all deducted from the paper of Miltgen et al. (2013), who took the questions of this construct from Yi et al. (2006). Social influence was measured through the last three items of this question that were created by the author of this study. Ethical Ideologies. Question 6 analyzed whether the respondents were idealistic or relativistic oriented (i.e. ethical ideology). The Ethics Position Questionnaire of Forsyth (1980) was used. Respondents were asked to rate their (dis)agreement with the 20 items on a 5-point Likert Scale 25

ranging from 1 ‘totally disagree’ to 5 ‘totally agree’. The translation of this EPQ for the Dutch survey was obtained from Van Kenhove, Vemeir and Verniers (2001). Personal Values. Question 7 handles the measurement of personal values. The personal values were originally assessed by Schwartz (1994) using 56 items, but were reduced to 10 personal values and used by Lindeman and Verkasalo (2006). Different values belonged to different dimensions and with these measurements, the resultant conservations construct and the selfenhancement construct were formed to see whether people’s conservatism or self-enhancement were related to their behavioral intention to accept biometrics in a retail setting. Each value was rated on a 9-point importance scale ranging from 0 ‘opposed to my principles’, to 1 ‘not important’ to 4 ‘important’ to 8 ‘supreme important’. Demographic Questions The demographic questions contained standard questions. They measured gender (male, female), age (<18, 18 – 24, 25 – 34, 35 – 44, 45 – 54, 55 – 65, >65), highest obtained education (primary education, secondary education short-type, secondary education long-type, professional bachelor, academic bachelor, academic master), social status (single, in a relationship, cohabiting, married, divorced, widow/widower), profession (student, unemployed/job seeker, worker, employee, manager, official, self-employed, retired). These were all measured by multiple choice questions.

The survey was distributed in Dutch, so it is important to note that every value, item and question was translated from English to Dutch. It is important to take into consideration that small differences could occur if the survey would be distributed in English. The next chapter will provide the results and interpretations of this study.

26

Chapter IV: Results and Interpretation Measurement Model This section provides an outline on how the final measurement model was developed by different analyses with regard to the iris scanning & facial recognition and the fingerprint recognition survey. A Reliability Analysis was implemented in order to verify the consistency between the items in the survey and to construct the dependent and independent variables. A good cronbach’s alpha for every perceived construct was followed by calculating the mean of the relevant items to create that construct. A summary of the constructs, their cronbach’s alpha’s, means and standard deviations of both the identification technologies can be found in table 3 (see p.28). A detailed outline of each item’s analysis can be found in appendix 4 and 5. To form some constructs, some items were left out in order for the construct to be consistent. The second item in question three measuring perceived usefulness was not consistent with the other items (cronbach’s α: 0.76, cronbach’s α if it item deleted for item 2 was 0.84 and cronbach’s α 0.82 and cronbach’s α if it item deleted for item 2 was 0.83 in the fingerprint recognition analysis). In the fingerprint recognition analysis, the fifth item of the construct perceived usefulness was left out as well, because this item was not used in the iris scanning and facial recognition survey, which would otherwise compromise comparison later. It would be difficult to compare the results of both biometric systems when they do not contain the same items. The prospected element of perceived ease of use consisted of three items, but the third item matched better with the relative advantage construct (otherwise the cronbach’s α of perceived ease of use was 0.65). The prospected element of perceived ease of use in the fingerprint recognition survey consisted of three items and would be acceptable in terms of reliability (cronbach’s α: 0.79), but in order to be consistent with the other survey, the third prospected item of perceived ease of use was left out to match it with the relative advantage construct. The second item of the prospected ‘facilitating conditions’ factor was left out in the fingerprint recognition survey due to inconsistency with the other items. (Cronbach’s α: 0.51). This is not consistent with the other survey, but such a low cronbach’s alpha would not show reliability. The last item of the prospected factor ‘relativism’ was eliminated due to a high cronbach’s α it item deleted (cronbach’s α: 0.73 in the two surveys). Finally, there exists a low cronbach’s alpha for dimension self-transcendence (cronbach’s α: 0.55 for IS&FR & 0.61 for FP), but there will be no reduction of items in this construct because it only has two items.

27

An exploratory factor analysis was used to create the constructs of question 3 (perceived usefulness, perceived ease of use, relative advantage, privacy concerns, technology anxiety, facilitating conditions and experience). The factor analysis (see appendix 5) shows that there are seven different constructs, explaining 72 % of the variance in the iris scanning and facial recognition output and 70 % in the fingerprint recognition output. One factor measures the Perceived Usefulness, one Perceived Ease of Use, one Relative Advantage, one Privacy Concerns, one Technology Anxiety, one Innovativeness and one the Experience The outcome of the factor analysis was verified by a reliability analysis for each construct (appendix 4).

Iris scanning & facial recognition Construct

Cronbach’s

Number

Number

α

of

of final

original

items

Mean

Fingerprint recognition Standard

Cronbach’s

Number

Number

Deviation

α

of

of final

original

items

items Behavioral

Mean

Standard Deviation

items

0.91

6

6

2.7

0.98

0.97

6

6

3.23

1.21

0.95

3

3

2.63

1.03

0.96

3

3

3.08

1.12

0.84

5

4

2.67

0.86

0.82

6

4

3.54

0.86

0.76

3

2

3.48

0.82

0.75

3

2

3.93

0.76

0.89

3

4

2.56

0.81

0.78

3

4

3.44

0.78

0.83

5

5

3.77

0.77

0.86

5

5

3.50

0.80

0.78

5

5

3.04

0.73

0.78

5

5

2.64

0.71

Intention to accept (BI)

Behavioral Recommendation (BR)

Perceived Usefulness (PU) Perceived Ease Of Use (PEOU)

Relative Advantage (RA) Privacy Concerns

Technology Anxiety (TA)

28

Iris scanning & facial recognition Construct

Cronbach’s

Number

Number

α

of

of final

original

items

Mean

Fingerprint recognition Standard

Cronbach’s

Number

Number

Deviation

α

of

of final

original

items

items

Mean

Standard Deviation

items

0.65

3

3

3.05

0.8

0.63

3

2

3.38

0.68

Experience (Exp)

0.79

3

3

1.92

0.86

0.77

3

3

1.94

0.85

Innovativeness

0.85

3

3

2.79

0.86

0.83

3

3

2.86

0.85

0.74

3

3

3.29

0.80

0.79

3

3

3.26

0.77

Idealism (ID)

0.91

10

10

3.73

0.61

0.84

10

10

3.66

0.53

Relativism (RE)

0.72

10

9

3.42

0.49

0.73

10

9

3.33

0.52

Dimension Self-

0.69

3

3

4,73

1.50

0.74

3

3

4.80

1.47

0.69

2

2

5,43

1.41

0.76

2

2

5.49

1.54

0.55

2

2

5,71

1.27

0.61

2

2

5.95

1.27

0.75

3

3

5,48

1.33

0.74

3

3

5.73

1.25

Facilitating Conditions (FC)

(Innov)

Social Influence (SI)

Enhancement

Dimension Openness to Change

Dimension SelfTranscendence Dimension Conservation

Table 3 Cronbach's Alpha, number of items, mean, standard deviation of constructs

29

Results In the next chapter the hypotheses are tested one by one, describing each identification technology per hypothesis. To end this chapter the differences in variables between iris scanning and facial recognition and fingerprint identification are compared. The hypotheses are confirmed or not by a regression analysis. Hypothesis testing

H1: The intention to accept the biometric system positively influences the intention to recommend this technology to others. For iris scanning and facial recognition, this hypothesis could be confirmed (R²=0.72, β = 0.85 and p < 0.001). This hypothesis could also be confirmed for fingerprint recognition (R² = 0.73, β = 0.85 and p < 0.001).

The following hypotheses are analyzed and tested chronologically. However, hypotheses 2, 3a, 4, 5, 7, 8, 9, 10, 11, 12b, 13a and 15 all make predictions with different independent variables but with the same dependent variable: behavioral intention to accept. Normally, a multiple regression based on these variables should be performed but this will not be the case due to high correlations between several independent variables. (See appendix 7). A single linear aggression analysis is conducted for each independent variable separately. Furthermore, there are two hypotheses testing mediation, and two testing moderation (see appendix 8 for output). For each hypothesis, there are results and interpretations for both biometric systems. A summarized table (table 4) is included on page 35.

H2: The greater the perceived usefulness, the greater the intention to accept a biometric system. For iris scanning and facial recognition this hypothesis can be confirmed (R²=0.45, β = 0.67 and p < 0.001). The same can be stated for fingerprint recognition (R²=0.49, β = 0.70 and p < 0.001).

30

H3a: The greater the perceived ease of use, the greater the intention to accept a biometric system. For iris scanning and facial recognition, the hypothesis can be confirmed (R²=0.05, β = 0.24 and p = 0.004). The same goes for fingerprint recognition (R² = 0.23, β = 0.48 and p < 0.001).

H3b: The perceived ease of use has an indirect, positive effect on the intention to accept a biometric system, through perceived usefulness. Therefore, perceived usefulness will be the mediator in the relation perceived ease of use – behavioral intention to accept. The previous hypothesis stated that there is a significant relation between the perceived ease of use and the behavioral intention to accept (see hypothesis 3a). According to the Method of Barry and Kenny (1986), there is only a mediation when the independent variable (Perceived Ease Of Use) has also a significant relation with the mediator (Perceived Usefulness) (p < 0.001) and when the mediator has a significant relation with the dependent variable (Behavioral Intention to accept), which has been proven in hypothesis 2 (see p.30). All three conditions are met for both biometric applications. However, Perceived Ease Of Use is not significant when we include Perceived Usefulness in the model (p = 0.88 for iris scanning and facial recognition and p = 0.45 for fingerprint recognition). This means that hypothesis 3b counts for both biometric systems and is a complete mediation. (See appendix 8.1).

β = 0.34 IS & FR

Perceived Usefulness

β = 0.67 IS & FR β = 0.66 FP

β = 0.65 FP

Perceived Ease Of Use

Behavioral Intention β = 0.24 IS & FR β = 0.48 FP

H4: The more perceived relative advantage the identification technology offers, the more an individual will have the intention to accept the identification technology. Relating to the iris scanning and facial recognition analysis, this hypothesis can be confirmed (R² = 0.4, β = 0.63, p < 0.001). The same goes for the fingerprint recognition analysis. (R² = 0.58, β = 0.77, p < 0.001).

31

H5: Individuals with higher privacy concerns will have less intention to accept a biometric system. Again, for both biometric applications, the hypothesis can be confirmed. (Iris scanning & facial recognition: R² = 0.20, β = -0.45, p < 0.001 and fingerprint recognition: R² = 0.28, β = - 0.54, p < 0.001).

H6: The more anxious an individual is towards technology, the more privacy concerns the individual adopts and thus the less inclined he or she is to accept the identification technology. Therefore, privacy concerns will be a mediator between technology anxiety and behavioral intention to accept the biometric system. For iris scanning and facial recognition, this mediation hypothesis can be confirmed; it is, however, not a complete mediation but a partial mediation. The significance between the independent variable (Privacy Concerns) and the dependent variable (Behavioral Intention) can be confirmed by hypothesis 5. Privacy concerns and technology anxiety (mediator) have also a significant relation (p< 0.001). The two variables, put in one model, both turn out to be significant regarding their relation with behavioral intention to accept (TA: p<0.001 and PC: p = 0.015). That is why this mediation is partial (see appendix8.2 for more detailed output).

β = 0.56 IS & FC

Technology Anxiety

β = - 0.44 IS & FC β = - 0.66 FR

β = 0.55 FR

Privacy Concerns

Behavioral Intention β = - 0.45 IS & FC β = - 0.54 FR

H7: Consumer’s anxiety in the technology has a negative influence on the intention to accept a biometric system. For both biometric systems, the hypothesis can be confirmed (Iris scanning & facial recognition: R²=0.30, β = - 0.55, p < 0.001 and fingerprint recognition: R²=0.57, β = - 0.76, p < 0.001). Remarkable is that technology anxiety has more impact on the BI than privacy concerns!

32

H8: The more resultant conservation oriented a consumer is, the more he or she is inclined to reject the identification technology. H9: The more self-enhanced a consumer is, the more he or she is inclined to accept the identification technology. For iris scanning and facial recognition, hypothesis 8 cannot be confirmed (R²=0.002, β = 0.01, p = 0.250), but hypothesis 9 can be (R²=0.03, β = 0.18, p = 0.034). Therefore, people who are more egocentric oriented (power, achievement, hedonism) are inclined to accept the iris scanning and facial recognition in a retail setting. On the contrary, for fingerprint recognition, neither construct provide enough significance to confirm the hypotheses (H8: R²= 0.006, β = - 0.075, p = 0.361; H9: R²=0.02, β = 0.141, p = 0.08).

H10: The more idealistic oriented a consumer is, the less he or she will have the intention to accept the identification technology. H11: The more relativistic oriented a consumer is, the more he or she will have the intention to accept the identification technology. For both iris scanning & facial recognition and fingerprint recognition, the above hypotheses cannot be confirmed (H10: Iris scanning & facial recognition: R²=-0.006, β = 0.04, p = 0.631; fingerprint recognition: R²= 0.0, β = 0.08, p = 0.312; H11: iris scanning & facial recognition: R² = - 0.001, β = 0.08, p = 0.360; fingerprint recognition: R²= -0.004, β = -0.05, p = 0.556).

H12a: Higher innovative consumers intend to perceive ease of use more positively. Regarding to iris scanning and facial recognition, this hypothesis cannot be confirmed. (R²= -0.003, β = 0.06, p = 0.672). On the other hand, the hypothesis can be confirmed regarding to fingerprint recognition (R²= 0.05, β = 0.241, p = 0.002). People who are more innovative seem to perceive the use of fingerprint recognition systems more easily, than the use of iris scanning and facial recognition applications. H12b: The more personal innovativeness a consumer has, the more willingly he or she will accept identification technology. The hypothesis can also be confirmed for both biometric applications (Iris scanning & facial recognition: R²= 0.11, β = 0.34, p < 0.001; fingerprint recognition: R²=0.02, β= 0.16, p = 0.039).

33

H13a: The more facilitating conditions consumers have, the more they will intend to accept the identification technology. For both biometric applications, this hypothesis can be confirmed. (Iris scanning and facial recognition: R²=0.23, β = 0.49, p <0.001 & fingerprint recognition: R²=0.24, β = 0.49, p <0.001). For fingerprint applications as well as for iris or facial applications, consumers seem to better accept the applications when there are facilitating conditions involved. H13b: The influence of facilitating conditions on behavioral intention to accept biometrics will be moderated by age, such that the effect will be stronger for older consumers. For iris scanning and facial recognition, this hypothesis cannot be confirmed and because of the insignificance of the moderated relation (significant F change: p = 0.308), there is no use in determining any β coefficients. The same conclusion can be made for fingerprint recognition (significant F change: p = 0.08), however the p-value lies closely to 0.05, which indicates an almost significant moderation effect (see appendix 8.3 for detailed output).

H14: The impact of social influence on behavioral intention in a voluntary retail situation will be moderated by gender (Gen) (a), age (Age)(b) and experience (Exp)(c) , such that the effect will be stronger for women, older people and when the individual lacks experience. Hypothesis 14a cannot be confirmed, neither for iris scanning & facial recognition, nor for fingerprint recognition (significant F change for iris scanning & facial recognition: p = 0.79 and for fingerprint recognition p = 0.93). There is also no significance to prove hypothesis 14b. (Significant F change for iris scanning & facial recognition: p = 0.48 and for fingerprint recognition p = 0.29). Hypothesis 14c can also not be confirmed. (Significant F change for iris scanning & facial recognition: p = 0.681 and for fingerprint recognition p = 0.124) (See appendix 8 for more detailed output) (See appendix 8.4).

H15: The more experienced a consumer is with identification technology in general, the more intention he or she has to accept the technology in a retail setting. This hypothesis can be confirmed for both biometric systems (iris scanning & facial recognition: R²=0.07, β = 0.27, p = 0.001 and fingerprint recognition: R² = 0.03, β = 0.19, p = 0.014).

34

R²

β

t-value

p-value

Hypothesis

Behavioral Intention Perceived Usefulness

H2

Iris scanning & facial recognition

0.45

0.67

10.914

< 0.001

Yes

Fingerprint Recognition

0.49

0.70

12.854

< 0.001

Yes

Perceived Ease of Use

H3a

Iris scanning & facial recognition

0.05

0.24

2.887

0.004

Yes

Fingerprint Recognition

0.23

0.48

7.172

< 0.001

Yes

Relative Advantage

H4

Iris scanning & facial recognition

0.4

0.63

9.738

< 0.001

Yes

Fingerprint Recognition

0.58

0.77

15.422

< 0.001

Yes

Privacy Concerns

H5

Iris scanning & facial recognition

0.20

-0.45

-5.993

< 0.001

Yes

Fingerprint Recognition

0.28

-0.54

-8.013

< 0.001

Yes

Technology Anxiety

H7

Iris scanning & facial recognition

0.30

-0.55

-7.835

< 0.001

Yes

Fingerprint Recognition

0.57

-0.76

-14.548

< 0.001

Yes

Resultant Conservation

H8

Iris scanning & facial recognition

0.002

0.01

1.155

0.250

No

Fingerprint Recognition

0.006

-0.075

-0.915

0.361

No

Resultant Self-Enhancement

H9

Iris scanning & facial recognition

0.03

0.18

2.140

0.034

Yes

Fingerprint Recognition

0.02

0.14

1.738

0.08

No

Idealism

H10

Iris scanning & facial recognition

- 0.006

0.04

0.481

0.631

No

Fingerprint Recognition

0.0

0.08

1.015

0.312

No

Relativism

H11

Iris scanning & facial recognition

-0.001

0.08

0.918

0.360

No

Fingerprint Recognition

-0.004

-0.05

-0.590

0.556

No

Innovativeness

H12b

Iris scanning & facial recognition

0.11

0.34

4.339

0.001

Yes

Fingerprint Recognition

0.02

0.16

2.083

0.039

Yes

Facilitating Conditions

H13a

Iris scanning & facial recognition

0.23

0.49

6.589

< 0.001

Yes

Fingerprint Recognition

0.24

0.49

7.052

< 0.001

Yes

Experience

H15

Iris scanning & facial recognition

0.07

0.27

3.288

0.001

Yes

Fingerprint Recognition

0.03

0.19

2.482

0.014

Yes

Table 4 Results for the independent variables on Behavioral Intention and hypothesis testing

35

The results show a few assumptions (which are tested in the next chapter). The more perceived usefulness, the more perceived ease of use and the more relative advantages create more behavioral intention to accept the biometric system, but the effect is greater for fingerprint recognition. On the contrary, the positive effect is greater for iris scanning and facial recognition regarding higher innovativeness and more experience. Also, privacy concerns and technology anxiety have negative effects on the behavioral intention to accept, but these impacts seem greater for fingerprint recognition than for iris scanning and facial recognition. For now, these remarks are just assumptions and the significant differences between these two application types are tested in the next subchapter. The differences between the identification technologies

In the next subchapter, the two different identification technology applications are compared on the basis of the formed constructs (of the scenario questions) by independent samples t-tests. It gives a good insight on how people think differently about the two technologically systems and it could give a pragmatic insight for retailers which biometric system to choose. How can they facilitate the transformation from traditional marketing practices to iris scanning and facial recognition applications (based on the different constructs)? Answers to these questions are provided in this chapter as well as in the conclusive chapter. Furthermore, the study examines whether there are main effects of the demographic variables. Interaction effects of these demographic variables between the two types of biometric applications are discussed. The output can help retailers to form an idea on which type of consumers to focus more when implementing an identification technology in their store. Significant differences between the biometric types: t-tests

By comparing the means of each construct of both identification technologies, it is possible to see which constructs or elements make people think differently about different types of biometrics. The output is summarized in table 5 (p.39) and appendix 9. In order to fully comprehend the means, the number indicates what answer has been given: 1 = totally disagree, 2 = disagree, 3 = nor disagree, or agree, 4 = agree and 5 = totally agree.

36

Based on the results (see table 5, p.39), the two biometric applications are significantly different for almost every construct, except for experience. As most people that do not have any experience with the technology (means are 1.92 and 1.93), experience seems to be irrelevant. As the table shows, there is a significant difference in how people intend to accept both biometrics (t (336) = -4.139, p < 0.001). Respondents obviously would rather accept the fingerprint recognition system at the check-out than they would accept the marketing observation applications of iris scanning and facial recognition. Although the average respondent does not have a clear opinion on whether or not they would accept fingerprint recognition (mean = 3.30), iris scanning and facial recognition are rated below 3, meaning that it probably would not be accepted. An assumption could be that people are more familiar with fingerprint recognition at the check-out, hence it is recommended for retailers to implement this technology first. The same conclusion can be made for behavioral recommendation (t (321) =-3.802, p< 0.001). Although fingerprint recognition’s mean does not indicate great acceptance, it differs with 0.467 from the iris scanning and facial recognition mean. Therefore people are more inclined to recommend the fingerprint recognition application than the iris scanning and facial recognition application. While people do not value the time-saving and value-offering characteristics of the iris scanning and facial recognition system (mean: 2.78), they do recognize these elements in the fingerprint system (mean: 3.65). The perceived usefulness is therefore also significantly different between both biometrics (t (314) = -8.598, p< 0.001). The results of perceived ease of use also show significant differences (t (313) = -5.520, p< 0.001), but indicates positive means for both systems (3.59 for iris scanning and facial recognition and 4.08 for fingerprint recognition). People obviously perceive both biometric applications as easy to use; it does not mean that they are willing to accept them. Retailers must be aware that focusing on this element, when implementing the identification technology, will not have a great impact. The fingerprint recognition system creates more relative advantages than the iris scanning and facial recognition system (3.58 vs 2.66). They also differ significantly (t (314) = -9.922, p< 0.001). Consumers realize that the fingerprint system can offer advantages compared to cash and credit cards (the retailer may want to do some more research to investigate the exact advantages), but consumers do not appreciate these advantages with iris scanning and facial recognition compared to traditional marketing methods. The means of both biometric applications are relatively high with regard to privacy concerns (3.81 for IS&FR and 3.52 for FP). Furthermore, the difference between the two identification 37

technologies with regard to privacy concerns, is significant (t (302) =2.858, p = 0.005). Retailers must consider different actions to reduce the privacy concerns when launching these systems in their store. But, the results show lower means for both biometrics for technology anxiety (IS&FR: 3.05 & FP: 2.65). This construct differs also significant between the two types (t (301) = 4.520, p< 0.001). Although hypothesis 6 (see p.31) confirmed that the more technology anxious people are, the more privacy concerns they have. They seem to be less technology anxious about these technologies than that they have problems with sharing their private information. Especially with fingerprint recognition: the average buyer would not go to another store or would be scared or have doubts to use the technology. Therefore – a reminder for retailers – respondents seem not to be afraid of using or undergoing (in the case of iris scanning and facial recognition) the technology, they are afraid of what is happening with their personal information when they have been using a biometric system. Next, there is a significant difference between the two applications regarding facilitating conditions (t (301) = -3.815, p<0.001). Again, respondents would rather accept the fingerprint recognition system when there would be assistance to answer questions than that they would accept the iris scanning and facial recognition system. The last construct, experience, shows no significant difference between the two types of identification technologies (t (301) = -0.081, p= 0.936). Perhaps this is a consequence of the low average experiences for both biometric applications.

All these conclusions and valid hypotheses conclude that retailers must be careful when implementing iris scanning and facial recognition applications. According to the regression analysis, they affect the behavioral intention to accept the identification technologies, but the ttest suggests that the amount of impact on the acceptance differs from one biometric applicationto the other.

38

Construct

Iris scanning and facial recognition Mean

Standard

P-value

Fingerprint recognition Mean

Deviation

Standard

p-value

Deviation

BI

2.79

0.98

< 0.001

3.30

1.23

< 0.001

BR

2.60

1.06

< 0.001

3.07

1.14

< 0.001

PU

2.78

0.89

< 0.001

3.65

0.91

< 0.001

PEOU

3.59

0.81

< 0.001

4.08

0.76

< 0.001

RA

2.66

0.85

< 0.001

3.58

0.82

< 0.001

PC

3.81

0.87

0.005

3.52

0.88

0.005

TA

3.05

0.79

< 0.001

2.65

0.75

< 0.001

FC

3.08

0.85

< 0.001

3.43

0.74

< 0.001

Exp

1.92

0.87

0.936

1.93

0.88

0.936

Table 5 Differences between constructs and biometric applications: mean, standard deviation, p-value

Main and interaction effects of demographic variables on behavioral intention to accept identification technologies – General Linear Model

This subchapter is about examining the effects of the demographic variables: main effects on the construct behavioral intention to accept and interaction effects of gender on the acceptance and the type of biometric applications. The detailed output can be found in appendix 10. For each effect, a General Linear Model is used. The results for an effect of gender show that there is no significant main effect of gender on the BI (F (1) = 0.518, p = 0.472), nor an interaction effect on the BI of IS&FR and FP (F (1) = 0.15, p= 0.700). The results for an effect of age show that there is a significant main effect of age on the BI (F (6) = 3.944, p= 0.001), but there is no interaction effect on the BI of IS&FR and FP (F (6) = 1.167, p= 0.324). However, the correlation between age and BI is rather low but significant (r = 0.13, p =0.028). The estimates (appendix 10.1) of age on the BI indicate a high acceptance with young people (<18) (mean: 3.563), but indicate that the trend goes downwards when people age until a certain moment (the age of 40). After people passed this age, they start to intend to accept the biometric systems again. This perceived trend, however, is limitative. First, the survey contains few respondents in the age category of – 18 (4.7 % of all respondents). Second, we believe the + 60 age category evaluated the use of identification technologies rather theoretically, instead of practically (mean= 3.354). Third, the correlation between age and the BI is rather low. 39

Regarding the highest obtained education of respondents, there is nor a significant main effect (F (5) = 0.950, p= 0.449), nor a significant interaction effect on the two types of biometric systems (F (5) = 0.739, p= 0.595). This conclusion can also be made, based on this study’s results, for the main effect of social status (F (5) = 1.493, p= 0.192) or the interaction effect (F (5) = 0.372, p= 0.868). There is also no significant main effect regarding the profession (F (7) = 1.307, p= 0.247), nor a significant interaction effect (F (7) = 1.913, p= 0.068). Although the latter is almost significant, there is no trend found. The detailed output is given in appendix 10.1.

The behavioral intention to accept depends, as tested and confirmed by the regression analysis, on several constructs (independent variables). In order to enhance the chance that buyers would accept the identification technology in retailers’ stores, the retailer must be well informed which independent variables to focus on. Another GLM test shows that there is a main effect of age on privacy concerns (appendix 10.2) (F (6) = 3.359, p= 0.003). As the output in appendix 10.1 indicates, the age category of 25 till 65 have the most concerns regarding their privacy, while the 18-24 and +65 age categories have the lowest levels of concerns. Retailers should use adapted marketing tools to convince the former age categories to accept the biometrics in their store. The same conclusion can be made for age on the perceived usefulness on the biometric systems (F (6) = 4.164, p= 0.001). As summarized in table 6, these assumptions can be made for almost every independent variable that has a significant regression effect on BI (see table 4, p.35). Age is therefore a strong determinant. For each variable, the correlation with age is measured to strengthen or weaken this assumption (see appendix 10.3). The factors ‘Technology Anxiety’ and ‘Experience’ were left out because of a different trend than the other variables (see appendix 10.2). Construct

Df

F

Sig

r

PU

6

4.164

0.001

0.048 (p= 0.415)

PEOU

6

2.137

0.049

0.068 (p= 0.248)

RA

6

6.339

< 0.001

0.172 (p= 0.003)

PC

6

3.359

0.003

-0.068 (p=0.247)

FC

6

2.429

0.026

-0.027 (p=0.645)

Table 6 Main effect of age on independent variables (PU, PEOU, RA, PC, FC)

40

The next chapter offers limitations of this study and suggestions for further research.

Chapter V: Limitations and Suggestions for Future Research Although this study examines different influencing variables, that it goes beyond the student age category and that it includes different biometric systems, there are some limitations as well as possible directions for future research. First of all, there was no significance regarding the idealism and relativism constructs towards the behavioral intention to accept. Some respondents, however, remarked that they were influenced by the scenario questions when they filled in the general questions. Perhaps, results and significance would be possible if the general questions were put first in the survey. Hereby, respondents would perhaps not be influenced by the identification technologies when pointing out their view on ethics. The personal values of Schwartz (1992) were also translated in Dutch and could give another perception than when they would have been maintained in English. Another possible limitation of the survey itself is its length. A lot of respondents did not finish the survey and therefore, there were relatively many missing values. Next, not every construct contained a lot of items. For example: dimension selftranscendence carries only two items (benevolence and universalism) and shows a low cronbach’s alpha (0.55) as a result. Also, privacy concerns only consisted of five items, but already had a significant relation with the behavioral intention to accept (Iris scanning & facial recognition: R² = 0.20, β = -0.45, p < 0.001 and fingerprint recognition: R² = 0.28, β = - 0.54, p < 0.001). Further research could analyze which elements create the most privacy concerns with consumers. Consumers should come up with their own reasons, based on focus groups in order for the retailer to really comprehend their thoughts about the topic. This is not only valid for privacy concerns. The whole research should be done more experimentally and empirically to gain deeper insights. People’s answers are more relevant and useful when they actually have undergone a situation in the past, and less relevant when they have to predict how they would react. Furthermore, due to a high correlation among the independent variables, it was not possible to analyze these variables in one model. Therefore, the independent variables were examined one by one to the dependent variable, which could give a biased interpretation. A last important limitation involves the comparison of the two biometric systems regarding the demographic variables. There were too few respondents to spot obvious trends (for example only two 41

respondents just had a primary school degree, only eight were older than 65 …). With that amount of respondents, generalizations could be overestimated. Regarding suggestions for future research: there was no hypothesis describing a direct relationship between social influence and behavioral intention, mainly because it did not show any significance in previous studies. But in this world, where direct advertising becomes more challenging because consumers listen more to their peers (word of mouth, fora on internet,…), it is difficult to believe that social influence does not affect the behavioral intention to accept an identification technology. Further research could examine this possible relation into more detail, perhaps even via behavioral intention to recommend. Also, the survey was done in Belgium. It would be interesting to see if other European countries or continents would score differently. The results could even be different for other ethnic groups. Furthermore, other variables could be included in future research as well. What if it was mandatory to pay by touch for several stores in the neighborhood or what would be the impact of a general use of these technologies in different stores in the respondent’s environment? Also, could store commitment have an impact on a person’s decision to enter the iris scanning and facial recognition store or not? These are all suggestions for future research, but the main recommendation would be to convert the survey into an experiential research for the respondent.

Chapter VI: Conclusion & Pragmatically implications The topic of this dissertation (Consumer Acceptance of Identification Technology) is gaining popularity each year. However, although people seem to accept these biometric applications in a mandatory setting (casinos, border controls, access to particular sites), few studies have investigated the acceptance of consumers in a voluntary setting or in an environment where people or consumers are positioned daily. This study examines consumers’ acceptance of biometrics in a retail setting with three types of identification technologies: fingerprint recognition, iris scanning and facial 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). “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” (findBIOMETRICS, 2014,).

42

The composed theoretical framework of this topic (see page 19) does not only provide an academic model for research, but provides pragmatic insight on how retailers can implement fingerprint recognition (as a payment method) and iris scanning and facial recognition scanners (as a marketing tool) in their stores, keeping influencing variables on consumers’ acceptance in mind. Several hypotheses were tested among 187 fingerprint and 151 iris scanning and facial recognition survey respondents and significant results were found. The behavioral intention to accept these identification technologies by consumers depends significantly on the perceived usefulness of the technology (β: 0.67 for IS&FR, 0.70 for FP), the perceived ease of use (β: 0.24 for IS&FR, 0.48 for FP), the relative advantage of the technology compared to other payment methods or marketing tools (β: 0.63 for IS&FR, 0.77 for FP), the privacy concerns of consumers regarding the technologies (β: -0.45 for IS&FR, - 0.54 for FP), the anxiety of consumers towards these technologies (β: - 0.55 for IS&FR, - 0.76 for FP) the innovativeness of consumers in general (β: 0.34 for IS&FR, 0.16 for FP), the facilitating conditions surrounding these technologies (β: 0.49 for IS&FR and for FP) and the experience consumers have with the technology (β: 0.27 for IS&FR, 0.19 for FP). Retailers should definitely therefore reduce the privacy concerns of consumers and although there is a greater effect of the technology anxiety, research indicates that people are less anxious about using the technology but more about what happens with their personal data. Retailers should provide enough information on what happens with the data that is collected by the pay by touch mechanism and observed by the iris and facial scanner. Retailers should also – especially in the early stages of implementation – hire experts or extra employees assisting people and answering questions about the technology to facilitate people’s experience. Talking about experience: although we did not find many respondents who had any experience in this area, the intention of consumers to accept the technology would be enhanced when they had more experience. Therefore, as long as technology is not implemented by many retailers in the neighborhood of consumers, it will be very difficult to let them accept the identification technology by this variable. Because there are two different biometric applications measured in this survey, it is interesting to see the differences between the two. By using a ‘two independent samples t-test’, the results show that consumers are significantly more comfortable with the fingerprint recognition system, among all constructs (of the scenario questions: PU, PEOU, RA, PC, TA, FC) except for experience. That is why it would be advisable for retailers that, if they would like to enhance customer experience by an identification technology, to start with the fingerprint scanner. Hereby, people would more easily adapt to future iris scanning and facial recognition technology 43

in their department store. A little notation on this conclusion is that this dissertation links the fingerprint with the payment method, and the iris scanning and facial recognition with the marketing tool. Perhaps consumers have opposite reactions towards these technologies just because of their usage situation (payment or advertising) and not because of the technology. Implementation of face recognition at the check-out could be a suggestion for further research. Using a General Linear Model, we examined the effect of demographic variables on behavioral intention to accept (main –and interaction effect between the two types of biometrics) as well as on relevant constructs. Although there were no interaction effects to be found, age had a strong main effect on BI, PU, PEOU, RA, PC and FC. The trends of the effects are all the same, the extreme age categories have more intention to accept, see more usefulness, ease of use, relative advantages, facilitating conditions and have less privacy concerns than the age category 25 – 40. However, the trend is limitative due to several elements. First, the survey contains few respondents in the age category of – 18 (4.7 % of all respondents). Second, we believe the + 60 age category evaluated the use of identification technologies rather theoretically, instead of practically. Third, the correlation between age and the variables is rather low or non-significant.

Although the study handles many hypotheses and takes many variables into consideration, there are limitations and possible suggestions for future research. For example, the study takes place in Belgium, but perhaps there are different coefficients for different cultures or countries. Another example could be that it is difficult for respondents to imagine a situation they have never experienced before. The whole study could be redone, but under pragmatic circumstances. Also, focus groups, laboratories and experiments will give more insights for the retailer than a survey.

In summary, the study gives a good insight of the behavioral intention of consumers to accept identification technologies. It is based on different age categories, a daily environment and different biometrics. It facilitates retailers to understand the reasons behind consumers’ (probable) acceptance of these biometric systems. But unless the implementation process of such technologies in retail settings is done with caution and consideration of the surrounding variables, it will be seen as invasive (more for iris scanning and facial recognition applications than for fingerprint recognition), which would be very unfortunate for a technology that could offer so much in the near future.

44

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III

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V

Appendices Appendix 1: Survey – Dutch version Appendix 1.1 Fingerprint recognition – Vingerafdruk herkenning

Aanvaarding van Identificatie Technologie door consumenten Inleiding Vandaag de dag draait alles om technologie. We kijken naar technologie, we luisteren naar technologie, we voelen de technologie en bovenal, we zijn soms zelf de technologie. Daarover handelt deze enquête: hoe gaan mensen om met het feit dat wij soms zelf de technologie zijn? Bijvoorbeeld: de irissen van onze ogen worden gescand alvorens we een vliegtuig mogen opstappen, we openen onze gsm’s door middel van onze vingerafdruk en in casino’s worden onze gezichten continu gescand (voor het geval we criminelen zouden zijn). Deze soort technologie noemt: identificatie technologie of gewoonweg Biometrics. Biometrics wordt gebruikt om personen te identificeren of te herkennen: we zijn als het ware ons eigen wachtwoord. Er zijn voornamelijk drie typen biometrics: fingerprint, iris scanning en facial recognition. In

het

Nederlands

wordt

dit

Vingerafdruk,

Iris

Scanning

en

Gezicht

herkenning.

Bij fingerprint worden patronen in onze vingers gescand als we een vingerafdruk achterlaten. Bij iris scanning worden de irissen van onze ogen gescand en bij facial recognition worden we herkend op basis van onze gezichtsstructuur. Deze technologieën zijn al bekend in verschillende sectoren en situaties, maar stel je nu eens voor dat ze gebruikt worden in onze dagdagelijkse omgeving: warenhuizen. Wij proberen uit te zoeken, aan de hand van deze enquête, hoe mensen zouden staan ten opzichte van het gebruik van deze technologieën en welke

soort

mensen

het

snelst

deze

technologie

zullen

aanvaarden.

Eén ding is zeker: de toekomst is minder ver weg dan we denken. Deze enquête zal fingerprint recognition behandelen. De vragenlijst maakt deel uit van mijn masterproef uit Master Toegepaste Economische Wetenschappen – Marketing. Gelieve telkens één antwoord te geven, indien anders gevraagd, en deze enquête zo eerlijk mogelijk in te vullen. Deze enquête zal volledig anoniem behandeld worden. Ik wil u alvast hartelijk bedanken voor uw deelname. Hylke Huys, master Studente TEW - Marketing

Appendix 1.1 | - 1 -

Deel 1: Scenariovragen De situatie is als volgt: u beslist om boodschappen te gaan doen naar uw favoriete warenhuis. Na weer veel te veel in uw winkelwagen te hebben geladen, komt u uiteindelijk aan de kassa. Alles lijkt hetzelfde, maar er is één grote wijziging. U kan nog steeds met uw bankkaart of cash betalen, maar aan de kassa bevindt zich nog een derde apparaat (behalve de kassa en de bankcontact): een vingerafdruk lezer. Met dit apparaat betaalt u door uw vinger op het toestel te leggen, waardoor het apparaat u herkent en automatisch het bedrag, dat u moet betalen, aftrekt van uw bankrekening.

U hebt dus geen bankkaart, pincode of geld nodig om uw betaling te doen. U hebt juist datgene nodig wat u altijd bij heeft: uw vinger. Als dit uw eerste bezoek aan de winkel was, zal u eerst nog een korte registratie moeten ondergaan, maar nadien is uw vinger telkens genoeg om te betalen. Wat is uw beslissing wanneer u aan de kassa staat en hoe staat u tegenover deze wijze van betalen? Gelieve de volgende vragen te beantwoorden.

Figure 8 Picture pay by touch: Mindfully.org, 2005, Piggly Wiggly Fingerprint Scanners, .

Appendix 1.1 | - 2 -

1. Bepaal de kans dat u het vingerafdruk toestel gaat gebruiken in plaats van uw bankkaart of cash. Omcirkel het voor uw gepaste antwoord. De schalen zijn als volgt verdeeld: 1= zeer onwaarschijnlijk (vb.), 2= redelijk onwaarschijnlijk (vb.), 3 =geen mening, 4 =redelijk waarschijnlijk (vb.), 5= zeer waarschijnlijk (vb.).) = QUESTION 1 Onwaarschijnlijk 1

Waarschijnlijk 2

3

4

5

Onmogelijk

Mogelijk 1

2

3

4

Zou het zeker niet gebruiken 1

5 Zou het zeker en vast gebruiken

2

3

4

5

2. Beantwoordt de volgende vragen aan de hand van de schalen. = QUESTION 2

Helemaal niet akkoord

Niet akkoord

Akkoord

Helemaal akkoord

O

Noch akkoord, noch niet akkoord O

Aangezien ik het vingerafdruk toestel kan gebruiken, ben ik van plan h et te gebruiken.

O

O

O

Ik neem mij voor het toestel nogmaals te gebruiken in de komende maanden.

O

O

O

O

O

Ik ga het toestel zeker gebruiken in de komende maanden.

O

O

O

O

O

Volgende keer, als ik mijn vrienden/familie zie, zal ik zeker positief vertellen over dit nieuwe systeem.

O

O

O

O

O

Ik zou dit vingerafdruk systeem aanraden aan anderen.

O

O

O

O

O

Ik zou dit vingerafdruk systeem aanraden aan mensen, die mijn advies vragen.

O

O

O

O

O

Ik ben zeker van mijn vorige antwoorden.

O

O

O

O

O

Appendix 1.1 | - 3 -

3. Bepaal in welke mate de volgende uitspraken voor u van toepassing zijn. = QUESTION 3

Helemaal niet akkoord

Niet akkoord

Akkoord

Helemaal akkoord

O

Noch akkoord, noch niet akkoord O

Het Vingerafdruksysteem zorgt voor een waardevolle dienstverlening.

O

O

O

Met het Vingerafdruksysteem wordt het makkelijker om iemand te identificeren.

O

O

O

O

O

Door het Vingerafdruksysteem te kiezen als betaalmiddel, zal ik mij tijd besparen.

O

O

O

O

O

Kunnen betalen met behulp van een Vingerafdruksysteem maakt winkelen gemakkelijker.

O

O

O

O

O

Met het Vingerafdruksysteem kan ik op een veiligere manier betalen.

O

O

O

O

O

Met het Vingerafdruksysteem worden mijn persoonlijke gegevens op een gemakkelijke manier bijgehouden.

O

O

O

O

O

Het gebruik van een Vingerafdruksysteem is gemakkelijk.

O

O

O

O

O

Het gebruik van een Vingerafdruksysteem vergt weinig kennis of mentale inspanning.

O

O

O

O

O

Het Vingerafdruksysteem is simpeler dan andere betaalmiddelen.

O

O

O

O

O

Het Vingerafdruksysteem levert een snellere dienstverlening.

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

Het Vingerafdruksysteem levert een betrouwbaardere dienstverlening. Het Vingerafdruksysteem levert een meer geschikte dienstverlening.

Appendix 1.1 | - 4 -

4. Bepaal in welke mate je akkoord gaat met de volgende uitspraken over de systemen. = QUESTION 4

Helemaal niet akkoord

Niet akkoord

Akkoord

Helemaal akkoord

O

Noch akkoord, noch niet akkoord O

Met het Vingerafdruk systeem krijgen bedrijven persoonlijke informatie over mij, die ik als privé beschouw.

O

O

O

Met het Vingerafdruk systeem wordt mijn persoonlijke informatie gebruikt zonder mijn medeweten.

O

O

O

O

O

Met het Vingerafdruk systeem worden mijn persoonlijke gegevens met verschillende mensen en zonder mijn toestemming gedeeld.

O

O

O

O

O

Ik ben bezorgd over de gevolgen door mijn persoonlijke informatie te delen.

O

O

O

O

O

Ik ben er zeker van dat mijn persoonlijke informatie goed wordt beschermd.

O

O

O

O

O

Ik ben bang om het Vingerafdruk systeem te gebruiken.

O

O

O

O

O

Vanwege mijn angst voor technologie, zou ik eerder naar andere winkels gaan, waar ze het Vingerafdruk systeem niet gebruiken.

O

O

O

O

O

Ik twijfel om het Vingerafdruk systeem te gebruiken uit schrik voor schade aan mijn eigen lichaam.

O

O

O

O

O

Ik zou het Vingerafdruk systeem vertrouwen.

O

O

O

O

O

Ik denk dat de dienstverlening met het Vingerafdruk systeem betrouwbaar is.

O

O

O

O

O

Het Vingerafdruk systeem is gemakkelijk omdat ik niets anders nodig heb dan mijn eigen lichaam.

O

O

O

O

O

Ik zou het Vingerafdruk systeem eerder gebruiken indien er personen beschikbaar zijn voor eventuele vragen/problemen te beantwoorden.

O

O

O

O

O

Ik zou de het Vingerafdruk systeem eerder gebruiken omdat de identificatie van mensen zo gemakkelijker is.

O

O

O

O

O

Appendix 1.1 | - 5 -

Helemaal niet akkoord

Niet akkoord

Akkoord

Helemaal akkoord

O

Noch akkoord, noch niet akkoord O

Ik heb ervaring met de systemen in andere situaties/omgevingen. (Voorbeelden: in de luchthaven, Iphone,…)

O

O

O

Ik gebruik de systemen frequent in andere situaties/omgevingen.

O

O

O

O

O

Ik heb ervaring met de systemen in een warenhuis-omgeving.

O

O

O

O

O

Appendix 1.1 | - 6 -

Deel 2: Algemene Vragen In het volgende deel worden er vragen gesteld om een beter beeld te krijgen van u, als persoon.

1. Bepaal in welke mate je akkoord gaat met de volgende vragen. Deze vragen staan volledig los van Vingerafdruk systemen en zijn dus algemeen. = QUESTION 5

Helemaal niet akkoord

Niet akkoord

Akkoord

Helemaal akkoord

O

Noch akkoord, noch niet akkoord O

Ik ben ten opzichte van mijn vrienden/familie één van de eersten om een nieuwe technologie uit te proberen.

O

O

O

Wanneer ik hoor dat er een nieuwe technologie op de markt is, zoek ik meteen naar manieren om aan deze technologie te geraken.

O

O

O

O

O

Ik hou ervan om te experimenteren met nieuwe technologieën.

O

O

O

O

O

Als mijn vrienden mij iets aanraden, ben ik geneigd om dit te proberen.

O

O

O

O

O

Ik ben beïnvloedbaar door de mening van mijn familie of vrienden.

O

O

O

O

O

Ik hecht veel waarde aan de mening van mijn vrienden of familie.

O

O

O

O

O

Appendix 1.1 | - 7 -

2. De volgende vragen hebben met ethiek te maken. Bepaal in welke mate je akkoord gaat met de volgende vragen. Alvorens je de vragen beantwoordt, geven we een duidelijke betekenis aan de woorden ethiek en moreel. = QUESTION 6

Ethiek: Ethiek is het geheel van gedachten over en visies op de gedragsregels die mensen tegenover elkaar en tegenover de natuurlijke omgeving in acht moeten nemen, het is het nadenken over wat goed of slecht is. Ethisch gedrag betekent dus juist handelen. Moreel: heeft te maken met hoe iets hoort te zijn. Bijvoorbeeld: het is moreel je ouders te gehoorzamen. De tegenpool van moreel is immoreel. Helemaal niet akkoord

Niet akkoord

Akkoord

Helemaal akkoord

O

Noch akkoord, noch niet akkoord O

Vooraleer een actie te ondernemen moet men er zeker van zijn dat men door deze actie niemand zal kwetsen of beledigen.

O

O

O

Als een handeling anderen kan kwetsen of beledigen mag men deze actie niet ondernemen.

O

O

O

O

O

Iets doen dat een ander kan schaden is steeds fout, ongeacht de voordelen voor jezelf.

O

O

O

O

O

Men mag een ander nooit fysisch of emotioneel kwetsen.

O

O

O

O

O

Acties die de waardigheid of het welzijn van een ander schaden, mag men niet stellen

O

O

O

O

O

Als iemand die niets met uw actie te maken heeft schade kan oplopen, hoe klein ook, is deze actie onverantwoord.

O

O

O

O

O

Een beslissing nemen omtrent het uitvoeren of niet van een handeling die anderen schade kan toebrengen, enkel en alleen op basis van het afwegen van de voor- en nadelen voor zichzelf, is immoreel.

O

O

O

O

O

De zorg voor de waardigheid en het niet schaden van het welzijn van de medemensen zou de meest belangrijke zorg moeten zijn in elke maatschappij.

O

O

O

O

O

Het is in geen geval goed acties te ondernemen die anderen kunnen schaden.

O

O

O

O

O

Ethisch gedrag is dat gedrag dat idealerwijs de minst mogelijke risico’s op schade inhoudt voor anderen.

O

O

O

O

O

Appendix 1.1 | - 8 -

Helemaal niet akkoord

Niet akkoord

Akkoord

Helemaal akkoord

O

Noch akkoord, noch niet akkoord O

Wat ethisch aanvaardbaar is of niet is zo situatie gebonden dat je dit niet in een algemene gedragscode of wet kunt onderbrengen.

O

O

O

Wanneer iets ethisch is of niet, is volgens mij sterk afhankelijk van de situatie.

O

O

O

O

O

Wat de ene persoon als ethisch ziet, kan een ander persoon gerust onethisch vinden en omgekeerd.

O

O

O

O

O

Wanneer verschillende volkeren of groepen in de maatschappij andere ethische normen hanteren, mag men niet stellen dat de ene ethische norm juister is dan de andere.

O

O

O

O

O

Aangezien ethische opvattingen individueel zijn, kan men geen algemeen geldende ethische gedragsregels opstellen.

O

O

O

O

O

Ethische normen vertellen ons enkel hoe één persoon zich zou moeten gedragen en mogen daarom niet gebruikt worden om anderen te beoordelen.

O

O

O

O

O

Met betrekking tot interpersoonlijke relaties moet iedereen vrij zijn om zijn eigen morele regels te kunnen bepalen.

O

O

O

O

O

Alles wat met ethiek te maken heeft in regels en wetten vastleggen is zinloos daar ethiek een zeer persoonsgebonden iets is.

O

O

O

O

O

In het algemeen stellen dat liegen steeds onethisch is, is onmogelijk want of een leugen gerechtvaardigd is of niet hangt volledig af van de context of situatie.

O

O

O

O

O

Men zal nooit of te nimmer liegen.

O

O

O

O

O

Appendix 1.1 | - 9 -

3. In de volgende en laatste vraag van dit deel vragen we u om aan te duiden in hoeverre deze principes voor u belangrijk zijn in uw leven. De schaal is als volgt verdeeld: 0 = tegen mijn principes 1 = niet belangrijk 4 = belangrijk 8 = zeer belangrijk De niet vernoemde waarden liggen tussen de bovenstaande waarden. = QUESTION 7  MACHT (sociale macht, autoriteit hebben, rijkdom hebben)

0

1

2

3

4

5

6

7

8

 PRESTATIE (succes, capaciteiten hebben, ambitie, invloed hebben op mensen en gebeurtenissen) 0

1

2

3

4

5

6

7

8

 HEDONISME (een voldoening van je verlangens, vreugde in het leven, genotzucht) 0

1

2

3

4

5

6

7

8

 STIMULATIE (durf hebben, een gevarieerd en uitdagend leven, een opwindend leven) 0

1

2

3

4

5

6

7

8

 ZELFSTURING (creativiteit, vrijheid, nieuwsgierigheid, onafhankelijkheid, je eigen doelen in het leven kiezen) 0

1

2

3

4

5

6

7

8

 UNIVERSALISME (ruimdenkend, de schoonheid van de natuur en de kunsten, sociale rechtvaardigheid, wereldvrede, gelijkheid, wijsheid, eenheid met de natuur, milieubescherming) 0

1

2

3

4

5

6

7

8

 WELWILLENDHEID (hulpvaardig, eerlijkheid, vergevensgezind, trouw, verantwoordelijkheid) 0 1 2 3 4 5 6 7 8  TRADITIE (respect voor traditie, nederigheid, je plaats in het leven accepteren, toewijding, bescheidenheid) 0

1

2

3

4

5

6

7

8

Appendix 1.1 | - 10 -

 OVEREENSTEMMING (gehoorzaamheid, ouders en ouderen eren, zelfdiscipline, beleefdheid) 0

1

2

3

4

5

6

7

8

 VEILIGHEID (nationale veiligheid, familiale veiligheid, sociale orde, netheid, wederkeren van gunsten) 0

1

2

3

4

5

6

7

8

Appendix 1.1 | - 11 -

Deel 3: Demografische vragen Deze laatste vragen zijn standaardvragen om een beeld te hebben van de soort personen die onze enquête invullen. Geslacht

O Mannelijk O Vrouwelijk

Leeftijd

O < 18 jaar O 18 – 24 jaar O 25 – 34 jaar O 35 – 44 jaar O 45 – 54 jaar O 55 – 65 jaar O > 65 jaar

Hoogste opleidingsgraad

O Lagere School O Lager Secundair/Middelbaar Onderwijs O Hoger Secundair/Middelbaar Onderwijs O Professionele Bachelor (Hogeschool) O Academische Bachelor (Universiteit) O Academische Master

Sociale status

O Vrijgezel O In een relatie O Samenwonend O Gehuwd O Gescheiden O Weduwe/Weduwnaar O Student O Werkloos/werkzoekend O Arbeider O Bediende O Manager O Ambtenaar O Zelfstandig O Gepensioneerd

Beroep

Hartelijk dank voor het invullen van deze enquête. Hylke Huys, Master studente TEW – Marketing.

Appendix 1.1 | - 12 -

Appendix 1.2 Iris scanning en facial recognition ( iris scanning en gezichtsherkenning)

Aanvaarding van Identificatie Technologie door consumenten Inleiding Vandaag de dag draait alles om technologie. We kijken naar technologie, we luisteren naar technologie, we voelen de technologie en bovenal, we zijn soms zelf de technologie. Daarover handelt deze enquête: hoe gaan mensen om met het feit dat wij soms zelf de technologie zijn? Bijvoorbeeld: de irissen van onze ogen worden gescand alvorens we een vliegtuig mogen opstappen, we openen onze gsm’s door middel van onze vingerafdruk en in casino’s worden onze gezichten continu gescand (voor het geval we criminelen zouden zijn). Deze soort technologie noemt: identificatie technologie of gewoonweg Biometrics. Biometrics wordt gebruikt om personen te identificeren of te herkennen: we zijn als het ware ons eigen wachtwoord. Er zijn voornamelijk drie typen biometrics: fingerprint, iris scanning en facial recognition. In

het

Nederlands

wordt

dit

Vingerafdruk,

Iris

Scanning

en

Gezicht

herkenning.

Bij fingerprint worden patronen in onze vingers gescand als we een vingerafdruk achterlaten. Bij iris scanning worden de irissen van onze ogen gescand en bij facial recognition worden we herkend op basis van onze gezichtsstructuur. Deze technologieën zijn al bekend in verschillende sectoren en situaties, maar stel je nu eens voor dat ze gebruikt worden in onze dagdagelijkse omgeving: warenhuizen. Wij proberen uit te zoeken, aan de hand van deze enquête, hoe mensen zouden staan ten opzichte van het gebruik van deze technologieën en welke

soort

mensen

het

snelst

deze

technologie

zullen

aanvaarden.

Eén ding is zeker: de toekomst is minder ver weg dan we denken. Deze enquête zal iris scanning en facial recognition behandelen. De vragenlijst maakt deel uit van mijn masterproef uit Master Toegepaste Economische Wetenschappen – Marketing. Gelieve telkens één antwoord te geven, indien anders gevraagd, en deze enquête zo eerlijk mogelijk in te vullen. Deze enquête zal volledig anoniem behandeld worden. Ik wil u alvast hartelijk bedanken voor uw deelname. Hylke Huys, master Studente TEW - Marketing

Appendix 1.2 | - 13 -

Deel 1: Scenariovragen De situatie is als volgt: U komt weer toe aan één van uw favoriete warenhuizen. Bij het binnenkomen, bevindt zich een bord met de vermelding: ‘Wij gebruiken identificatie technologie om u meer tevreden en met een goed gevoel naar huis te kunnen laten gaan.’ Wat wil dit zeggen? In het warenhuis zelf zijn camera’s geplaatst die uw irissen en gezichtsuitdrukkingen kunnen scannen. Het scannen van uw irissen is om u als persoon te herkennen; het scannen van uw gezicht is om uw gevoelens over bepaalde producten te identificeren.

Bijvoorbeeld: wanneer u stilstaat bij een bepaalde Cava en uw gezichtsuitdrukking lijkt positief, zal de camera u identificeren (door uw gezicht te scannen) en krijgt u in de volgende gang een persoonlijke boodschap op een beeldscherm over de aperitiefhapjes die uitstekend passen bij diezelfde Cava. U krijgt uitleg over de aperitiefhapjes, waar ze zich bevinden in de winkel en over de prijs: een gepersonaliseerde reclameboodschap dus. U wordt dus tijdens uw bezoek in het warenhuis bekeken, maar krijgt nuttige informatie over de door u verkozen producten. Hoe staat u tegenover deze nieuwe technologie?

Figure 9 Facial Recognition camera ( Marieclaire.com, 2013, How Do You Shop? A Technology May Already Know. Hearst Communication, Inc., < http://www.marieclaire.com/blog/facialrecognition-shopping-technology>.)

Appendix 1.2 | - 14 -

1. In hoeverre accepteert u het gebruik van deze technologieën in uw favoriete warenhuis? De schalen zijn als volgt verdeeld: 1= zeer onwaarschijnlijk (vb.), 2= redelijk onwaarschijnlijk (vb.), 3 =geen mening, 4 =redelijk waarschijnlijk (vb.), 5= zeer waarschijnlijk (vb.).)= QUESTION 1 Onwaarschijnlijk 1

Waarschijnlijk 2

3

4

5

Onmogelijk 1

Mogelijk 2

3

4

5

Zou het zeker niet gebruiken 1

2.

2

Zou het zeker en vast gebruiken 3

4

5

Beantwoordt de volgende vragen aan de hand van de schalen.= QUESTION 2 Helemaal niet akkoord

Niet akkoord

Akkoord

Helemaal akkoord

O

Noch akkoord, noch niet akkoord O

Aangezien deze technologie mij helpt bij het uitkiezen van bepaalde producten, ga ik de winkel binnengaan.

O

O

O

Ik neem mij voor de winkel nogmaals te bezoeken in de komende maanden.

O

O

O

O

O

Ik ga de winkel zeker blijven gebruiken in de komende maanden.

O

O

O

O

O

Volgende keer, als ik mijn vrienden/familie zie, zal ik zeker positief vertellen over de technologieën dat deze winkel gebruikt.

O

O

O

O

O

Ik zou deze manier van winkelen aanraden aan anderen.

O

O

O

O

O

Ik zou deze manier van winkelen aanraden aan mensen die mijn advies vragen.

O

O

O

O

O

Ik ben zeker van mijn vorige antwoorden.

O

O

O

O

O

Appendix 1.2 | - 15 -

3. Bepaal in welke mate je akkoord gaat met de volgende uitspraken. = QUESTION 3 Helemaal niet akkoord

Niet akkoord

Akkoord

Helemaal akkoord

O

Noch akkoord, noch niet akkoord O

De Iris Scanning en de Gezicht Scanner zorgen voor een waardevolle dienstverlening.

O

O

O

Met de Iris Scanning en de Gezicht Scanner wordt het makkelijker om iemand te identificeren.

O

O

O

O

O

Door een winkel te kiezen dat Iris Scanning & Gezicht Scanning gebruikt, zal ik mij tijd besparen.

O

O

O

O

O

Iris Scanning & Gezicht Scanning maken winkelen gemakkelijker.

O

O

O

O

O

Met Iris Scanning & Gezicht Scanning wordt het voor mij als koper makkelijker want mijn persoonlijke gegevens worden op een gemakkelijke manier bijgehouden.

O

O

O

O

O

Het gebruik van Iris Scanning & Gezicht Scanning vergt een minimum aan inspanning.

O

O

O

O

O

Iris Scanning & Gezicht Scanning vergen weinig kennis of mentale inspanning.

O

O

O

O

O

Een winkel met Iris Scanning & Gezicht Scanning is simpeler dan winkels zonder.

O

O

O

O

O

Een winkel met Iris Scanning en een Gezicht Scanner levert een snellere dienstverlening dan een winkel zonder deze technologieën.

O

O

O

O

O

Een winkel met Iris Scanning en een Gezicht Scanner levert een betrouwbaardere dienstverlening dan een winkel zonder deze technologieën.

O

O

O

O

O

Een winkel met Iris Scanning en Gezicht Scanner levert een meer geschikte dienstverlening dan winkels zonder deze technologieën.

O

O

O

O

O

Appendix 1.2 | - 16 -

4. Bepaal in welke mate je akkoord gaat met de volgende uitspraken over de systemen Gezicht Scanning & Iris Scanning. = QUESTION 4

Helemaal niet akkoord

Niet akkoord

Akkoord

Helemaal akkoord

O

Noch akkoord, noch niet akkoord O

Met deze systemen krijgen bedrijven persoonlijke informatie over mij, die ik als privé beschouw.

O

O

O

Met deze systemen wordt mijn persoonlijke informatie gebruikt zonder mijn medeweten.

O

O

O

O

O

Met deze systemen worden mijn persoonlijke gegevens met verschillende mensen en zonder mijn toestemming gedeeld.

O

O

O

O

O

Ik ben bezorgd over de gevolgen door mijn persoonlijke informatie te delen.

O

O

O

O

O

Ik ben er zeker van dat mijn persoonlijke informatie goed wordt beschermd.

O

O

O

O

O

Ik ben bang om de systemen toe te staan.

O

O

O

O

O

Vanwege mijn angst voor technologie, zou ik eerder naar andere winkels gaan, waar ze deze systemen niet gebruiken.

O

O

O

O

O

Ik twijfel om de systemen toe te staan uit schrik voor schade aan mijn eigen lichaam.

O

O

O

O

O

Ik zou de systemen vertrouwen.

O

O

O

O

O

Ik denk dat de dienstverlening met deze systemen betrouwbaar is.

O

O

O

O

O

De systemen zijn gemakkelijk omdat ik niets anders nodig heb dan mijn eigen lichaam.

O

O

O

O

O

Ik zou de systemen eerder gebruiken/toestaan indien er personen beschikbaar zijn voor eventuele vragen/problemen te beantwoorden.

O

O

O

O

O

Ik zou de systemen eerder toestaan omdat de identificatie van mensen zo gemakkelijk is.

O

O

O

O

O

Ik heb ervaring met de systemen in andere situaties/omgevingen. (Luchthavens, Casino’s)

O

O

O

O

O

Ik gebruik de systemen frequent in andere situaties/omgevingen.

O

O

O

O

O

Ik heb ervaring met de systemen in een warenhuis-omgeving.

O

O

O

O

O

Appendix 1.2 | - 17 -

Deel 2: Algemene Vragen In het volgende deel worden er vragen gesteld om een beter beeld te krijgen van u, als persoon.

1. Bepaal in welke mate je akkoord gaat met de volgende vragen. = QUESTION 5

Helemaal niet akkoord

Niet akkoord

Akkoord

Helemaal akkoord

O

Noch akkoord, noch niet akkoord O

Ik ben ten opzichte van mijn vrienden/familie één van de eersten om een nieuwe technologie uit te proberen.

O

O

O

Wanneer ik hoor dat er een nieuwe technologie op de markt is, zoek ik meteen naar manieren om aan deze technologie te geraken.

O

O

O

O

O

Ik hou ervan om te experimenteren met nieuwe technologieën.

O

O

O

O

O

Als mijn vrienden mij iets aanraden, ben ik geneigd om dit te proberen.

O

O

O

O

O

Ik ben beïnvloedbaar door de mening van mijn familie of vrienden.

O

O

O

O

O

Ik hecht veel waarde aan de mening van mijn vrienden of familie.

O

O

O

O

O

Appendix 1.2 | - 18 -

2. De volgende vragen hebben met ethiek te maken. Bepaal in welke mate je akkoord gaat met de volgende vragen. Alvorens je de vragen beantwoordt geven we een duidelijke betekenis aan de woorden ethiek en moreel. = QUESTION 6 Ethiek: Ethiek is het geheel van gedachten over en visies op de gedragsregels die mensen tegenover elkaar en tegenover de natuurlijke omgeving in acht moeten nemen, het is het nadenken over wat goed of slecht is. Ethisch gedrag betekent dus juist handelen. Moreel: heeft te maken met hoe iets hoort te zijn. Bijvoorbeeld: het is moreel je ouders te gehoorzamen. De tegenpool van moreel is immoreel.

Helemaal niet akkoord

Niet akkoord

Akkoord

Helemaal akkoord

O

Noch akkoord, noch niet akkoord O

Vooraleer een actie te ondernemen moet men er zeker van zijn dat men door deze actie niemand zal kwetsen of beledigen.

O

O

O

Als een handeling anderen kan kwetsen of beledigen mag men deze actie niet ondernemen.

O

O

O

O

O

Iets doen dat een ander kan schaden is steeds fout, ongeacht de voordelen voor jezelf.

O

O

O

O

O

Men mag een ander nooit fysisch of emotioneel kwetsen.

O

O

O

O

O

Acties die de waardigheid of het welzijn van een ander schaden, mag men niet stellen Als iemand die niets met uw actie te maken heeft schade kan oplopen, hoe klein ook, is deze actie onverantwoord.

O

O

O

O

O

O

O

O

O

O

Een beslissing nemen omtrent het uitvoeren of niet van een handeling die anderen schade kan toebrengen, enkel en alleen op basis van het afwegen van de voor- en nadelen voor zichzelf, is immoreel.

O

O

O

O

O

De zorg voor de waardigheid en het niet schaden van het welzijn van de medemensen zou de meest belangrijke zorg moeten zijn in elke maatschappij.

O

O

O

O

O

Het is in geen geval goed acties te ondernemen die anderen kunnen schaden.

O

O

O

O

O

Ethisch gedrag is dat gedrag dat idealerwijs de minst mogelijke risico’s op schade inhoudt voor anderen.

O

O

O

O

O

Wat ethisch aanvaardbaar is of niet is zo situatie gebonden dat je dit niet in een algemene gedragscode of wet kunt onderbrengen.

O

O

O

O

O

Appendix 1.2 | - 19 -

Helemaal niet akkoord

Niet akkoord

Akkoord

Helemaal akkoord

O

Noch akkoord, noch niet akkoord O

Wanneer iets ethisch is of niet, is volgens mij sterk afhankelijk van de situatie.

O

O

O

Wat de ene persoon als ethisch ziet, kan een ander persoon gerust onethisch vinden en omgekeerd.

O

O

O

O

O

Wanneer verschillende volkeren of groepen in de maatschappij andere ethische normen hanteren, mag men niet stellen dat de ene ethische norm juister is dan de andere.

O

O

O

O

O

Aangezien ethische opvattingen individueel zijn, kan men geen algemeen geldende ethische gedragsregels opstellen.

O

O

O

O

O

Ethische normen vertellen ons enkel hoe één persoon zich zou moeten gedragen en mogen daarom niet gebruikt worden om anderen te beoordelen.

O

O

O

O

O

Met betrekking tot interpersoonlijke relaties moet iedereen vrij zijn om zijn eigen morele regels te kunnen bepalen.

O

O

O

O

O

Alles wat met ethiek te maken heeft in regels en wetten vastleggen is zinloos daar ethiek een zeer persoonsgebonden iets is.

O

O

O

O

O

In het algemeen stellen dat liegen steeds onethisch is, is onmogelijk want of een leugen gerechtvaardigd is of niet hangt volledig af van de context of situatie.

O

O

O

O

O

Men zal nooit of te nimmer liegen.

O

O

O

O

O

Appendix 1.2 | - 20 -

3. In de volgende en laatste vraag van dit deel vragen we u om aan te duiden in hoeverre deze principes voor u belangrijk zijn in uw leven. De schaal is als volgt verdeeld: 0 = tegen mijn principes 1 = niet belangrijk 4 = belangrijk 8 = zeer belangrijk De niet vernoemde waarden liggen tussen de bovenstaande waarden. = QUESTION 7  MACHT (sociale macht, autoriteit hebben, rijkdom hebben) 0

1

2

3

4

5

6

7

8

 PRESTATIE (succes, capaciteiten hebben, ambitie, invloed hebben op mensen en gebeurtenissen) 0

1

2

3

4

5

6

7

8

 HEDONISME (een voldoening van je verlangens, vreugde in het leven, genotzucht) 0

1

2

3

4

5

6

7

8

 STIMULATIE (durf hebben, een gevarieerd en uitdagend leven, een opwindend leven) 0

1

2

3

4

5

6

7

8

 ZELFSTURING (creativiteit, vrijheid, nieuwsgierigheid, onafhankelijkheid, je eigen doelen in het leven kiezen) 0

1

2

3

4

5

6

7

8

 UNIVERSALISME (ruimdenkend, de schoonheid van de natuur en de kunsten, sociale rechtvaardigheid, wereldvrede, gelijkheid, wijsheid, eenheid met de natuur, milieubescherming) 0

1

2

3

4

5

6

7

8

 WELWILLENDHEID (hulpvaardig, eerlijkheid, vergevensgezind, trouw, verantwoordelijkheid) 0 1 2 3 4 5 6 7 8  TRADITIE (respect voor traditie, nederigheid, je plaats in het leven accepteren, toewijding, bescheidenheid) 0

1

2

3

4

5

6

7

8

Appendix 1.2 | - 21 -

 OVEREENSTEMMING (gehoorzaamheid, ouders en ouderen eren, zelfdiscipline, beleefdheid) 0

1

2

3

4

5

6

7

8

 VEILIGHEID (nationale veiligheid, familiale veiligheid, sociale orde, netheid, wederkeren van gunsten) 0

1

2

3

4

5

6

7

8

Appendix 1.2 | - 22 -

Deel 3: Demografische vragen Deze laatste vragen zijn standaardvragen om een beeld te hebben van de soort personen die onze enquête invullen. Geslacht

O Mannelijk O Vrouwelijk

Leeftijd

O < 18 jaar O 18 – 24 jaar O 25 – 34 jaar O 35 – 44 jaar O 45 – 54 jaar O 55 – 65 jaar O > 65 jaar

Hoogste opleidingsgraad

O Lagere School O Lager Secundair Onderwijs O Hoger Secundair Onderwijs O Professionele Bachelor (Hogeschool) O Academische Bachelor (Universiteit) O Academische Master

Sociale status

O Vrijgezel O In een relatie O Samenwonend O Gehuwd O Gescheiden O Weduwe/Weduwnaar

Beroep

O Student O Werkloos/werkzoekend O Arbeider O Bediende O Manager O Ambtenaar O Zelfstandig O Gepensioneerd

Hartelijk dank voor het invullen van deze enquête. Hylke Huys, Master student TEW – Marketing.

Appendix 1.2 | - 23 -

Appendix 2: Survey – English version (translation) Appendix 2.1 Fingerprint recognition

Consumer Acceptance of Identification Technology Introduction Today, everything is breathing technology. We look at technology, we listen to technology, we feel technology and on top of that, sometimes we are the technology. That is the topic of this survey: how do people deal with the fact that we sometimes are the technology? For example: the irises of our eyes are scanned before we may enter an airplane, we open our mobile phones by using our fingerprint and at casinos, our faces are continuously scanned (in case we are criminals). This kind of technology is called identification technology or Biometrics. Biometrics are used to identify people or to recognize them: we are our own password. There are primary three types of biometrics: fingerprint, iris scanning and facial recognition. In Dutch these are called: Vingerafdruk, Iris Scanning and Gezicht herkenning. With fingerprint identification, the patterns in our fingers are scanned if we leave our fingerprint. Using Iris scanning, the irises of our eyes are scanned and using facial recognition, we are being recognized by our facial structure. These technologies are already known in different sectors and situations, but imagine for a minute that they are used in our daily environment: department stores. We try to find out, by this survey, how people would look at the use of these technologies and which kind of people would accept these technologies the most quickly. One thing is certain: the future is much closer then we perceive.

This survey will treat fingerprint Recognition. The survey is part of my Master of Science in Business Economics- Marketing master thesis. Please, always indicate only one answer, except when it is requested otherwise, and try to fill in this survey as honest as possible. This survey will be treated completely anonymously. I would already like to thank you for your participation.

Hylke Huys Master Student Business Economics - Marketing

Appendix 2.1 | - 24 -

Part 1: Scenario questions The scenario is as follows: you decide to do some grocery shopping in your favorite department store. After you have – again – loaded too many items in your shopping cart, you finally arrive at the check-out. Everything seems the same, but your store has changed one element. You can still pay by credit or debit card or cash, but now there is also an additional device at the check-out: a fingerprint scanner. With this device, you can pay by just putting your finger on the device. The latter recognizes you (and identifies you) and automatically deduct your payable amount from your bank account. Thus, no need for a credit/debit card, a PIN or for cash money to finish your payment. You just need that object that you always have with you: your finger. Only at your first visit to the store, you probably would have to go through a registration procedure, but once this is done, your finger is all you need to pay. What is your decision when you arrive at the check-out and how do you view this payment method? Please, fill in the next questions.

Figure 10 Picture pay by touch: Mindfully.org, 2005, Piggly Wiggly Fingerprint Scanners, .

Appendix 2.1 | - 25 -

1. Rate the probability that you will use the fingerprint scanner instead of your debit/credit card or cash. Circle the for you appropriate answer. The scales are divided as follows: 1 = highly unlikely (for example), 2 = fairly unlikely (for example), 3 = no opinion, 4 = reasonably likely (for example), 5 = highly likely (for example). = QUESTION 1

Unlikely

Likely 1

2

3

4

5

Impossible

Possible 1

2

3

4

5

Would definitely not use it 1

Would definitely use it 2

3

4

5

2. Answer the next questions according to the scales.= QUESTION 2

Totally Disagree

Disagree

Nor disagree, nor agree

Agree

Totally Agree

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

I would recommend this fingerprint system to others.

O

O

O

O

O

I would recommend this fingerprint system to people, who ask my advice.

O

O

O

O

O

I am certain of my previous answers.

O

O

O

O

O

Since I can use the fingerprint scanner, I am planning to use it.

I have decided to use the device again in the coming months. I will certainly use the device in the coming months. Next time, when I see my friends/family, I will definitely talk about this new system.

Appendix 2.1 | - 26 -

3. Determine in what degree the next statements are applicable for you. = QUESTION 3

Totally disagree

Disagree

Nor disagree, nor agree

Agree

Totally agree

The Fingerprint system ensures a valuable service.

O

O

O

O

O

With the Fingerprint system, it becomes easier to identify someone.

O

O

O

O

O

I will save time by choosing the Fingerprint system as payment method.

O

O

O

O

O

Being able to pay with the Fingerprint system, makes shopping easier.

O

O

O

O

O

I can pay more safely with the Fingerprint system.

O

O

O

O

O

My personal data is stored in an easy way with the Fingerprint system.

O

O

O

O

O

The use of a Fingerprint system is easy.

O

O

O

O

O

The use of a Fingerprint system requires little knowledge or mental effort.

O

O

O

O

O

The Fingerprint system is easier than other payment methods.

O

O

O

O

O

The Fingerprint system delivers a faster service.

O

O

O

O

O

The Fingerprint system delivers a more reliable service.

O

O

O

O

O

The Fingerprint system delivers a more appropriate service.

O

O

O

O

O

Appendix 2.1 | - 27 -

4. Determine in what degree you agree with the next statements about the system. = QUESTION 4

Totally disagree

Disagree

Nor disagree, nor agree

Agree

Totally agree

With the Fingerprint system, companies receive personal information about me, which I consider as private.

O

O

O

O

O

My personal information is used without my knowledge with the Fingerprint system.

O

O

O

O

O

With the Fingerprint system, my personal data is shared among many people and without my permission.

O

O

O

O

O

I am worried about the consequences by sharing my personal information.

O

O

O

O

O

I am certain that my personal information is well protected.

O

O

O

O

O

I am afraid to use the Fingerprint system.

O

O

O

O

O

Due to my fear for technology, I would rather go to other stores, where the Fingerprint system is not used.

O

O

O

O

O

I doubt to use the Fingerprint system out of fear to damage my own body.

O

O

O

O

O

I would trust the fingerprint system.

O

O

O

O

O

I think that the service with the Fingerprint system is more reliable.

O

O

O

O

O

The Fingerprint system is easy because I do not need anything else than my own body.

O

O

O

O

O

I would rather use the Fingerprint system if there were people available to answer any questions/issues.

O

O

O

O

O

I would rather use the Fingerprint system because of the easiness to identify people.

O

O

O

O

O

I have experience with the systems in other situations/environments. (For example: at the airport, Iphone, …).

O

O

O

O

O

I frequently use the systems in other situations/environments.

O

O

O

O

O

Appendix 2.1 | - 28 -

I have experience with the systems in a retail – environment.

O

O

O

O

O

Part 2: General questions The type of questions asked in the next pare are to get a better image of you, as respondent.

1. Determine how much you agree with the next questions. These questions are completely separated from the Fingerprint system and are therefore general questions. = QUESTION 5

Totally disagree

Disagree

Nor disagree, nor agree

Agree

Totally agree

I am, compared to my friends/family, one of the first people to try a new technology.

O

O

O

O

O

When I discover that there is a new technology on the market, I immediately search for ways to get that new technology.

O

O

O

O

O

I love to experiment with new technologies.

O

O

O

O

O

If my friends recommend something to me, I am inclined to try it.

O

O

O

O

O

I am influenced by the opinion of my family and friends.

O

O

O

O

O

I attach a lot of value to the opinion of my friends and family.

O

O

O

O

O

Appendix 2.1 | - 29 -

2. The next questions are about ethics. Determine how much you agree with the next questions. Before you answer the questions, we give a clear meaning to the words ethics and moral. = QUESTION 6

Ethics is the set of ideas and views on the conduct that people must respect against each other and against the natural environment; it is thinking about what is good or bad. Ethical behavior means thus act correctly. Moral: has to do with how something is supposed to be. For example, it is morally to obey your parents. The opposite of moral is immoral. Totally disagree

Disagree

Nor disagree, nor agree

Agree

Totally agree

A person should make certain that their actions never intentionally harm another even to a small degree.

O

O

O

O

O

Risks to another should never be tolerated, irrespective of how small the risks might be.

O

O

O

O

O

The existence of potential harm of others is always wrong, irrespective of the benefits gained.

O

O

O

O

O

One should never psychologically or physically harm another person.

O

O

O

O

O

One should not perform an action that might in any way threaten the dignity and welfare of another individual.

O

O

O

O

O

If an action could harm an innocent other, then it should not be done.

O

O

O

O

O

Deciding whether or not to perform an act by balancing the positive consequences of the act against the negative consequences of the act is immoral.

O

O

O

O

O

The dignity and welfare of people should be the most important concern in any society.

O

O

O

O

O

It’s never necessary to sacrifice the welfare of others.

O

O

O

O

O

Moral actions are those which closely match ideals of the most “perfect” action.

O

O

O

O

O

Appendix 2.1 | - 30 -

There are no ethical principles that are so important that they should be a part of any code of ethics.

O

O

O

O

O

Totally disagree

Disagree

Agree

Totally agree

What is ethical varies from one situation and society to another.

O

O

Nor disagree, nor agree O

O

O

Moral standards should be seen as being individualistic; what one person considers being moral may be judged to be immoral by another person.

O

O

O

O

O

Different types of moralities cannot be compared as to rightness.

O

O

O

O

O

What is ethical for everyone can never be resolved since what is moral or immoral is up to the individual.

O

O

O

O

O

Moral standards are simply personal rules which indicate how a person should behave, and are not to be applied in making judgments of others.

O

O

O

O

O

Ethical considerations in interpersonal relations are so complex that individuals should be allowed to formulate their own individual codes.

O

O

O

O

O

Rigidly codifying an ethical position that prevents certain types of actions stand in the way of better human relations and adjustment.

O

O

O

O

O

No rule concerning lying can be formulated; whether a lie is permissible or not permissible totally depends upon the situation.

O

O

O

O

O

Whether a lie is judged to be immoral depends upon the circumstances surrounding the actions.

O

O

O

O

O

Appendix 2.1 | - 31 -

3. In the next and last question of this part, we ask you to indicate how much these principles are important in your life. The scale is divides as follows: 0 = opposed to my principles 1 = not important 4 = important 8 = supreme important The values that are not mentioned are lying between the above values. = QUESTION 7  POWER (social power, authority, wealth)

0

1

2

3

4

5

6

7

8

 ACHIEVEMENT (Successful ,capable , ambitious, influential on people and situations) 0

1

2

3

4

5

6

7

8

 HEDONISM (self-indulgent, , enjoying life, pleasure) 0

1

2

3

4

5

6

7

8

 STIMULATION (daring, a varied life, an exciting life) 0

1

2

3

4

5

6

7

8

 SELF - DIRECTION (creativity, freedom, curious, independent, choosing own goals in life) 0

1

2

3

4

5

6

7

8

 UNIVERSALISM (broad-minded, a world of beauty, social justice, a world at peace , equality, wisdom, unity with nature protecting the environment) 0

1

2

3

4

5

6

7

8

 BENEVOLENCE (helpful, honest, forgiving, loyal, responsible) 0

1

2

3

4

5

6

7

8

 TRADITION (respect for tradition, humble, accepting portion in life, devout, moderate) 0

1

2

3

4

5

6

7

8

Appendix 2.1 | - 32 -

 CONFORMITY (obedient, honoring parents and elders, self-discipline, politeness) 0

1

2

3

4

5

6

7

8

 SECURITY (national security, family security, social order, clean, reciprocation of favors) 0

1

2

3

4

5

6

7

8

Appendix 2.1 | - 33 -

Part 3: Demographic Questions These last questions are standard questions to get an image of the respondents who fill in our survey.

Gender

O Male O Female

Age

O < 18 jaar O 18 – 24 jaar O 25 – 34 jaar O 35 – 44 jaar O 45 – 54 jaar O 55 – 65 jaar O > 65 jaar

Highest obtained education

O Primary School O Secondary School Short – type O Secondary School Long – type O Professional Bachelor O Academic Bachelor O Academic Master

Social status

O Single O In a relationship O Cohabiting O Married O Divorced O Widow/Widower

Beroep

O Student O Unemployed/ Job Seeker O Worker O Employee O Manager O Official O Self-employed O Retired

Thank you for your participation. Hylke Huys, Master student Business Economics – Marketing.

Appendix 2.1 | - 34 -

Appendix 2.2 Iris scanning and facial recognition

Consumer Acceptance of Identification Technology Introduction Today, everything is breathing technology. We look at technology, we listen to technology, we feel technology and on top of that, sometimes we are the technology. That is the topic of this survey: how do people deal with the fact that we sometimes are the technology? For example: the irises of our eyes are scanned before we may enter an airplane, we open our mobile phones by using our fingerprint and at casinos, our faces are continuously scanned (in case we are criminals). This kind of technology is called identification technology or Biometrics. Biometrics are used to identify people or to recognize them: we are our own password. There are primary three types of biometrics: fingerprint, iris scanning and facial recognition. In Dutch these are called: Vingerafdruk, Iris Scanning and Gezicht herkenning. With fingerprint identification, the patterns in our fingers are scanned if we leave our fingerprint. Using Iris scanning, the irises of our eyes are scanned and using facial recognition, we are being recognized by our facial structure. These technologies are already known in different sectors and situations, but imagine for a minute that they are used in our daily environment: department stores. We try to find out, by this survey, how people would look at the use of these technologies and which kind of people would accept these technologies the most quickly. One thing is certain: the future is much closer then we perceive.

This survey will treat iris scanning and facial recognition. The survey is part of my Master of Science in Business Economics- Marketing master thesis. Please, always indicate only one answer, except when it is requested otherwise, and try to fill in this survey as honest as possible. This survey will be treated completely anonymously. I would already like to thank you for your participation.

Hylke Huys Master Student Business Economics – Marketing

Appendix 2.2 | - 35 -

Part 1: Scenario questions This is the situation: you arrive at one of your favorite department stores. When entering the store, you see a sign mentioning the following text: ‘We use identification technology to keep you more satisfied and to let you go home with a good feeling.’ What does this mean? There have been placed several cameras in the department stores. Cameras, which can scan your irises and facial expressions. The scanning of the irises is to recognize you as a person, the scanning of your facial expression is to identify your feelings about certain products.

For example: when you would stop at a certain cava and your facial expression seems positive, the camera will identify you (by scanning your face) and in the next array you will receive a personal message on a display about certain appetizers that would perfectly match with that same cava. You will receive an explanation about those appetizers, where they are situated in the store, how much they cost: hence, a personalized advertising. Thus, you are being watched during your visit in the department store, but you receive useful information about products, chosen by you. How do you look upon this new technology?

Figure 11 Facial Recognition Camera (Marieclaire.com, 2013, How Do You Shop? A Technology May Already Know. Hearst Communication, Inc., < http://www.marieclaire.com/blog/facial-recognition-shopping-technology>. )

Appendix 2.2 | - 36 -

1. Rate the probability that you will use the fingerprint scanner instead of your debit/credit card or cash. Circle the for you appropriate answer. The scales are divided as follows: 1 = highly unlikely (for example), 2 = fairly unlikely (for example), 3 = no opinion, 4 = reasonably likely (for example), 5 = highly likely (for example). = QUESTION 1

Unlikely

Likely 1

2

3

4

5

Impossible

Possible 1

2

3

4

5

Would definitely not use it 1

Would definitely use it 2

3

4

5

2. Answer the following questions according to the scales. = QUESTION 2

Totally Disagree

Disagree

Nor disagree, nor agree

Agree

Totally Agree

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

O

I would recommend this way of shopping to others.

O

O

O

O

O

II would recommend this way of shopping to people, who ask my advice.

O

O

O

O

O

I am certain of my previous answers.

O

O

O

O

O

Since this technology helps me choosing certain products, I will enter the store.

I intend to visit the store again in the coming months. I will certainly keep visiting the store in the coming months. Next time, when I see my friends/family, I will definitely talk about the technologies that this store is using.

Appendix 2.2 | - 37 -

3. Determine in what degree the next statements are applicable for you. = QUESTION 3

Totally disagree

Disagree

Nor disagree, nor agree

Agree

Totally agree

The Iris Scanning and Facial Recognition applications ensure a valuable service.

O

O

O

O

O

With the Iris scanning and Facial recognition applications, it becomes easier to identify someone.

O

O

O

O

O

I will save time by choosing a store that uses Iris Scanning and Facial Recognition applications.

O

O

O

O

O

The Iris Scanning and Facial Recognition applications make shopping easier.

O

O

O

O

O

It becomes, for me as a buyer, easier with Iris Scanning and Facial Recognition applications because my personal data is stored in an easy way.

O

O

O

O

O

The use of Iris Scanning and Facial Recognition applications requires a minimum of effort.

O

O

O

O

O

The use of Iris Scanning and Facial Recognition applications requires little knowledge or mental effort.

O

O

O

O

O

A store with Iris Scanning and Facial Recognition applications is easier than a store without these technologies.

O

O

O

O

O

A store with Iris Scanning and Facial Recognition applications delivers a faster service than a store without these technologies.

O

O

O

O

O

A store with Iris Scanning and Facial Recognition applications delivers a more reliable service than a store without these technologies.

O

O

O

O

O

A store with Iris Scanning and Facial Recognition applications delivers a more appropriate service than a store without these technologies.

O

O

O

O

O

Appendix 2.2 | - 38 -

4. Determine in what degree you agree with the next statements about the system. = QUESTION 4

Totally disagree

Disagree

Nor disagree, nor agree

Agree

Totally agree

With these systems, companies receive personal information about me, which I consider as private.

O

O

O

O

O

My personal information is used without my knowledge with these systems.

O

O

O

O

O

With these systems, my personal data is shared among many people and without my permission.

O

O

O

O

O

I am worried about the consequences by sharing my personal information.

O

O

O

O

O

I am certain that my personal information is well protected.

O

O

O

O

O

I am afraid to allow these systems.

O

O

O

O

O

Due to my fear for technology, I would rather go to other stores, where these systems are not used.

O

O

O

O

O

I doubt to use these systems out of fear to damage my own body.

O

O

O

O

O

I would trust these systems.

O

O

O

O

O

I think that the service with these systems is more reliable.

O

O

O

O

O

The systems are easy because I do not need anything else than my own body.

O

O

O

O

O

I would rather use/allow these systems if there were people available to answer any questions/issues.

O

O

O

O

O

I would rather allow these systems because of the easiness to identify people.

O

O

O

O

O

I have experience with these systems in other situations/environments. (For example: at the airport, Casinos …).

O

O

O

O

O

I frequently use the systems in other situations/environments.

O

O

O

O

O

I have experience with the systems in a retail – environment.

O

O

O

O

O

Appendix 2.2 | - 39 -

Part 2: General questions The type of questions asked in the next pare are to get a better image of you, as respondent.

1. Determine how much you agree with the next questions. These questions are completely separated from the Fingerprint system and are therefore general questions. = QUESTION 5

Totally disagree

Disagree

Nor disagree, nor agree

Agree

Totally agree

I am, compared to my friends/family, one of the first people to try a new technology.

O

O

O

O

O

When I discover that there is a new technology on the market, I immediately search for ways to get that new technology.

O

O

O

O

O

I love to experiment with new technologies.

O

O

O

O

O

If my friends recommend something to me, I am inclined to try it.

O

O

O

O

O

I am influenced by the opinion of my family and friends.

O

O

O

O

O

I attach a lot of value to the opinion of my friends and family.

O

O

O

O

O

Appendix 2.2 | - 40 -

2. The next questions are about ethics. Determine how much you agree with the next questions. Before you answer the questions, we give a clear meaning to the words ethics and moral. = QUESTION 6

Ethics is the set of ideas and views on the conduct that people must respect against each other and against the natural environment; it is thinking about what is good or bad. Ethical behavior means thus act correctly. Moral: has to do with how something is supposed to be. For example, it is morally to obey your parents. The opposite of moral is immoral. Totally disagree

Disagree

Nor disagree, nor agree

Agree

Totally agree

A person should make certain that their actions never intentionally harm another even to a small degree.

O

O

O

O

O

Risks to another should never be tolerated, irrespective of how small the risks might be.

O

O

O

O

O

The existence of potential harm of others is always wrong, irrespective of the benefits gained.

O

O

O

O

O

One should never psychologically or physically harm another person.

O

O

O

O

O

One should not perform an action that might in any way threaten the dignity and welfare of another individual.

O

O

O

O

O

If an action could harm an innocent other, then it should not be done.

O

O

O

O

O

Deciding whether or not to perform an act by balancing the positive consequences of the act against the negative consequences of the act is immoral.

O

O

O

O

O

The dignity and welfare of people should be the most important concern in any society.

O

O

O

O

O

It’s never necessary to sacrifice the welfare of others.

O

O

O

O

O

Moral actions are those which closely match ideals of the most “perfect” action.

O

O

O

O

O

There are no ethical principles that are so important that they should be a part of any code of ethics.

O

O

O

O

O

Appendix 2.2 | - 41 -

Totally disagree

Disagree

Agree

Totally agree

O

Nor disagree, nor agree O

What is ethical varies from one situation and society to another.

O

O

O

Moral standards should be seen as being individualistic; what one person considers being moral may be judged to be immoral by another person.

O

O

O

O

O

Different types of moralities cannot be compared as to rightness.

O

O

O

O

O

What is ethical for everyone can never be resolved since what is moral or immoral is up to the individual.

O

O

O

O

O

Moral standards are simply personal rules which indicate how a person should behave, and are not to be applied in making judgments of others.

O

O

O

O

O

Ethical considerations in interpersonal relations are so complex that individuals should be allowed to formulate their own individual codes.

O

O

O

O

O

Rigidly codifying an ethical position that prevents certain types of actions stand in the way of better human relations and adjustment.

O

O

O

O

O

No rule concerning lying can be formulated; whether a lie is permissible or not permissible totally depends upon the situation.

O

O

O

O

O

Whether a lie is judged to be immoral depends upon the circumstances surrounding the actions.

O

O

O

O

O

Appendix 2.2 | - 42 -

3. In the next and last question of this part, we ask you to indicate how much these principles are important in your life. The scale is divides as follows: 0 = opposed to my principles 1 = not important 4 = important 8 = supreme important The values that are not mentioned are lying between the above values. = QUESTION 7  POWER (social power, authority, wealth)

0

1

2

3

4

5

6

7

8

 ACHIEVEMENT (Successful ,capable , ambitious, influential on people and situations) 0

1

2

3

4

5

6

7

8

 HEDONISM (self-indulgent, , enjoying life, pleasure) 0

1

2

3

4

5

6

7

8

 STIMULATION (daring, a varied life, an exciting life) 0

1

2

3

4

5

6

7

8

 SELF - DIRECTION (creativity, freedom, curious, independent, choosing own goals in life) 0

1

2

3

4

5

6

7

8

 UNIVERSALISM (broad-minded, a world of beauty, social justice, a world at peace , equality, wisdom, unity with nature protecting the environment) 0

1

2

3

4

5

6

7

8

 BENEVOLENCE (helpful, honest, forgiving, loyal, responsible) 0

1

2

3

4

5

6

7

8

 TRADITION (respect for tradition, humble, accepting portion in life, devout, moderate) 0

1

2

3

4

5

6

7

8

Appendix 2.2 | - 43 -

 CONFORMITY (obedient, honoring parents and elders, self-discipline, politeness) 0

1

2

3

4

5

6

7

8

 SECURITY (national security, family security, social order, clean, reciprocation of favors) 0

1

2

3

4

5

6

7

8

Appendix 2.2 | - 44 -

Part 3: Demographic Questions These last questions are standard questions to get an image of the respondents who fill in our survey.

Gender

O Male O Female

Age

O < 18 jaar O 18 – 24 jaar O 25 – 34 jaar O 35 – 44 jaar O 45 – 54 jaar O 55 – 65 jaar O > 65 jaar

Highest obtained education

O Primary School O Secondary School Short – type O Secondary School Long – type O Professional Bachelor O Academic Bachelor O Academic Master

Social status

O Single O In a relationship O Cohabiting O Married O Divorced O Widow/Widower

Beroep

O Student O Unemployed/ Job Seeker O Worker O Employee O Manager O Official O Self-employed O Retired

Thank you for your participation. Hylke Huys, Master student Business Economics – Marketing.

Appendix 2.2 | - 45 -

Appendix 3: Sources Instruments measure Variable

Source2

Behavioral Intention to accept identification Fishbein and Ajzen (1975) technology

Ajzen and Fishbein (1980) Venkatesh and Davis (2000) Venkatesh et al. (2003)

Behavioral intention to Recommend

Price and Arnould (1999)

identification technology Perceived Usefulness

Davis et al. (1989) Miltgen et al. ( 2013)

Perceived Ease of Use

Davis (1989) Venkatesh and Davis (2000) Miltgen et al. (2013)

Relative Advantage

Walker et al. (2002) Miltgen et al. (2013)

Privacy Concerns

Fogel and Nehmad (2009) Miltgen et al. (2013)

Technology Anxiety

Pavlou (2003) Miltgen et al. ( 2013)

Facilitating Conditions

Venkatesh et al. ( 2013)

Experience

Self- developed

Innovativeness

Yi et al. (2006) Miltgen et al. (2013)

Social Influence

Self – developed

Idealism & Relativism ( ethical ideologies )

Forsyth (1980)

Personal Values

Schwartz (1994)

2

The items are deducted from the sources, but most items are self-developed from the mentioned literature.

Appendix 3 | - 46 -

Appendix 4: Reliability Analysis output Appendix 4.1 Fingerprint recognition a. Behavioral Intention to accept identification technology - 6 items Reliability Statistics Cronbach's

N of Items

Alpha ,967

6

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

BI1

15,98

33,439

,907

,959

BI2

15,83

35,644

,804

,969

BI3

16,00

34,106

,907

,958

BI4

15,98

34,249

,940

,955

BI5

16,02

34,833

,901

,959

BI6

16,06

34,693

,897

,960

b. Behavioral intention to Recommend identification technology - 3 items Reliability Statistics Cronbach's

N of Items

Alpha ,961

3

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

BR1

6,11

5,166

,910

,947

BR2

6,25

5,103

,927

,934

BR3

6,09

4,997

,912

,946

c. Perceived Usefulness – 6 items Reliability Statistics Cronbach's

N of Items

Alpha ,825

6

Appendix 4.1 | - 47 -

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

PU1

18,13

13,552

,745

,763

PU2

17,32

17,439

,376

,834

PU3

17,85

13,732

,646

,785

PU4

18,00

12,982

,742

,762

PU5

18,33

13,768

,577

,802

PU6

17,96

15,609

,479

,819

Perceived Usefulness – 4 items: removal PU2 & PU5 Reliability Statistics Cronbach's

N of Items

Alpha ,820

4

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

PU1

10,74

6,742

,700

,748

PU3

10,46

6,550

,665

,763

PU4

10,61

5,999

,777

,706

PU6

10,57

8,115

,445

,855

d. Perceived Ease Of Use – 3 items Reliability Statistics Cronbach's

N of Items

Alpha ,789

3

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

PEOU1

7,79

2,508

,660

,681

PEOU2

7,59

2,611

,628

,716

PEOU3

7,85

2,316

,607

,745

Appendix 4.1 | - 48 -

Perceived Ease Of Use – 2 items after removal PEOU3 Reliability Statistics Cronbach's

N of Items

Alpha ,745

2

e. Relative Advantage – 4 items ( including PEOU3) Reliability Statistics Cronbach's

N of Items

Alpha ,781

4

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

PEOU3

9,92

5,750

,578

,732

RA1

9,90

5,797

,595

,723

RA2

10,67

5,483

,531

,761

RA3

10,54

5,656

,653

,695

f. Privacy Concerns – 5 items Reliability Statistics Cronbach's

N of Items

Alpha ,861

5

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

PC1

13,86

10,413

,717

,822

PC2

14,14

9,692

,775

,806

PC3

14,24

9,503

,804

,797

PC4

13,64

11,665

,583

,855

PC5r

14,15

11,789

,525

,868

Appendix 4.1 | - 49 -

g. Technology Anxiety – 5 items Reliability Statistics Cronbach's

N of Items

Alpha ,776

5

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

TA1

10,29

8,067

,602

,716

TA2

10,78

8,146

,640

,703

TA3

11,37

10,530

,231

,827

TA4

10,26

7,989

,651

,698

TA5

10,24

8,134

,642

,702

h. Facilitating Conditions – 3 items Reliability Statistics Cronbach's

N of Items

Alpha ,514

3

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

FC1

6,26

2,335

,389

,322

FC2

7,10

2,631

,192

,629

FC3

6,94

1,965

,427

,233

Facilitating Conditions – 2 items after removal FC2 Reliability Statistics Cronbach's

N of Items

Alpha ,629

2

Appendix 4.1 | - 50 -

i. Experience – 3 items Reliability Statistics Cronbach's

N of Items

Alpha ,766

3

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

Exp1

3,52

2,277

,678

,631

Exp2

3,85

2,823

,783

,470

Exp3

4,30

4,596

,442

,846

j. Innovativeness – 3 items Reliability Statistics Cronbach's

N of Items

Alpha ,825

3

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

Innov1

5,53

3,086

,694

,746

Innov2

6,10

3,294

,674

,767

Innov3

5,60

3,013

,679

,763

k. Social Influence – 3 items Reliability Statistics Cronbach's

N of Items

Alpha ,792

3

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

SI1

6,56

2,754

,589

,763

SI2

6,70

2,263

,743

,591

SI3

6,33

2,829

,578

,774

Appendix 4.1 | - 51 -

l. Idealism – 10 items Reliability Statistics Cronbach's

N of Items

Alpha ,840

10

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

ID1

32,79

23,185

,558

,823

ID2

32,92

21,631

,702

,809

ID3

32,88

22,361

,577

,821

ID4

32,64

22,205

,604

,819

ID5

32,71

22,732

,647

,816

ID6

33,08

23,376

,441

,835

ID7

33,03

22,630

,522

,827

ID8

32,79

23,883

,444

,834

ID9

32,84

23,370

,493

,829

ID10

32,88

24,871

,361

,840

m. Relativism – 10 items Reliability Statistics Cronbach's

N of Items

Alpha ,692

10

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

RE1

29,41

15,299

,550

,629

RE2

29,03

17,567

,332

,673

RE3

28,87

18,469

,199

,692

RE4

29,24

17,731

,216

,693

RE5

29,81

15,566

,546

,632

RE6

29,72

16,422

,396

,660

RE7

29,56

16,454

,359

,668

RE8

29,56

15,152

,590

,622

RE9

29,17

17,457

,323

,674

RE10

30,23

18,727

,055

,726

Appendix 4.1 | - 52 -

Relativism – 9 items after removal RE10 Reliability Statistics Cronbach's

N of Items

Alpha ,726

9

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

RE1

26,91

13,985

,558

,671

RE2

26,53

16,114

,351

,710

RE3

26,37

16,741

,262

,723

RE4

26,73

16,265

,231

,733

RE5

27,31

14,337

,539

,676

RE6

27,22

15,213

,381

,706

RE7

27,05

15,312

,334

,716

RE8

27,06

13,921

,586

,666

RE9

26,67

15,840

,369

,707

n. Self – Enhancement dimension – 3 items Reliability Statistics Cronbach's

N of Items

Alpha ,738

3

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

Power

10,97

9,046

,557

,664

Achievement

9,49

8,715

,702

,481

Hedonism

8,35

11,407

,448

,775

o. Openness to change – 2 items Reliability Statistics Cronbach's

N of Items

Alpha ,757

2

Appendix 4.1 | - 53 -

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

Stimulation Self Direction

5,80

2,557

,615

.

5,18

3,366

,615

.

p. Self – Transcendence – 2 items Reliability Statistics Cronbach's

N of Items

Alpha ,612

2

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

Universalism

6,34

2,041

,443

.

Benevolence

5,56

2,460

,443

.

q. Conservatism – 3 items Reliability Statistics Cronbach's

N of Items

Alpha ,742

3

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

Tradition

12,11

6,189

,564

,677

Conformity

11,22

7,332

,627

,598

Security

11,02

7,726

,532

,699

Appendix 4.1 | - 54 -

Appendix 4.2 Iris scanning and facial recognition a. Behavioral Intention to accept identification technology – 6 items

Reliability Statistics Cronbach's

N of Items

Alpha ,913

6 Item-Total Statistics

Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

BI1

13,27

22,884

,770

,896

BI2

13,02

23,609

,679

,910

BI3

13,48

22,398

,789

,893

BI4

13,56

23,906

,794

,893

BI5

13,27

24,233

,750

,899

BI6

13,27

24,229

,781

,895

b. Behavioral intention to Recommend identification technology – 3 items Reliability Statistics Cronbach's

N of Items

Alpha ,948

3

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

BR1

5,22

4,404

,868

,940

BR2

5,33

4,399

,907

,911

BR3

5,20

4,228

,897

,919

c. Perceived Usefulness – 5 items Reliability Statistics Cronbach's

N of Items

Alpha ,763

5

Appendix 4.2 | - 55 -

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

PU1

11,85

8,366

,497

,734

PU2

10,67

11,751

,052

,837

PU3

12,26

7,650

,654

,674

PU4

12,17

7,472

,711

,651

PU5

12,10

7,143

,745

,635

Perceived Usefulness – 4 items after removal PU2 Reliability Statistics Cronbach's

N of Items

Alpha ,837

4

d. Perceived Ease Of Use – 3 items Reliability Statistics Cronbach's

N of Items

Alpha ,646

3

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

PEOU1

6,16

1,967

,543

,418

PEOU2

5,87

2,201

,556

,416

PEOU3

6,97

2,682

,294

,754

Perceived Ease Of Use – 2 items after removal PEOU3 Reliability Statistics Cronbach's

N of Items

Alpha ,755

2

e. Relative Advantage – 4 items ( including PEOU3) Reliability Statistics Cronbach's

N of Items

Alpha

Appendix 4.2 | - 56 -

,888

4

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

PEOU3

7,74

6,252

,744

,861

RA1

7,59

5,712

,764

,855

RA2

7,77

6,178

,774

,850

RA3

7,72

6,149

,746

,860

f. Privacy Concerns – 5 items Reliability Statistics Cronbach's

N of Items

Alpha ,834

5

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

PC1

14,93

9,811

,696

,784

PC2

15,13

9,260

,703

,781

PC3

15,07

9,087

,742

,768

PC4

14,98

10,191

,597

,811

PC5r

15,19

11,488

,439

,849

g. Technology Anxiety – 5 items Reliability Statistics Cronbach's

N of Items

Alpha ,777

5

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

TA1

11,80

8,528

,614

,714

TA2

12,41

8,357

,605

,717

TA3

12,87

9,799

,385

,791

TT1r

11,77

8,875

,650

,705

TT2r

11,85

9,545

,522

,746

Appendix 4.2 | - 57 -

h. Facilitating Conditions – 3 items Reliability Statistics Cronbach's

N of Items

Alpha ,647

3

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

FC1

5,83

3,242

,404

,620

FC2

6,13

2,869

,473

,529

FC3

6,33

3,021

,498

,495

i. Experience – 3 items Reliability Statistics Cronbach's

N of Items

Alpha ,794

3

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

Exp1

3,49

2,815

,555

,862

Exp2

3,97

3,090

,802

,552

Exp3

4,08

3,866

,624

,749

j. Innovativeness – 3 items Reliability Statistics Cronbach's

N of Items

Alpha ,846

3

Appendix 4.2 | - 58 -

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

Innov1

5,43

2,951

,774

,726

Innov2

5,97

3,506

,690

,811

Innov3

5,40

3,030

,688

,815

k. Social Influence – 3 items Reliability Statistics Cronbach's

N of Items

Alpha ,736

3

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

SI1

6,61

2,953

,563

,646

SI2

6,78

2,562

,640

,548

SI3

6,35

3,223

,484

,734

l. Idealism – 10 items Reliability Statistics Cronbach's

N of Items

Alpha ,910

10

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

ID1

33,53

30,438

,736

,897

ID2

33,60

29,553

,768

,895

ID3

33,64

29,028

,763

,895

ID4

33,50

29,268

,733

,897

ID5

33,50

29,955

,714

,898

ID6

33,81

29,840

,660

,902

ID7

33,61

32,364

,503

,910

ID8

33,57

30,982

,623

,903

ID9

33,54

29,641

,778

,894

ID10

33,61

32,942

,439

,913

Appendix 4.2 | - 59 -

m. Relativism – 10 items Reliability Statistics Cronbach's

N of Items

Alpha ,674

10

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

RE1

29,76

16,037

,464

,627

RE2

29,46

17,530

,284

,660

RE3

29,36

16,939

,423

,639

RE4

29,58

17,715

,221

,671

RE5

30,06

15,232

,540

,609

RE6

30,20

15,777

,448

,628

RE7

29,99

16,162

,344

,649

RE8

30,09

14,792

,574

,599

RE9

29,75

17,967

,166

,681

RE10

30,78

18,966

-,009

,722

Relativism – 9 items after removal RE10 Reliability Statistics Cronbach's

N of Items

Alpha ,722

9

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

RE1

27,31

15,040

,486

,681

RE2

27,01

16,279

,344

,706

RE3

26,92

15,942

,445

,692

RE4

27,14

16,591

,255

,721

RE5

27,61

14,533

,518

,674

RE6

27,75

15,203

,404

,696

RE7

27,54

15,265

,348

,708

RE8

27,65

13,965

,574

,661

RE9

27,31

16,744

,212

,729

Appendix 4.2 | - 60 -

n. Self – Enhancement – 3 items Reliability Statistics Cronbach's

N of Items

Alpha ,693

3

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

Power

10,89

8,869

,549

,551

Achievement

9,43

8,217

,678

,357

Hedonism

8,07

13,840

,342

,779

o. Openness to change – 2 items Reliability Statistics Cronbach's

N of Items

Alpha ,692

2

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

Stimulation Self Direction

5,74

2,192

,536

.

5,11

3,025

,536

.

p. Self – Transcendence – 2 items Reliability Statistics Cronbach's

N of Items

Alpha ,548

2

Appendix 4.2 | - 61 -

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

Universalism

6,26

1,692

,393

.

Benevolence

5,17

2,994

,393

.

q. Conservatism – 3 items Reliability Statistics Cronbach's

N of Items

Alpha ,754

3

Item-Total Statistics Scale Mean if

Scale Variance

Corrected Item-

Cronbach's

Item Deleted

if Item Deleted

Total

Alpha if Item

Correlation

Deleted

Tradition

11,62

7,296

,575

,688

Conformity

10,76

7,611

,679

,563

Security

10,48

8,913

,509

,751

Appendix 4.2 | - 62 -

Appendix 5: Factor Analysis constructs Question 3 & 4 Appendix 5.1 Fingerprint recognition Total Variance Explained Component

Initial Eigenvalues

Total

% of

Cumulative

Variance

%

Extraction Sums of Squared

Rotation Sums of Squared

Loadings

Loadings

Total

% of

Cumulative

Variance

%

Total

% of

Cumulative

Variance

%

1

9,851

35,183

35,183

9,851

35,183

35,183

5,387

19,240

19,240

2

2,526

9,023

44,205

2,526

9,023

44,205

4,756

16,987

36,227

3

2,054

7,335

51,540

2,054

7,335

51,540

3,216

11,486

47,713

4

1,687

6,025

57,565

1,687

6,025

57,565

2,148

7,671

55,384

5

1,331

4,755

62,320

1,331

4,755

62,320

1,731

6,181

61,565

6

1,147

4,096

66,415

1,147

4,096

66,415

1,297

4,631

66,196

7

1,054

3,765

70,180

1,054

3,765

70,180

1,115

3,984

70,180

8

,839

2,997

73,177

Extraction Method: Principal Component Analysis.

Appendix 5.1 | - 63 -

Rotated Component Matrix

a

Component 1

2

3

PU1

,571

,513

PU2

,251

,287

PU3

,759

PU4

,717

,303

PU5

,326

,783

PU6

,516

,392

PEOU1

,800

,241

PEOU2

,658

PEOU3

,751

RA1

,787

RA2

,221

,817

RA3

,543

,483

4

5

6

-,207 ,593

-,247

-,321

,226

PC1

,850

PC2

,836

PC3

,864 -,396

,578

-,558

,367

TA1

-,637

,265

,332

TA2

-,484

,267

,625

PC4 PC5r

-,268

,276

,742

TA4

-,315

-,705

,218

TA5

-,243

-,651

,255

FC1

,651

-,274 -,213

-,290 -,247

,232

-,380

,873

FC2 ,337

,515

,220

Exp1

,872

Exp2

,896

Exp3

,268 ,394

TA3

FC3

7

,258

,531

-,391

,607

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 7 iterations.

Appendix 5.1 | - 64 -

Appendix 5.2 Iris scanning and facial recognition Total Variance Explained Component

Initial Eigenvalues

Total

% of

Cumulative

Variance

%

Extraction Sums of Squared

Rotation Sums of Squared

Loadings

Loadings

Total

% of

Cumulative

Variance

%

Total

% of

Cumulative

Variance

%

1

8,507

31,509

31,509

8,507

31,509

31,509

5,898

21,846

21,846

2

3,193

11,825

43,334

3,193

11,825

43,334

3,349

12,404

34,250

3

2,607

9,657

52,991

2,607

9,657

52,991

2,941

10,892

45,142

4

1,805

6,686

59,677

1,805

6,686

59,677

2,518

9,328

54,469

5

1,220

4,518

64,195

1,220

4,518

64,195

2,194

8,127

62,597

6

1,155

4,278

68,473

1,155

4,278

68,473

1,587

5,876

68,473

7

,972

3,600

72,073

Extraction Method: Principal Component Analysis.

Appendix 5.2 | - 65 -

Rotated Component Matrix

a

Component 1 PU1

2

3

,480

4

5

-,318 ,699

PU2 PU3

,864

PU4

,860

PU5

,754

PEOU1

,351

PEOU2

,202

PEOU3

,824

RA1

,828

RA2

,739

RA3

,750

-,208 ,682

-,241

,732

-,228 ,253

PC1

,844

PC2

,879

PC3

,260

,842

PC4

,591

,418

PC5r

6

-,403

,536

TA1

,671

TA2

,793

-,246 ,343

TA3

,333

,673

TT1r

-,336

,687

,208

TT2r

-,406

,492

,308

FC1

-,230

,238

-,202 -,271

,236

FC3

,337

,443 ,835 ,669

,292 ,796

Exp1

-,211 ,558

-,360

FC2

-,209

Exp2

,209

,866

Exp3

,308

,733

,237

-,231

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 6 iterations.

Appendix 5.2 | - 66 -

Appendix 6: Final list of items per construct (original: Dutch) Appendix 6.1 Fingerprint recognition Construct

Questions

Items

BI

Q1_1

onwaarschijnlijk – waarschijnlijk

Q1_2

onmogelijk – mogelijk

Q1_3

zou het zeker niet gebruiken – zou het zeker en vast gebruiken

Q2_1

Aangezien ik het vingerafdruk toestel kan gebruiken, ben ik van plan het te gebruiken.

BR

Q2_2

Ik neem mij voor het toestel nogmaals te gebruiken in de komende maanden.

Q2_3

Ik ga het toestel zeker gebruiken in de komende maanden.

Q2_4

Volgende keer, als ik mijn vrienden/familie zie, zal ik zeker positief vertellen over dit nieuwe systeem.

PU

Q2_5

Ik zou dit vingerafdruk systeem aanraden aan anderen.

Q2_6

Ik zou dit vingerafdruk systeem aanraden aan mensen, die mijn advies vragen.

Q3_1

Het Vingerafdruksysteem zorgt voor een waardevolle dienstverlening.

Q3_3

Door het Vingerafdruksysteem te kiezen als betaalmiddel, zal ik mij tijd besparen.

Q3_4

Kunnen betalen met behulp van een Vingerafdruksysteem maakt winkelen gemakkelijker.

Q3_6

Met het Vingerafdruksysteem worden mijn persoonlijke gegevens op een gemakkelijke manier bijgehouden.

PEOU

Q3_7

Het gebruik van een Vingerafdruksysteem is gemakkelijk.

Q3_8

Het gebruik van een Vingerafdruksysteem vergt weinig kennis of mentale inspanning.

RA

PC

Q3_9

Het Vingerafdruksysteem is simpeler dan andere betaalmiddelen.

Q3_10

Het Vingerafdruksysteem levert een snellere dienstverlening.

Q3_11

Het Vingerafdruksysteem levert een betrouwbaardere dienstverlening.

Q3_12

Het Vingerafdruksysteem levert een meer geschikte dienstverlening.

Q4_1

Met het Vingerafdruk systeem krijgen bedrijven persoonlijke informatie over mij, die ik als privé beschouw.

Q4_2

Met het Vingerafdruk systeem wordt mijn persoonlijke informatie gebruikt zonder mijn medeweten.

Q4_3

Met het Vingerafdruk systeem worden mijn persoonlijke gegevens met verschillende mensen en zonder mijn toestemming gedeeld.

Q4_4

Ik ben bezorgd over de gevolgen door mijn persoonlijke informatie te delen.

Q4_5

Ik ben er zeker van dat mijn persoonlijke informatie goed wordt beschermd.

Appendix 6.1 | - 67 -

Construct

Questions

Items

TA

Q4_6

Ik ben bang om het Vingerafdruk systeem te gebruiken.

Q4_7

Vanwege mijn angst voor technologie, zou ik eerder naar andere winkels gaan, waar ze het Vingerafdruk systeem niet gebruiken.

Q4_8

Ik twijfel om het Vingerafdruk systeem te gebruiken uit schrik voor schade aan mijn eigen lichaam.

FC

Q4_9

Ik zou het Vingerafdruk systeem vertrouwen.

Q4_10

Ik denk dat de dienstverlening met het Vingerafdruk systeem betrouwbaar is.

Q4_11

Het Vingerafdruk systeem is gemakkelijk omdat ik niets anders nodig heb dan mijn eigen lichaam.

Q4_12

Ik zou het Vingerafdruk systeem eerder gebruiken indien er personen beschikbaar zijn voor eventuele vragen/problemen te beantwoorden.

Q4_13

Ik zou de het Vingerafdruk systeem eerder gebruiken omdat de identificatie van mensen zo gemakkelijker is.

Exp

Q4_14

Ik heb ervaring met de systemen in andere situaties/omgevingen. (Voorbeelden: in de luchthaven, Iphone, )

Innov

Q4_15

Ik gebruik de systemen frequent in andere situaties/omgevingen.

Q4_16

Ik heb ervaring met de systemen in een warenhuis-omgeving.

Q5_1

Ik ben ten opzichte van mijn vrienden/familie één van de eersten om een nieuwe technologie uit te proberen.

Q5_2

Wanneer ik hoor dat er een nieuwe technologie op de markt is, zoek ik meteen naar manieren om aan deze technologie te geraken.

SI

ID

Q5_3

Ik hou ervan om te experimenteren met nieuwe technologieën.

Q5_4

Als mijn vrienden mij iets aanraden, ben ik geneigd om dit te proberen.

Q5_5

Ik ben beïnvloedbaar door de mening van mijn familie of vrienden.

Q5_6

Ik hecht veel waarde aan de mening van mijn vrienden of familie.

Q6_1 –

See survey appendix 1.1 general question 2

_10 RE

Q6_11_19

See survey appendix 1.1 general question 2

Self –

Q7_1

Macht

Enhancement

Q7_2

Prestatie

Q7_3

Hedonisme

Openness to

Q7_4

Stimulatie

change

Q7_5

Zelfsturing

Self-

Q7_6

Universalisme

Transcendence

Q7_7

Welwillendheid

Conservatism

Q7_8

Traditie

Q7_9

Overeenstemming

Q7_10

Veiligheid

Appendix 6.1 | - 68 -

Appendix 6.2 Iris scanning and facial recognition Construct

Questions

Items

BI

Q1_1

onwaarschijnlijk – waarschijnlijk

Q1_2

onmogelijk – mogelijk

Q1_3

zou het zeker niet gebruiken – zou het zeker en vast gebruiken

Q2_1

Aangezien deze technologie mij helpt bij het uitkiezen van bepaalde producten, ga ik de winkel binnengaan.

BR

Q2_2

Ik neem mij voor de winkel nogmaals te bezoeken in de komende maanden.

Q2_3

Ik ga de winkel zeker blijven gebruiken in de komende maanden.

Q2_4

Volgende keer, als ik mijn vrienden/familie zie, zal ik zeker positief vertellen over de technologieën dat deze winkel gebruikt.

PU

Q2_5

Ik zou deze manier van winkelen aanraden aan anderen.

Q2_6

Ik zou deze manier van winkelen aanraden aan mensen die mijn advies vragen.

Q3_1

De Iris Scanning en de Gezicht Scanner zorgen voor een waardevolle dienstverlening.

Q3_3

Door een winkel te kiezen dat Iris Scanning & Gezicht Scanning gebruikt, zal ik mij tijd besparen.

Q3_4

Iris Scanning & Gezicht Scanning maken winkelen gemakkelijker.

Q3_5

Met Iris Scanning & Gezicht Scanning wordt het voor mij als koper makkelijker want mijn persoonlijke gegevens worden op een gemakkelijke manier bijghouden.

PEOU

Q3_6

Het gebruik van Iris Scanning & Gezicht Scanning vergt een minimum aan inspanning.

RA

Q3_7

Iris Scanning & Gezicht Scanning vergen weinig kennis of mentale inspanning.

Q3_8

Een winkel met Iris Scanning & Gezicht Scanning is simpeler dan winkels zonder. Een winkel met Iris Scanning en een Gezicht Scanner levert een snellere

Q3_9

dienstverlening dan een winkel zonder deze technologieën. Een winkel met Iris Scanning en een Gezicht Scanner levert een betrouwbaardere

Q3_10

dienstverlening dan een winkel zonder deze technologieën. Een winkel met Iris Scanning en Gezicht Scanner levert een meer geschikte

PC

Q3_11

dienstverlening dan winkels zonder deze technologieën.

Q4_1

Met deze systemen krijgen bedrijven persoonlijke informatie over mij, die ik als privé beschouw.

Q4_2

Met deze systemen wordt mijn persoonlijke informatie gebruikt zonder mijn medeweten.

Q4_3

Ik ben er zeker van dat mijn persoonlijke informatie goed wordt beschermd.

Q4_4

Ik ben bezorgd over de gevolgen door mijn persoonlijke informatie te delen.

Q4_5

Met deze systemen worden mijn persoonlijke gegevens met verschillende mensen en zonder mijn toestemming gedeeld.

Appendix 6.2 | - 69 -

Construct

Questions

Items

TA

Q4_6

Ik ben bang om de systemen toe te staan.

Q4_7

Vanwege mijn angst voor technologie, zou ik eerder naar andere winkels gaan, waar ze deze systemen niet gebruiken.

Q4_8

Ik twijfel om de systemen toe te staan uit schrik voor schade aan mijn eigen lichaam.

FC

Q4_9

Ik zou de systemen vertrouwen.

Q4_10

Ik denk dat de dienstverlening met deze systemen betrouwbaar is.

Q4_11

De systemen zijn gemakkelijk omdat ik niets anders nodig heb dan mijn eigen lichaam.

Q4_12

Ik zou de systemen eerder gebruiken/toestaan indien er personen beschikbaar zijn voor eventuele vragen/problemen te beantwoorden.

Q4_13

Ik zou de systemen eerder toestaan omdat de identificatie van mensen zo gemakkelijk is.

Exp

Q4_14

Ik heb ervaring met de systemen in andere situaties/omgevingen. (Luchthavens,Casino’s)

Innov

Q4_15

Ik gebruik de systemen frequent in andere situaties/omgevingen.

Q4_16

Ik heb ervaring met de systemen in een warenhuis-omgeving.

Q5_1

Ik ben ten opzichte van mijn vrienden/familie één van de eersten om een nieuwe technologie uit te proberen.

Q5_2

Wanneer ik hoor dat er een nieuwe technologie op de markt is, zoek ik meteen naar manieren om aan deze technologie te geraken.

Q5_3

Ik hou ervan om te experimenteren met nieuwe technologieën.

Q5_4

Als mijn vrienden mij iets aanraden, ben ik geneigd om dit te proberen.

Q5_5

Ik ben beïnvloedbaar door de mening van mijn familie of vrienden.

Q5_6

Ik hecht veel waarde aan de mening van mijn vrienden of familie.

ID

Q6_1-_10

See survey appendix 2 general question 2

RE

Q6_11_19

See survey appendix 1.2 general question 2

Self –

Q7_1

Macht

Enhancement

Q7_2

Prestatie

Q7_3

Hedonisme

Openness to

Q7_4

Stimulatie

change

Q7_5

Zelfsturing

Self-

Q7_6

Universalisme

Transcendence

Q7_7

Welwillendheid

Conservatism

Q7_8

Traditie

Q7_9

Overeenstemming

Q7_10

Veiligheid

SI

Appendix 6.2 | - 70 -

Appendix 7: Correlation Diagnostics and table Appendix 7.1 Fingerprint recognition Correlations BI Pearson Correlation BI

Pearson Correlation

,853

,164

Id

,088

Re

SE

RC

,082 -,048

,141

-,075

,270

,312

,556

,084

,361

175

171

170

171

160

160

160

160

160

160

155

155

152

152

1

**

**

**

**

**

**

*

**

,124

,130

,004

,188

*

-,027

,713

,713

,201

,227

,000

,012

,004

,124

,112

,958

,023

,749

167

167

167

156

156

156

156

155

155

150

150

147

147

1

**

**

**

**

**

**

*

-,015

,005

**

-,053

**

,647

,647

,067 ,333

,168

,279

,000

,400

,000

,034

,858

,947

,001

,521

170

171

160

160

160

160

159

159

154

154

151

151

1

**

**

**

**

*

-,012

167

170

170

**

**

**

**

,638

,638

,091 ,241

,098

,076

,055

,166

,000

,256

,002

,220

,348

,497

,043

,883

170

159

159

159

159

158

158

153

153

150

150

**

**

**

**

**

,104

,058 -,020

,171

*

,072

1 -,485

,000

,000

N

171

167

171

170

171

**

**

**

**

**

-,485

,204

,286

,000

,010

,000

,190

,478

,803

,036

,383

160

160

160

160

159

159

154

154

151

151

1

**

**

**

*

-,061

,006

,077

-,094

,004

,000

,000

,000

,000

N

160

156

160

159

160

160

**

**

**

**

**

**

,000

,498

,000

,000

-,643

-,643

,000

Sig. (2-tailed)

,000

,411

,000

,000

-,405

-,405

,000

,000

-,278

-,278

**

,000

Sig. (2-tailed)

,000

,554

,000

170

-,572

-,572

,000

,000

-,435

-,435

,000

,000

,773

,773

,000

,000

,000

,535

,000

171

-,706

-,706

,000

**

-,582

-,582

,000

167

,754

,754

,000

**

,480

,480

,000

171

,000

,194

SI

*

,039

N

Sig. (2-tailed)

,489

Innov *

,014

,000

Pearson Correlation -,757

-,757

Exp **

,000

,000

Pearson Correlation -,538

-,538

FC **

,000

Sig. (2-tailed)

,765

,765

TA **

,000

**

Pearson Correlation

PC **

,000

175

,484

,484

RA

,000

**

N

TA

,703

**

,000

175 ,703

PEOU **

,000

N

PEOU Sig. (2-tailed)

PC

**

,853

,000

Pearson Correlation

RA

187

PU **

Sig. (2-tailed)

Pearson Correlation PU

1

Sig. (2-tailed) N

BR

BR

,545

,000

,545

-,329

-,223

-,175

,000

,000

,005

,027

,442

,945

,343

,252

,964

160

160

160

159

159

154

154

151

151

**

,121

,006

,139

**

-,092

,000

,248

1 -,443

-,152 -,075 ,055

,350

-,034 ,674

,016 -,223 ,844

Appendix 7.1 | - 71 -

N

160 BI

Pearson Correlation FC

Exp

Innov

SI

Id

SE

,535

PU **

,554

159 PEOU

**

,411

**

160 RA ,498

160 PC

**

-,329

160 TA

**

-,443

160 FC

**

1

,000

,000

,000

,000

,000

,000

N

160

156

160

159

160

160

160

*

*

,067

,091

**

**

-,092

,115

,201

SI

Id

Re

,137

,190

,147

,016

,085

,018

160

159

159

**

,012

,400

,256

,010

,005

,248

,147

N

160

156

160

159

160

160

160

160

160

*

**

**

**

**

*

-,152

,192

*

**

-,175

154

,192

,014

,286

154

,115

Sig. (2-tailed)

,241

159

*

,194

,333

-,223

160

159 Innov

Pearson Correlation

,227

,204

160 Exp

,000

,164

,229

Sig. (2-tailed)

,039

,004

,000

,002

,000

,027

,055

,016

,004

N

160

155

159

158

159

159

159

159

159

*

,098

,104

-,061

-,075

,137

RC

,002

,013

,246

154

154

151

151

-,087

-,022

,080

,018

,038

,004

,275

,783

,323

,822

,641

159

159

154

154

151

151

**

-,030

,112

,085

-,122

,000

,710

,164

,297

,135

160

160

155

155

152

152

**

1

-,100

,106

,113

-,068

,217

,190

,165

,408

155

155

152

152

1

*

**

Pearson Correlation

,088

,124

,168

Sig. (2-tailed)

,270

,124

,034

,220

,190

,442

,350

,085

,275

,000

N

160

155

159

158

159

159

159

159

159

160

*

-,087 ,375

-,022

160

Pearson Correlation

,082

,130

-,015

,076

,058

,006

-,034

,190

Sig. (2-tailed)

,312

,112

,858

,348

,478

,945

,674

,018

,783

,710

,217

N

155

150

154

153

154

154

154

154

154

155

155

**

,080

,112

,106

,191

-,030 -,100

,242

,191

,201

-,324

,194

*

,017

,000

,017

155

155

152

152

*

1

,092

-,072

,262

,380

152

152

-,048

,004

,005

,055

-,020

,077

,016

Sig. (2-tailed)

,556

,958

,947

,497

,803

,343

,844

,002

,323

,164

,190

,017

N

155

150

154

153

154

154

154

154

154

155

155

155

155

Pearson Correlation

,141

,188

*

**

*

*

**

*

,018

,085

,113 -,324

**

,092

1 -,206

Sig. (2-tailed)

,084

,023

,001

,043

,036

,252

,006

,013

,822

,297

,165

,000

,262

,011

N

152

147

151

150

151

151

151

151

151

152

152

152

152

152

152

-,122 -,068

*

-,072

-,206

*

1

,279

,166

,171

-,094 -,223

,242

SE **

151

-,095

1 ,375

*

151

*

1 ,229

Pearson Correlation

Pearson Correlation RC

BR **

160

Sig. (2-tailed)

Pearson Correlation Re

,489

156

,201

-,075

-,027

-,053

-,012

,072

,004

,121

-,095

,038

,194

Sig. (2-tailed)

,361

,749

,521

,883

,383

,964

,139

,246

,641

,135

,408

,017

,380

,011

N

152

147

151

150

151

151

151

151

151

152

152

152

152

152

*

152

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

Appendix 7.1 | - 72 -

Appendix 7.2 Iris scanning and facial recognition Correlations BI Pearson Correlation BI

Pearson Correlation

,846

,267

,342

**

SI ,223

Id **

,041

Re

SE

RC

,078

,182

*

,099

,000

,007

,631

,360

,034

,250

148

145

145

145

144

143

143

143

144

144

138

138

136

137

1

**

**

**

**

**

**

**

**

*

,021

,072

,179

*

,662

,662

,507

,250

,292

,179

,203

*

,000

,000

,003

,000

,033

,810

,409

,039

,019

142

142

142

141

140

140

140

141

141

135

135

133

134

1

**

**

**

**

**

**

**

**

*

**

,148

145

**

**

,335

,335

,470

,245

,354

,236

,072 ,187

,226

,001

,000

,003

,000

,004

,398

,028

,008

,084

145

145

144

143

143

143

144

144

138

138

136

137

1

**

**

**

-,025

,060

,019

,026

,116

,151

,025

145

142

145

145

**

**

**

**

,293

Sig. (2-tailed)

,000

,000

,000

,000

N

145

142

145

145

**

**

**

,293

-,089 -,308 ,286

,000

,000

,764

,472

,824

,763

,177

,080

,768

145

144

143

143

143

144

144

138

138

136

137

**

**

**

**

**

,096

,107

,033

,214

*

1 -,380

145

-,089 -,380

**

,448

,345

,312

,194

*

,000

,000

,000

,000

,251

,213

,701

,012

,023

144

143

143

143

144

144

138

138

136

137

1

**

**

**

-,126

-,008

-,040

,074

-,008

-,133

,000

,000

,286

,000

N

144

141

144

144

144

144

**

**

**

**

**

**

-,302

-,302

,000

,000

-,308

,335

,000

Sig. (2-tailed)

-,263

-,263

,000

,000

-,371

-,371

,000

,005

,818

,818

,000

,004

-,419

-,419

,000

142

-,449

-,449

,000

**

,616

,616

,005

145

,233

,233

,000

N

Sig. (2-tailed)

,485

Innov **

,001

,000

Pearson Correlation -,551

-,551

Exp **

,000

,000

Pearson Correlation -,449

-,449

FC **

,000

Sig. (2-tailed)

,631

,631

TA **

,000

**

Pearson Correlation

PC **

,000

148

,235

,235

RA

,004

**

N

TA

,674

**

,000

148 ,674

PEOU **

,000

N

PEOU Sig. (2-tailed)

PC

**

,846

,000

Pearson Correlation

RA

151

PU **

Sig. (2-tailed)

Pearson Correlation PU

1

Sig. (2-tailed) N

BR

BR

,562

,000

,000

,001

,000

,000

,000

143

140

143

143

143

143

,562

-,295

-,319

,000

,000

,000

,132

,921

,642

,390

,923

,121

143

143

143

144

144

138

138

136

137

**

**

*

-,057

,009

,039

,030

,038

1 -,351

-,223

-,186

,000

,007

,026

,498

,916

,647

,729

,662

143

143

143

143

137

137

135

136

N 143

Appendix 7.2 | - 73 -

BI

Pearson Correlation FC

Id

Re

SE

RC

**

,470

**

RA

**

,448

,335

PC **

-,295

TA **

-,351

FC **

Exp 1

,000

,000

,000

,000

,000

,000

N

143

140

143

143

143

143

143

143

**

**

**

-,025

**

**

**

*

,267

,250

,245

,345

-,319

-,223

,198

Innov

,018

,057

,108

,289

143

143

143

137

1

*

*

,000

,000

,007

,018

N

143

140

143

143

143

143

143

143

143

**

**

**

,060

**

-,126

-,186

*

,160

,170

*

,170

,108

,079

,006

,211

137

135

136

,102 -,109

-,083 ,243

**

,237

,206

,339

,004

143

143

137

137

135

136

*

**

1 ,435

,000

,000

,472

,000

,132

,026

,057

,042

N

144

141

144

144

144

144

143

143

143

144

**

*

**

,019

,096

-,008

-,057

,135

-,188

*

**

,236

**

,025

,000

,179

-,188

RC

,042

Sig. (2-tailed)

,223

,236

,151

,764

,312

SE

,091

,003

,354

Re

,135

,003

,292

Id

,160

,001

,342

SI

*

,198

Sig. (2-tailed)

Pearson Correlation SI

,507

PEOU

,000

Pearson Correlation Innov

**

PU

Sig. (2-tailed)

Pearson Correlation Exp

,485

BR

,435

Sig. (2-tailed)

,007

,033

,004

,824

,251

,921

,498

,108

,025

,000

N

144

141

144

144

144

144

143

143

143

144

**

-,006 ,176

,288

-,171

*

,000

,948

,039

,001

,046

144

138

138

136

137

,095

,130

**

-,135

,267

,127

,010

,115

138

138

136

137

**

,024

,618

,000

,781

1

144

1

,221

Pearson Correlation

,041

,021

,072

,026

,107

-,040

,009

,091

,102

-,006

,095

,043 -,437

Sig. (2-tailed)

,631

,810

,398

,763

,213

,642

,916

,289

,237

,948

,267

N

138

135

138

138

138

138

137

137

137

138

138

138

138

136

137

*

,116

,033

,074

,039

,151

-,109

,176

*

,130

,043

1

,161

-,039

,062

,655

Pearson Correlation

,078

,072

,187

Sig. (2-tailed)

,360

,409

,028

,177

,701

,390

,647

,079

,206

,039

,127

,618

N

138

135

138

138

138

138

137

137

137

138

138

138

138

136

137

*

*

**

,151

,214

*

-,008

,030

**

**

**

**

,161

1

-,110

Pearson Correlation

,182

,179

,226

,236

-,083 ,288

Sig. (2-tailed)

,034

,039

,008

,080

,012

,923

,729

,006

,339

,001

,010

,000

,062

N

136

133

136

136

136

136

135

135

135

136

136

136

136

136

136

Pearson Correlation

,099

,203

*

,148

,025

,194

*

-,133

,038

,108

**

*

-,135

,024 -,039

-,110

1

Sig. (2-tailed)

,250

,019

,084

,768

,023

,121

,662

,211

,004

,046

,115

,781

,655

,202

N

137

134

137

137

137

137

136

136

136

137

137

137

137

136

,243

-,171

,221

-,437

,202

137

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

Appendix 7.2 | - 74 -

Appendix 8: Mediator and Moderator relations regression analysis Appendix 8.1 Mediation hypothesis 3b Iris scanning and facial recognition Coefficients Model

a

Unstandardized Coefficients

Standardized

t

Sig.

Coefficients B (Constant)

Std. Error 1,672

,351

,283

,098

Beta 4,769

,000

2,887

,004

1 Perceived Ease Of Use

,235

a. Dependent Variable: Behavioral Intention to Accept Coefficients Model

a

Unstandardized Coefficients

Standardized

t

Sig.

Coefficients B (Constant)

Std. Error 1,442

,296

,352

,083

Beta 4,868

,000

4,247

,000

1 Perceived Ease Of Use

,335

a. Dependent Variable: Perceived Usefulness

Coefficients Model

a

Unstandardized Coefficients

Standardized

t

Sig.

Coefficients B

1

Std. Error

(Constant)

,562

,289

Perceived Usefulness

,770

,075

Perceived Ease Of Use

,012

,079

Beta 1,948

,053

,671

10,197

,000

,010

,155

,877

a. Dependent Variable: Behavioral Intention to Accept

Appendix 8.1 | - 75 -

Fingerprint recognition Coefficients Model

a

Unstandardized Coefficients

Standardized

t

Sig.

Coefficients B

Std. Error

(Constant)

,235

,427

Perceived Ease Of Use

,766

,107

Beta ,551

,582

7,172

,000

1 ,484

a. Dependent Variable: Behavioral Intention to Accept

Coefficients Model

a

Unstandardized Coefficients

Standardized

t

Sig.

Coefficients B

Std. Error

(Constant)

,684

,263

Perceived Ease Of Use

,724

,066

Beta 2,597

,010

10,998

,000

1 ,647

a. Dependent Variable: Perceived Usefulness

Coefficients Model

a

Unstandardized Coefficients

Standardized

t

Sig.

Coefficients B (Constant) 1

Std. Error -,405

,356

Perceived Usefulness

,937

,102

Perceived Ease Of Use

,087

,115

Beta -1,137

,257

,663

9,151

,000

,055

,760

,448

a. Dependent Variable: Behavioral Intention to Accept

Appendix 8.1 | - 76 -

Appendix 8.2 Mediation hypothesis 6 Iris scanning and facial recognition Coefficients Model

a

Unstandardized Coefficients

Standardized

t

Sig.

Coefficients B

Std. Error

(Constant)

4,804

,367

Privacy Concerns

-,571

,095

Beta 13,088

,000

-5,993

,000

1 -,449

a. Dependent Variable: Behavioral Intention to Accept

Coefficients Model

a

Unstandardized Coefficients

Standardized

t

Sig.

Coefficients B (Constant)

Std. Error 1,040

,253

,530

,066

Beta 4,112

,000

8,071

,000

1 Privacy Concerns

,562

a. Dependent Variable: Technology Anxiety

Coefficients Model

a

Unstandardized Coefficients

Standardized

t

Sig.

Coefficients B

1

Std. Error

(Constant)

5,415

,358

Technology Anxiety

-,587

,113

Privacy Concerns

-,262

,106

Beta 15,141

,000

-,435

-5,210

,000

-,206

-2,465

,015

a. Dependent Variable: Behavioral Intention to Accept

Appendix 8.2 | - 77 -

Fingerprint recognition Coefficients Model

a

Unstandardized Coefficients

Standardized

t

Sig.

Coefficients B

Std. Error

(Constant)

6,049

,358

Privacy Concerns

-,799

,100

Beta 16,901

,000

-8,013

,000

1 -,538

a. Dependent Variable: Behavioral Intention to Accept

Coefficients Model

a

Unstandardized Coefficients

Standardized

t

Sig.

Coefficients B

Std. Error

(Constant)

,955

,212

Privacy Concerns

,482

,059

Beta 4,508

,000

8,167

,000

1 ,545

a. Dependent Variable: Technology Anxiety

Coefficients Model

a

Unstandardized Coefficients

Standardized

t

Sig.

Coefficients B (Constant) 1

Technology Anxiety Privacy Concerns

Std. Error 7,107

,288

-1,109

,102

-,265

,090

Beta 24,686

,000

-,660

-10,889

,000

-,178

-2,941

,004

a. Dependent Variable: Behavioral Intention to Accept

Appendix 8.2 | - 78 -

Appendix 8.3 Moderation hypothesis 13b Iris scanning and facial recognition Model Summary Model

1 2

R

R

Adjusted R

Std. Error of

Square

Square

the Estimate

Change Statistics R Square

F

df1

Change

Change

df2

Sig. F Change

,487

a

,238

,226

,851

,238

20,876

2

134

,000

,493

b

,244

,226

,851

,006

1,047

1

133

,308

a. Predictors: (Constant), Age, Facilitating Conditions b. Predictors: (Constant), Age, Facilitating Conditions, H13bmoderator

a

ANOVA Model

1

Sum of Squares

Mean Square

F

Regression

30,262

2

15,131

Residual

97,126

134

,725

127,388

136

Regression

31,021

3

10,340

Residual

96,367

133

,725

127,388

136

Total

2

df

Total

Sig. b

20,876

,000

14,271

,000

c

a. Dependent Variable: Behavioral Intention to Accept b. Predictors: (Constant), Age, Facilitating Conditions c. Predictors: (Constant), Age, Facilitating Conditions, H13bmoderator

Coefficients Model

a

Unstandardized Coefficients

Standardized

t

Sig.

Coefficients B

1

Std. Error

(Constant)

,658

,325

Facilitating Conditions

,574

,093

Age

,065

,042

-,057

,770

Facilitating Conditions

,795

,235

Age

,258 -,059

(Constant)

Beta 2,026

,045

,467

6,178

,000

,118

1,565

,120

-,074

,941

,646

3,380

,001

,193

,468

1,337

,184

,058

-,409

-1,023

,308

2 H13bmoderator

a. Dependent Variable: Behavioral Intention to Accept

Appendix 8.4 | - 79 -

Fingerprint recognition Model Summary Model

1 2

R

R

Adjusted R

Std. Error of

Square

Square

the Estimate

Change Statistics R Square

F

df1

Change

Change

df2

Sig. F Change

,517

a

,267

,257

1,017

,267

26,955

2

148

,000

,531

b

,282

,268

1,010

,015

3,106

1

147

,080

a. Predictors: (Constant), Age, Facilitating Conditions b. Predictors: (Constant), Age, Facilitating Conditions, H13bmoderator

a

ANOVA Model

Sum of Squares Regression

1

Mean Square

F

55,774

2

27,887

Residual

153,115

148

1,035

Total

208,889

150

58,942

3

19,647

Residual

149,947

147

1,020

Total

208,889

150

Regression 2

df

Sig. b

26,955

,000

19,261

,000

c

a. Dependent Variable: Behavioral Intention to Accept b. Predictors: (Constant), Age, Facilitating Conditions c. Predictors: (Constant), Age, Facilitating Conditions, H13bmoderator

Coefficients Model

a

Unstandardized Coefficients

Standardized

t

Sig.

Coefficients B (Constant) 1

Std. Error -,215

,489

Facilitating Conditions

,901

,126

Age

,118

,046

-1,854

1,049

1,374

,296

,517 -,116

(Constant) Facilitating Conditions

Beta -,439

,662

,506

7,136

,000

,183

2,582

,011

-1,767

,079

,771

4,639

,000

,231

,802

2,239

,027

,066

-,655

-1,762

,080

2 Age H13bmoderator

a. Dependent Variable: Behavioral Intention to Accept

Appendix 8.4 | - 80 -

Appendix 8.4 Moderation hypothesis 14 Iris scanning and facial recognition 14a Model Summary Model

1 2

R

R

Adjusted R

Std. Error of

Square

Square

the Estimate

Change Statistics R Square

F

df1

Change

Change

df2

Sig. F Change

,180

a

,032

,018

,951

,032

2,214

2

133

,113

,181

b

,033

,011

,954

,001

,071

1

132

,790

a. Predictors: (Constant), Gender, Social Influence b. Predictors: (Constant), Gender, Social Influence, moderator14a

3

a

ANOVA Model

Sum of Squares Regression

1

Mean Square

4,004

2

2,002

Residual

120,228

133

,904

Total

124,231

135

4,068

3

1,356

Residual

120,163

132

,910

Total

124,231

135

Regression 2

df

F

Sig. b

2,214

,113

1,490

,220

c

a. Dependent Variable: Behavioral Intention to Accept b. Predictors: (Constant), Gender, Social Influence c. Predictors: (Constant), Gender, Social Influence, moderator14a

Coefficients Model

a

Unstandardized Coefficients

Standardized

t

Sig.

Coefficients B (Constant) 1

Std. Error 2,031

,383

,207

,104

Gender

-,091

,174

(Constant)

1,831

,843

Social Influence

,265

,243

Gender

,152 -,072

Social Influence

Beta 5,308

,000

,170

1,985

,049

-,045

-,521

,603

2,172

,032

,218

1,090

,278

,929

,075

,164

,870

,269

-,127

-,266

,790

2 moderator14a

a. Dependent Variable: Behavioral Intention to Accept

3

Moderator 14a = Social Influence * Gender

Appendix 8.4 | - 81 -

Iris scanning and facial recognition 14b Model Summary Model

R

R Square

Adjusted R

Std. Error of the

Square

Estimate

Change Statistics R Square

F Change

df1

d

Change 1 2

,267

a

,071

,058

,937

,071

5,181

2

,273

b

,075

,054

,938

,004

,509

1

a. Predictors: (Constant), Age, Social Influence b. Predictors: (Constant), Age, Social Influence, moderator14b

4

a

ANOVA Model

Sum of Squares Regression

1

Mean Square

9,088

2

4,544

Residual

118,413

135

,877

Total

127,501

137

9,536

3

3,179

Residual

117,965

134

,880

Total

127,501

137

Regression 2

df

F

Sig. b

5,181

,007

3,611

,015

c

a. Dependent Variable: Behavioral Intention to Accept b. Predictors: (Constant), Age, Social Influence c. Predictors: (Constant), Age, Social Influence, moderator14b

Coefficients Model

a

Unstandardized Coefficients

Standardized

t

Sig.

Coefficients B (Constant) 1

Std. Error 1,310

,434

Social Influence

,287

,105

Age

,112

,047

(Constant)

,743

,905

Social Influence

,458

,262

Age

,256 -,045

Beta 3,018

,003

,234

2,727

,007

,203

2,365

,019

,821

,413

,374

1,749

,083

,207

,464

1,235

,219

,063

-,268

-,713

,477

2 moderator14b

a. Dependent Variable: Behavioral Intention to Accept

4

Moderator 14b= Social Influence * Age

Appendix 8.4 | - 82 -

Iris scanning and facial recognition 14c Model Summary Model

1 2

R

R

Adjusted R

Std. Error of

Square

Square

the Estimate

Change Statistics R Square

F

df1

Change

Change

df2

Sig. F Change

,384

a

,148

,135

,914

,148

12,119

2

140

,000

,386

b

,149

,130

,917

,001

,170

1

139

,681

a. Predictors: (Constant), Experience, Social Influence b. Predictors: (Constant), Experience, Social Influence, moderator14c

5

a

ANOVA Model

Sum of Squares Regression

1

Mean Square

20,259

2

10,130

Residual

117,021

140

,836

Total

137,280

142

20,403

3

6,801

Residual

116,878

139

,841

Total

137,280

142

Regression 2

df

F

Sig. b

12,119

,000

8,088

,000

c

a. Dependent Variable: Behavioral Intention to Accept b. Predictors: (Constant), Experience, Social Influence c. Predictors: (Constant), Experience, Social Influence, moderator14c

Coefficients Model

a

Unstandardized Coefficients

Standardized

t

Sig.

Coefficients B

1

Std. Error

(Constant)

,809

,400

Social Influence

,345

,097

Experience

,366

,091

(Constant)

,490

,871

Social Influence

,439

,249

Experience

,522 -,047

Beta 2,025

,045

,281

3,542

,001

,320

4,024

,000

,563

,575

,359

1,762

,080

,389

,456

1,343

,182

,114

-,147

-,413

,681

2 moderator14c

a. Dependent Variable: Behavioral Intention to Accept

5

Moderator 14c = Social Influence * Experience

Appendix 8.4 | - 83 -

Fingerprint recognition 14a Model Summary Model

1 2

R

R

Adjusted R

Std. Error of

Square

Square

the Estimate

Change Statistics R Square

F

df1

Change

Change

df2

Sig. F Change

,045

a

,002

-,012

1,186

,002

,151

2

147

,860

,046

b

,002

-,018

1,190

,000

,009

1

146

,925

a. Predictors: (Constant), Gender, Social Influence b. Predictors: (Constant), Gender, Social Influence, H14moderator1

6

a

ANOVA Model

Sum of Squares Regression

1

Mean Square

,425

2

,213

Residual

206,780

147

1,407

Total

207,206

149

,438

3

,146

Residual

206,768

146

1,416

Total

207,206

149

Regression 2

df

F

Sig. b

,151

,860

,103

,958

c

a. Dependent Variable: Behavioral Intention to Accept b. Predictors: (Constant), Gender, Social Influence c. Predictors: (Constant), Gender, Social Influence, H14moderator1

Coefficients Model

a

Unstandardized Coefficients

Standardized

t

Sig.

Coefficients B (Constant) 1

Std. Error 3,078

,449

,072

,131

Gender

-,019

,204

(Constant)

3,019

,767

Social Influence

,090

,235

Gender

,067 -,027

Social Influence

Beta 6,857

,000

,045

,548

,584

-,008

-,093

,926

3,939

,000

,057

,384

,702

,940

,027

,071

,943

,284

-,039

-,094

,925

2 H14moderator1

a. Dependent Variable: Behavioral Intention to Accept

6

H14moderator1=Social Influence*Gender

Appendix 8.4 | - 84 -

Fingerprint recognition 14b Model Summary Model

1 2

R

R

Adjusted R

Std. Error of

Square

Square

the Estimate

Change Statistics R Square

F

df1

Change

Change

df2

Sig. F Change

,154

a

,024

,011

1,174

,024

1,798

2

148

,169

,176

b

,031

,011

1,173

,007

1,115

1

147

,293

a. Predictors: (Constant), Age, Social Influence b. Predictors: (Constant), Age, Social Influence, H14moderator2

7

a

ANOVA Model

Sum of Squares Regression

1

Mean Square

4,955

2

2,477

Residual

203,934

148

1,378

Total

208,889

150

6,490

3

2,163

Residual

202,399

147

1,377

Total

208,889

150

Regression 2

df

F

Sig. b

1,798

,169

1,571

,199

c

a. Dependent Variable: Behavioral Intention to Accept b. Predictors: (Constant), Age, Social Influence c. Predictors: (Constant), Age, Social Influence, H14moderator2

Coefficients Model

a

Unstandardized Coefficients

Standardized

t

Sig.

Coefficients B (Constant) 1

Std. Error 2,381

,570

Social Influence

,160

,137

Age

,101

,056

1,340

1,138

Social Influence

,467

,322

Age

,353 -,077

(Constant)

Beta 4,178

,000

,101

1,163

,247

,156

1,805

,073

1,177

,241

,294

1,451

,149

,246

,548

1,438

,153

,073

-,383

-1,056

,293

2 H14moderator2

a. Dependent Variable: Behavioral Intention to Accept

7

H14moderator2=Social Influence*Age

Appendix 8.4 | - 85 -

Fingerprint recognition 14c Model Summary Model

1 2

R

R

Adjusted R

Std. Error of

Square

Square

the Estimate

Change Statistics R Square

F

df1

Change

Change

df2

Sig. F Change

,208

a

,043

,031

1,174

,043

3,542

2

156

,031

,241

b

,058

,040

1,169

,015

2,390

1

155

,124

a. Predictors: (Constant), Experience, Social Influence b. Predictors: (Constant), Experience, Social Influence, H14moderator3

8

a

ANOVA Model

Sum of Squares Regression

1

Mean Square

9,771

2

4,886

Residual

215,176

156

1,379

Total

224,947

158

13,039

3

4,346

Residual

211,908

155

1,367

Total

224,947

158

Regression 2

df

F

Sig. b

3,542

,031

3,179

,026

c

a. Dependent Variable: Behavioral Intention to Accept b. Predictors: (Constant), Experience, Social Influence c. Predictors: (Constant), Experience, Social Influence, H14moderator3

Coefficients Model

a

Unstandardized Coefficients

Standardized

t

Sig.

Coefficients B (Constant) 1

Std. Error 2,309

,485

Social Influence

,122

,124

Experience

,280

,110

(Constant)

,858

1,055

Social Influence

,569

,314

Experience

1,033

H14moderator3

-,234

Beta 4,763

,000

,077

,985

,326

,200

2,549

,012

,813

,418

,360

1,811

,072

,499

,738

2,070

,040

,151

-,598

-1,546

,124

2

a. Dependent Variable: Behavioral Intention to Accept

8

H14moderator3= Social Influence*Experience

Appendix 8.4 | - 86 -

Appendix 9: T-tests between independent identification technologies Group Statistics Respondent survey iris scanning and facial BI

recognition survey fingerprint recognition survey iris scanning and facial

BR

recognition survey fingerprint recognition survey iris scanning and facial

PU

recognition survey fingerprint recognition survey iris scanning and facial

PEOU

recognition survey fingerprint recognition survey iris scanning and facial

RA

recognition survey fingerprint recognition survey iris scanning and facial

PC

recognition survey fingerprint recognition survey iris scanning and facial

TA

recognition survey fingerprint recognition survey iris scanning and facial

FC

recognition survey fingerprint recognition survey iris scanning and facial

Exp

recognition survey fingerprint recognition survey

N

Mean

Std. Deviation

Std. Error Mean

151

2,79

,984

,080

187

3,30

1,234

,090

148

2,60

1,055

,087

175

3,07

1,138

,086

145

2,78

,893

,074

171

3,65

,910

,070

145

3,59

,813

,068

170

4,08

,762

,058

145

2,66

,845

,070

171

3,58

,817

,062

144

3,81

,871

,073

160

3,52

,876

,069

143

3,05

,790

,066

160

2,65

,746

,059

143

3,08

,848

,071

160

3,43

,740

,059

143

1,92

,873

,073

160

1,93

,884

,070

Appendix 9 | - 87 -

Independent Samples Test Levene's Test for Equality of

t-test for Equality of Means

Variances F

Sig.

t

df

Sig. (2-tailed)

Mean

Std. Error

95% Confidence Interval of the

Difference

Difference

Difference Lower

Equal variances assumed BI

20,916

,000

Equal variances not assumed Equal variances assumed

BR

,233

,630

Equal variances not assumed Equal variances assumed

PU

,092

,762

Equal variances not assumed Equal variances assumed

PEOU

7,981

,005

Equal variances not assumed Equal variances assumed

RA

,073

,787

Equal variances not assumed Equal variances assumed

PC

Equal variances not assumed

1,454

,229

Upper

-4,139

336

,000

-,511

,124

-,754

-,268

-4,239

335,952

,000

-,511

,121

-,749

-,274

-3,802

321

,000

-,467

,123

-,709

-,225

-3,826

318,280

,000

-,467

,122

-,707

-,227

-8,598

314

,000

-,876

,102

-1,076

-,675

-8,610

307,291

,000

-,876

,102

-1,076

-,676

-5,520

313

,000

-,490

,089

-,665

-,316

-5,492

297,983

,000

-,490

,089

-,666

-,315

-9,922

314

,000

-,930

,094

-1,114

-,745

-9,895

302,082

,000

-,930

,094

-1,115

-,745

2,858

302

,005

,287

,100

,089

,484

2,859

298,971

,005

,287

,100

,089

,484

Appendix 9 | - 88 -

Levene's Test for Equality of

t-test for Equality of Means

Variances F

Sig.

t

df

Sig. (2-tailed)

Mean

Std. Error

95% Confidence Interval of the

Difference

Difference

Difference Lower

Equal variances assumed TA

2,010

,157

Equal variances not assumed Equal variances assumed

FC

,058

,811

Equal variances not assumed Equal variances assumed

Exp

Equal variances not assumed

,000

1,000

Upper

4,520

301

,000

,399

,088

,225

,573

4,505

292,505

,000

,399

,089

,225

,573

-3,815

301

,000

-,348

,091

-,528

-,169

-3,786

283,699

,000

-,348

,092

-,529

-,167

-,081

301

,936

-,008

,101

-,207

,191

-,081

298,022

,936

-,008

,101

-,207

,191

Appendix 9 | - 89 -

Appendix 10: General Linear Model: main & interaction effects demographic variables on different identification technologies Appendix 10.1 Demographic variables on BI of IS&FR and FP Gender Tests of Between-Subjects Effects Dependent Variable: BI Source

Type III Sum of

df

Mean Square

F

Sig.

Partial Eta

Squares

Squared a

3

8,872

7,304

,000

,072

2435,901

1

2435,901

2005,424

,000

,877

21,784

1

21,784

17,934

,000

,060

,629

1

,629

,518

,472

,002

,182

1

,182

,150

,699

,001

Error

342,533

282

1,215

Total

3083,000

286

369,150

285

Corrected Model

26,617

Intercept Respondent_survey

9

Gender Respondent_survey * Gender

Corrected Total

a. R Squared = ,072 (Adjusted R Squared = ,062)

Estimates Dependent Variable: BI Gender

Mean

Std. Error

95% Confidence Interval Lower Bound

Upper Bound

Male

3,131

,112

2,912

3,351

Female

3,032

,080

2,874

3,191

9

Respondent survey = regarding the iris scanning and facial recognition survey (1) or regarding the fingerprint survey (2

Appendix 10.1 | - 90 -

Age Tests of Between-Subjects Effects Dependent Variable: BI Source

Type III Sum of

df

Mean Square

F

Sig.

Squares

Partial Eta Squared

a

13

4,539

3,984

,000

,158

2063,497

1

2063,497

1811,153

,000

,868

Respondent_survey

13,425

1

13,425

11,784

,001

,041

Age

26,960

6

4,493

3,944

,001

,079

7,978

6

1,330

1,167

,324

,025

Error

313,315

275

1,139

Total

3107,000

289

372,325

288

Corrected Model

59,010

Intercept

Respondent_survey * Age

Corrected Total

a. R Squared = ,158 (Adjusted R Squared = ,119)

Estimates Dependent Variable: BI Age

Mean

Std. Error

95% Confidence Interval Lower Bound

Upper Bound

<18

3,563

,267

3,037

4,088

18-24

2,834

,110

2,618

3,050

25-34

2,506

,210

2,093

2,919

35-44

3,109

,162

2,790

3,429

45-54

3,462

,149

3,167

3,756

55-65

3,049

,183

2,688

3,410

>65

3,354

,234

2,894

3,814

Correlations BI Pearson Correlation BI

1

Sig. (2-tailed) N

Age

Age ,129

*

,028 338

289

*

1

Pearson Correlation

,129

Sig. (2-tailed)

,028

N

289

289

*. Correlation is significant at the 0.05 level (2-tailed).

Appendix 10.1 | - 91 -

Highest obtained education Tests of Between-Subjects Effects Dependent Variable: BI Source

Type III Sum of

df

Mean Square

F

Sig.

Squares

Partial Eta Squared

Corrected Model

35,925

a

11

3,266

2,689

,003

,096

Intercept

717,165

1

717,165

590,532

,000

,681

Respondent_survey

8,143

1

8,143

6,705

,010

,024

Highest Obtained Education

5,766

5

1,153

,950

,449

,017

4,487

5

,897

,739

,595

,013

Error

336,400

277

1,214

Total

3107,000

289

372,325

288

Respondent_survey * Highest Obtained Education

Corrected Total

a. R Squared = ,096 (Adjusted R Squared = ,061)

Estimates Dependent Variable: BI Highest Obtained Education

Mean

Std. Error

95% Confidence Interval Lower Bound

Upper Bound

Primary School

3,500

,675

2,172

4,828

Secondary School short-type

2,922

,217

2,494

3,350

Secondary School long-type

3,187

,117

2,957

3,417

Professional Bachelor

3,050

,115

2,823

3,276

Academic Bachelor

3,236

,189

2,863

3,609

Academic Master

2,828

,168

2,497

3,159

Appendix 10.1 | - 92 -

Social status Tests of Between-Subjects Effects Dependent Variable: BI Source

Type III Sum of

df

Mean Square

F

Sig.

Squares

Partial Eta Squared

Corrected Model

36,784

a

11

3,344

2,761

,002

,099

Intercept

625,235

1

625,235

516,151

,000

,651

Respondent_survey

4,891

1

4,891

4,038

,045

,014

Social Status

9,042

5

1,808

1,493

,192

,026

2,253

5

,451

,372

,868

,007

Error

335,542

277

1,211

Total

3107,000

289

372,325

288

Respondent_survey * Social Status

Corrected Total

a. R Squared = ,099 (Adjusted R Squared = ,063)

Estimates Dependent Variable: BI Social Status

Mean

Std. Error

95% Confidence Interval Lower Bound

Upper Bound

Single

3,029

,139

2,756

3,302

In a relationship

2,852

,138

2,580

3,124

Cohabiting

3,006

,220

2,573

3,440

Married

3,230

,099

3,036

3,424

Divorced

2,333

,502

1,344

3,322

Widow/Widower

3,167

,502

2,178

4,156

Appendix 10.1 | - 93 -

Profession Tests of Between-Subjects Effects Dependent Variable: BI Source

Type III Sum of

df

Mean Square

F

Sig.

Squares

Partial Eta Squared

a

15

3,633

3,121

,000

,147

1433,854

1

1433,854

1231,646

,000

,820

Respondent_survey

28,345

1

28,345

24,348

,000

,082

Profession

10,652

7

1,522

1,307

,247

,033

15,589

7

2,227

1,913

,068

,047

Error

315,492

271

1,164

Total

3099,000

287

369,993

286

Corrected Model

54,501

Intercept

Respondent_survey * Profession

Corrected Total

a. R Squared = ,147 (Adjusted R Squared = ,100)

Estimates Dependent Variable: BI Profession

Mean

Std. Error

95% Confidence Interval Lower Bound

Upper Bound

student

2,928

,108

2,716

3,140

unemployed/job seeker

2,571

,338

1,906

3,237

worker

2,750

,319

2,122

3,378

employee

3,088

,131

2,831

3,346

manager

3,414

,316

2,792

4,036

official

3,420

,251

2,927

3,914

self-employed

3,208

,225

2,766

3,651

retired

3,224

,183

2,864

3,584

Appendix 10.1 | - 94 -

Appendix 10.2 Main and interaction effect of age regarding independent variables Perceived Usefulness Tests of Between-Subjects Effects Dependent Variable: PU Source

Type III Sum of

df

Mean Square

F

Sig.

Squares

Partial Eta Squared

a

13

6,557

9,003

,000

,299

2255,752

1

2255,752

3097,142

,000

,918

Respondent_survey

36,932

1

36,932

50,708

,000

,156

Age

18,196

6

3,033

4,164

,001

,083

5,889

6

,982

1,348

,236

,029

Error

200,292

275

,728

Total

3356,000

289

285,536

288

Corrected Model

85,245

Intercept

Respondent_survey * Age

Corrected Total

a. R Squared = ,299 (Adjusted R Squared = ,265)

Estimates Dependent Variable: PU Age

Mean

Std. Error

95% Confidence Interval Lower Bound

Upper Bound

<18

3,813

,213

3,392

4,233

18-24

3,196

,088

3,023

3,368

25-34

2,738

,168

2,408

3,069

35-44

3,049

,130

2,794

3,305

45-54

3,541

,120

3,305

3,776

55-65

3,236

,147

2,947

3,525

>65

3,300

,187

2,932

3,668

Appendix 10.2 | - 95 -

Perceived Ease Of Use Tests of Between-Subjects Effects Dependent Variable: PEOU Source

Type III Sum of

df

Mean Square

F

Sig.

Squares

Partial Eta Squared

a

13

2,197

3,809

,000

,153

3137,497

1

3137,497

5440,314

,000

,952

Respondent_survey

8,973

1

8,973

15,559

,000

,054

Age

7,395

6

1,233

2,137

,049

,045

Respondent_survey * Age

2,838

6

,473

,820

,555

,018

Error

158,019

274

,577

Total

4457,000

288

186,580

287

Corrected Model

28,561

Intercept

Corrected Total

a. R Squared = ,153 (Adjusted R Squared = ,113)

Estimates Dependent Variable: PEOU Age

Mean

Std. Error

95% Confidence Interval Lower Bound

Upper Bound

<18

4,063

,190

3,689

4,436

18-24

3,751

,078

3,597

3,904

25-34

3,780

,149

3,486

4,074

35-44

3,664

,116

3,437

3,892

45-54

4,133

,106

3,924

4,342

55-65

3,806

,130

3,549

4,062

>65

3,848

,168

3,517

4,180

Appendix 10.2 | - 96 -

Relative Advantage Tests of Between-Subjects Effects Dependent Variable: RA Source

Type III Sum of

df

Mean Square

F

Sig.

Squares

Partial Eta Squared

a

13

7,151

11,499

,000

,352

2133,176

1

2133,176

3429,986

,000

,926

Respondent_survey

40,781

1

40,781

65,573

,000

,193

Age

23,655

6

3,943

6,339

,000

,122

3,520

6

,587

,943

,464

,020

Error

171,028

275

,622

Total

3142,000

289

263,993

288

Corrected Model

92,965

Intercept

Respondent_survey * Age

Corrected Total

a. R Squared = ,352 (Adjusted R Squared = ,322)

Estimates Dependent Variable: RA Age

Mean

Std. Error

95% Confidence Interval Lower Bound

Upper Bound

<18

3,500

,197

3,112

3,888

18-24

2,963

,081

2,804

3,122

25-34

2,524

,155

2,218

2,829

35-44

3,117

,120

2,881

3,353

45-54

3,500

,110

3,283

3,717

55-65

3,181

,135

2,914

3,447

>65

3,458

,173

3,118

3,798

Appendix 10.2 | - 97 -

Privacy Concerns Tests of Between-Subjects Effects Dependent Variable: PC Source

Type III Sum of

df

Mean Square

F

Sig.

Squares

Partial Eta Squared

a

13

1,875

2,580

,002

,109

2792,891

1

2792,891

3841,958

,000

,933

3,114

1

3,114

4,283

,039

,015

14,651

6

2,442

3,359

,003

,068

5,254

6

,876

1,205

,304

,026

Error

199,910

275

,727

Total

4061,000

289

224,291

288

Corrected Model

24,381

Intercept Respondent_survey Age Respondent_survey * Age

Corrected Total

a. R Squared = ,109 (Adjusted R Squared = ,067)

Estimates Dependent Variable: PC Age

Mean

Std. Error

95% Confidence Interval Lower Bound

Upper Bound

<18

3,500

,213

3,080

3,920

18-24

3,630

,087

3,458

3,803

25-34

4,155

,168

3,825

4,485

35-44

3,700

,130

3,445

3,955

45-54

3,552

,119

3,317

3,787

55-65

3,809

,146

3,521

4,097

>65

3,104

,187

2,737

3,472

Appendix 10.2 | - 98 -

Technology Anxiety Tests of Between-Subjects Effects Dependent Variable: TA Source

Type III Sum of

df

Mean Square

F

Sig.

Squares

Partial Eta Squared

a

13

1,850

3,397

,000

,139

1642,492

1

1642,492

3016,418

,000

,917

Respondent_survey

8,755

1

8,755

16,079

,000

,055

Age

6,715

6

1,119

2,055

,059

,043

Respondent_survey * Age

3,450

6

,575

1,056

,389

,023

Error

149,198

274

,545

Total

2457,000

288

173,247

287

Corrected Model

24,049

Intercept

Corrected Total

a. R Squared = ,139 (Adjusted R Squared = ,098)

Estimates Dependent Variable: TA Age

Mean

Std. Error

95% Confidence Interval Lower Bound

Upper Bound

<18

2,438

,184

2,074

2,801

18-24

2,904

,076

2,755

3,053

25-34

3,147

,148

2,857

3,438

35-44

2,753

,112

2,532

2,974

45-54

2,692

,103

2,489

2,896

55-65

2,833

,127

2,584

3,083

>65

2,808

,162

2,490

3,126

Appendix 10.2 | - 99 -

Facilitating Conditions Tests of Between-Subjects Effects Dependent Variable: FC Source

Type III Sum of

df

Mean Square

F

Sig.

Partial Eta

Squares

Squared a

13

1,572

2,670

,002

,112

2303,018

1

2303,018

3911,152

,000

,935

Respondent_survey

2,443

1

2,443

4,148

,043

,015

Age

8,582

6

1,430

2,429

,026

,051

Respondent_survey * Age

4,266

6

,711

1,207

,303

,026

Error

161,340

274

,589

Total

3276,000

288

181,778

287

Corrected Model

20,437

Intercept

Corrected Total

a. R Squared = ,112 (Adjusted R Squared = ,070)

Estimates Dependent Variable: FC Age

Mean

Std. Error

95% Confidence Interval Lower Bound

Upper Bound

<18

3,812

,192

3,435

4,190

18-24

3,276

,079

3,121

3,431

25-34

2,923

,154

2,621

3,225

35-44

3,200

,117

2,970

3,430

45-54

3,329

,107

3,118

3,541

55-65

3,226

,132

2,966

3,485

>65

3,412

,168

3,082

3,743

Appendix 10.3 Correlations age – independent variables Correlations Age Pearson Correlation Age

PU 1

Sig. (2-tailed) N

289

PEOU

RA

PC

TA

FC

**

-,068

-,032

-,027

,248

,003

,247

,589

,645

288

289

289

288

288

,048

,068

,415 289

,172

**. Correlation is significant at the 0.01 level (2-tailed).

Appendix 10.2 | - 100 -