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).
II
Ondanks dat het onderzoek veel hypothesen en veel variabel bevat, is ze in bepaalde opzichten
limitatief en zijn er mogelijke suggesties voor toekomstig onderzoek. Bijvoorbeeld, het
onderzoek is uitgevoerd in België, maar misschien bestaan er andere coëfficiënten voor andere
landen of culturen. Een andere limitatie is dat het voor respondenten moeilijk is om zich iets
voor te stellen dat ze nog nooit eerder hebben meegemaakt in praktijk. Men zou de hele studie
kunnen herdoen, maar onder praktische omstandigheden. Ook focus groepen, laboratoria en
experimenten zullen voor de retailer meer inzicht bieden dan een enquête.
In het algemeen geeft de studie een goed inzicht in de behavioral intention van consumenten om
identificatie technologieën te accepteren. Ze is gebaseerd op verschillende leeftijdscategorieën,
op een dagelijkse omgeving en op verschillende biometrics. Retailers begrijpen hierdoor meer en
beter de achterliggende redenen van consumenten om biometric systemen te accepteren (of te
weigeren). Maar indien het implementatie proces van zulke technologieën niet met
voorzichtigheid en niet doordacht wordt uitgevoerd, zal deze technologie als bedreigend worden
opgevat. Dit zou zeer teleurstellend zijn, voornamelijk voor een technologie die zoveel te bieden
heeft in de toekomst.
III
Acknowledgments
This dissertation would not have been as it is now without the help of certain people. First of all,
I like to thank the thirty respondents, who filled in the pretest and placed critical, but useful
recommendations to form the final survey. Next, I would like to thank my parents, sister, friends
and family who helped me distributing the survey to as many age categories as possible, both
online and offline. I would also like to thank the readers of this final dissertation, for their time,
input and critical view. Finally, I would like to thank my supervisor, Prof. Dr. Patrick Van
Kenhove. He was honestly interested in the topic and helped me through the set-up of this final
dissertation.
IV
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 -
VIII
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
IX
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
X
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-
XI
Chapter I: Introduction
There has been a lot of research regarding identification technology or biometrics. The existing
research deals primarily with the systems of biometrics, the advantages and disadvantages, and
looks at it particularly from a corporate point of view, rather than from a consumer’s perspective.
But the latter is equally important. The increasing identification technologies used in other
industries and sectors make the question ‘Why will consumers accept or reject identification
technologies?’ more and more important. Furthermore, the arise of these type of technologies is
not something that is happening miles and miles away from the consumer’s daily live. On the
contrary, it moves closer! It is already known that people accept these technologies when they
are mandatory and when they are used in a logical situation (Casinos, airports, border
controls…), but are people also willing to accept them in a voluntary situation, for instance: at
their department store? Piggly Wiggly and Wall Mart (US) are already testing these applications,
but it is important to know for retailers (and managers) how they should approach consumers to
facilitate and enhance their acceptance of biometric technologies.
This dissertation discusses the acceptance of identification technology by consumers, specifically
in a retail setting. It investigates the acceptance of three well-known types: fingerprint
recognition, iris scanning and facial recognition. The first one is investigated in a ‘check-out’
situation: pay by touch. The two other, iris scanning and facial recognition are addressed from a
marketing perspective: at the point-of-sale (in the store). First, we investigate the biometric
applications separately to see which factors influence the consumers’ choice to accepting or
rejecting them. Next, we examine if there is a difference in how consumers perceive the different
biometrics systems: do they see them as similar technologies or do they have different opinions
about each biometric technology?
The purpose and the research of this dissertation is therefore twofold. First, it offers a theoretical
framework, containing factors related to technology acceptance, discovered by other researchers
and by ourselves. Secondly, it gives a pragmatic answer (for retailers) to the question which
factors have an important impact on the consumer’s acceptance of identification technology in a
retail setting. We offer a solution by formulating several hypotheses regarding the dependent
variable (the behavioral intention of consumers to accept identification technology) and test them
with an appropriate survey and SPSS analysis. An answer on this is also provided based on the
results of the survey, but is more pragmatically discussed in the conclusion.
1
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.
2
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
4
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.
6
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.
8
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.
9
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).
12
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).
13
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).
14
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.
16
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.
18
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
19
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.
20
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.
21
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
22
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).
24
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
References
Aggarwal, P. and Cha, T. (1997). Surrogate buyers and the new product adoption process: a
conceptualization and managerial framework. Journal of Consumer Marketing, 14(5), 391-400.
Ajzen, I. and Fishbein, M., Understanding attitudes and predicting social behavior, Prentice Hall,
Englewood cliffs, 1980, 278 pages.
Alterman, A. (2003). “A piece of yourself”: Ethical issues in biometric identification. Ethics and
Information Technology, 5, 139-150.
Baron, R.M. and Kenny, D.A. (1986). The Moderator-Mediator Variable Distinction in Social
Psychological Research: Conceptual, Strategic, and Statistical Considerations. Journal of Personality
and Social Psychology, 51 (6), 1173 – 1182.
Barton, B., Byciuk, S., Harris, C., Schumack, D. and Webster, K. (2005). The emerging cyber risks of
biometrics. Risk Management, 52(10), 26-28, 30-31.
Bitner, M.J., Brown, S.W. and Meuter, M.L. (2000). Technology Infusion in Service Encounters. Journal
of the Academy of Marketing Science, 28, 138-149.
Brass, L. (2003). Firms Dealing with Fingerprint Technology See Growing Demand. Knight
Ridder Tribune Business News, December 1.
Brownstein, R. (2004). E-commerce Becomes Mainstream amidst Security Concerns. Electronic
Design, 52(13), 75-76.
Bruner II, G.C., James, K.E. and Hensel, P.J., Marketing Scales Handbook, Volume III: A
Compilation of Multi-Item Measures (Marketing Sales Handbooks), South-Western Educational Pub,
2001, 1651 pages.
Buckley, B. and Hunter, M. (2011). Say cheese! Privacy and facial recognition. Computer Law &
Security Review, 27, 637-640.
Carrigan, M. and Attalla, A. (2001). The myth of the ethical consumer - do ethics matter in purchase
Behavior? Journal of Consumer Marketing, 18(7), 560 - 577.
Clodfelter, R. (2010). Biometric technology in retailing: Will consumers accept fingerprint
authentication? Journal of Retailing and Consumer Services, 17, 181-188.
Czegledy, N. and Czegledy, A. P. (2002). The Body as Password: Biometrics and Corporeal
Dispossession. Filozofski Vestnik, 23(2): 75-92.
Davies, S. G. (1994). Touching Big Brother: How Biometric Technology Will Fuse Flesh and Machine.
Information Technology & People, 7(4), 38-47.
Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1989). User Acceptance of Computer Technology: A
Comparison of Two Theoretical Models. Management Science , 35(8), 982-1003.
I
Davis, M.A., Andersen, M.G. and Curtis, M.B. (2001). Measuring Ethical Ideology in Business
Ethics : a Critical Analysis of the Ethics Position Questionnaire. Journal of Business Ethics, 32, 35-53.
De Pelsmacker, P. and Van Kenhove, P., Marktonderzoek: methoden en toepassingen,
Pearson Benelux B.V., Amsterdam, 2010, 568 pages.
Dictionary.com. (2014) URL: < http://dictionary.reference.com/browse/biometrics>.
(17/10/2013).
DigitalPersona. (2009). Combating Retail/Restaurant Fraud with Fingerprint Biometrics,
whitepaper, URL: <http://www.digitalpersona.com/Resources/White-Papers/>. (02/02/2014).
FindBIOMETRICS. (2014). Consumer/Residential Biometrics. URL:
<http://findbiometrics.com/applications/consumerresidential-biometrics/>. (13/11/2013)
FindBIOMETRICS, (2014). Iris Scanners & Recognition, URL:
<http://findbiometrics.com/solutions/iris-scanners-recognition/>. (13/11/2013).
Fishben, M. and Ajzen, I. Belief, attitude, intention and behavior: An Introduction to theory and
Research, Addison-Wesley, Reading, MA, 1975, 578 pages.
Fogel, J. and Nehmad, E. (2009). Internet social network communities: risk taking, trust, and privacy
concerns. Computers in Human Behavior, 25(1), 153-160.
Forsyth, D.R. (1980). A Taxonomy of Ethical Ideologies. Journal of Personality and Social
Psychology, 39 (1), 175-184.
Forsyth, D.R. and Berger, R.E. (1982). The effects of Ethical Ideology on Moral Behavior. The
Journal of Social Psychology, 117, 53-56.
Gelderman, C. J., Ghijsen, P.W.Th. and Van Diemen, R. (2011). Choosing self-service technologies or
interpersonal services—The impact of situational factors and technology-related attitudes. Journal of
Retailing and Consumer Services, 18, 414-421.
Gold, S. (2012). Biometrics at the ATM - the need for customer authentication. Biometric
Technology Today, June 2012(6), 7-10.
Hausman, A. and Stock J. R. (2003). Adoption and implementation of technological innovations within
long-term relationships. Journal of Business Research, 56, 681-686.
Hozanne, C. (2012). The future of biometrics in retail. Biometric Technology Today, June
2012(6), 5-7
Human Recognition Systems. (2014). Fingerprint Recognition, URL:
< http://www.hrsid.com/company/technology/finger-recognition>. (07/05/2014).
Im, S., Bayus, B.L. and Mason, C.H. (2003). An Empirical Study of Innate Consumer Innovativeness,
Personal Characteristics, and New-Product Adoption Behavior. Journal of the Academy of Marketing
Science, 31, 61-73.
Janssens, W., Wijnen, K., De Pelsmacker, P. and Van Kenhove, P., Marketing Research with SPSS,
Pearson education limited, Essex, 2008, 441 pages.
II
Jones, P., Williams, P., Hillier, D. and Comfort, D. (2007). Biometrics in retailing. International Journal
of Retail & Distribution Management, 35(3), 217-222.
Karahanna, E., Straub D.W. and Chervany, N.L. (1999). Information Technology Adoption Across Time:
A Cross-Sectional Comparison of Pre-Adoption and Post-Adoption Beliefs. MIS Quarterly, 23(2),
183-213.
Kimaldi Electronics. (2014). Payment solution through biometric fingerprint system. URL:
<http://www.kimaldi.com/kimaldi_eng/sectors/hotels_and_catering/payment_solution_through_biome
tric_fingerprint_system>. (07/05/2014).
Kulviwat, S., Bruner II, G.C. and Al-Shuridah, O. (2009). The role of social influence on adoption of
high tech innovations: The moderating effect of public/private consumption. Journal of Business
Research, 62, 706-712.
Kulviwat, S., et al. (2007). Toward a Unified Theory of Consumer Acceptance Technology. Psychology
&Marketing, 24(12), 1059-1084.
Langenderfer, J. and Linnhoff, S. (2005). The Emergence of Biometrics and Its Effect on Consumers.
The Journal of Consumer Affairs, 39(2), 314-338.
Lawson, W. J. (2003). Enhancing assistive technologies: through the theoretical adaptation of biometric
technologies to people of variable abilities. Dissertation, School of Business Kennedy-Western
University, 205 pages.
Lee, Y., Lee J. and Lee Z. (2006). Social Influence on Technology Behavior: Self-identity Theory
Perspective. Data Base for Advances in Information Systems, 37(2 & 3), 60-75.
Leroux, M. (2012, November 24), Warning, She’s watching you: the mannequin who spies. The Times,
URL: <http://www.thetimes.co.uk/tto/news/uk/article3610466.ece>. (13/11/2013).
Liljander, V., Gillberg, F., Gummerus, J. and Van Riel, A. (2006). Technology readiness and the
evaluation and adoption of self-service technologies. Journal of Retailing and Consumer Services, 13,
177-191.
Lin, C., Shih, H. and Sher, P.J. (2007). Integrating Technology Readiness into Technology Acceptance:
The TRAM Model. Psychology and Marketing, 24(7), 641-657.
Lindeman, M. and Verkasalo, M. (2005). Measuring Values with the Short Schwartz’s Value
Survey. Journal of Personality Assessment, 85 (2), 170–178.
Manning, K. C., Bearden, W.O. and Madden T.J. (1995). Consumer Innovativeness and the Adoption
Process. Journal of Consumer Psychology, 4(4), 329-345.
Marieclaire.com. (2013). How Do You Shop? A Technology May Already Know. Hearst
Communication, Inc., URL:
< http://www.marieclaire.com/blog/facial-recognition-shoppingtechnology>. (20/03/2014).
Mehrabian, A., and Russell, J.A., An approach to environmental psychology, MIT Press, Cambridge,
1974, 266 pages.
III
Mennecke, B. and Peters, A. (2013). Avatars to Mavatars: The Role of Marketing Avatars and Embodied
Representations in Consumer Profiling. Business Horizons, 26 pages.
Middlemiss, J., (2004, March 26), Biometrics Add Security in Insecure Times, Wall Street & Technology,
URL: <www.wallstreettech.com/electronic-trading/biometrics-add-security-in-insecure
times/18402883/.>. (13/11/2013).
Miltgen, C., Popovič A. and Oliveira, T. (2013). Determinants of end-user acceptance of biometrics:
Integrating the “Big 3” of technology acceptance with privacy context. Decision Support Systems, 56,
103-114.
Mindfully.org. (2005). Piggly Wiggly Fingerprint Scanners. URL:
<http://www.mindfully.org/Technology/2005/Piggly-Wiggly-Fingerprint11feb05.htm>. (13/11/2013).
Moore, G.C. and Benbasat, I. (1991). Development of an Instrument to Measure the Perceptions of
Adopting an Information Technology Innovation. Information Systems Research, 2(3), 191-222.
Norton, J.A. and Bass, F.M. (1987). A Diffusion Theory Model of Adoption and Substitution for
Successive Generations of High-Technology Products. Management Science, 33(9), 1069-1086.
Ozaki, R. and Dodgson, M. (2010). Adopting and Consuming innovations. Prometheus, 28 (4), 311-326.
Parasuraman, A. (2000). Technology Readiness Index (Tri): A Multiple-Item Scale to Measure Readiness
to Embrace New Technologies. Journal of Service Research, 2, 307-320.
Pavlou, P.A. (2003). Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the
Technology Acceptance Model. International Journal of Electronic Commerce, 7(3), 101-134.
Planet Biometrics. (2013). Tesco offers up personalized ads based on facial indicators. URL:
<http://www.planetbiometrics.com/article-details/i/1783/> (12/12/2013).
Price, L.L. and Arnould, E.J. (1999). Commercial Friendships: Service Provider – Client
Relationships in Context. Journal of Marketing, 63, 38-56.
Robinson, L. Jr. (2006). Moving Beyond Adoption: Exploring the Determinants of Student Intention to
Use Technology. Marketing Education Review, 16(2), 79-88.
Rogers, E.M., Diffusion of Innovations, 4th edition, The Free Press, New York, 1995, 518 pages.
Schepers, J. and Wetzels, M. (2007). A meta-analysis of the technology acceptance model:
Investigating subjective norm and moderation effects. Information and Management, 44, 90-103.
Schwartz, S.H. (1992). Universals in the Content and Structure of Values: Theoretical Advances
and Empirical Tests in 20 countries. Advances in Experimental Social Psychology, 25, 1–65.
Schwartz, S.H. (1994). Are there Universal Aspects in the Structure and Contents of Human Values?
Journal of Social Issues, 50(1), 19-45.
Selling biometrics to the retail sector. (2003). Biometric Technology Today, 11(6), 9-11.
Steenhaut, S. and Van Kenhove, P. (2006) An Empirical Investigation of the Relationships among
a Consumer’s Personal Values, Ethical Ideology and Ethical Beliefs. Journal of Business Ethics, 64,
137-155.
IV
Straub, E.T. (2009), Understanding Technology Adoption: Theory and Future Directions for Informal
Learning. Review of Educational Research, 79 (2), 625-649.
Trader, J. (2010). The Top 15 Reasons To Use Biometric Technology In Workforce
Management
And
Retail
Point
Of
Sale,
blog,
URL:
<http://blog.m2sys.com/workforce-
management/blog-series-%E2%80%93-the-top-15-reasons-to-use-biometric-technology-in-workforcemanagement-and-retail-point-of-service/> (15/02/2014).
Trocchia, P. J. and Ainscough T. L. (2006). Characterizing consumer concerns about identification
technology. International Journal of Retail & Distribution Management, 34(8), 609-620.
Tse, A.C.B. (1999). Factors affecting consumer perceptions on product safety. European Journal of
Marketing, 33(9/10), 911-925.
Van Kenhove, P., Vermeir, I. and Verniers, S. (2001). An Empirical Investigation of the Relationships
between Ethical Beliefs, Ethical Ideology, Political Preference and Need for Closure. Journal of
Business Ethics, 32, 347-361.
Venkatesh, V. and Davis. F.D. (2000). A Theoretical Extension of the Technology Acceptance Model:
Four Longitudinal Field Studies. Management Science, 46(2), 186-204.
Venkatesh, V. and Morris. M.G. (2000). Why Don't Men Ever Stop to Ask for Directions? Gender, Social
Influence, and Their Role in Technology Acceptance and Usage Behavior. MIS Quarterly, 24(1), 115139.
Venkatesh, V., Morris, M.G., Davis, G.B. and Davis, F.D. (2003). User Acceptance of Information
Technology: Toward a Unified View. MISQuarterly, 27(3), 425-478.
Vitell, S.J. (2003) Consumer Ethics Research: Review, Synthesis and Suggestions for the Future.
Journal of Business Ethics, 43, 33-47.
Walker, R., Craig-Lees, M., Hecker, R. and Francis, H. (2002). Technology-enabled service delivery: An
investigation of reasons affecting customer adoption and rejection. International Journal of Service
Industry Management, 13(1), 91-106.
Weinzierl, P. (2010). The body as password. Biometric Technology Today, June 2010(6), 6-8.
Ybarra, M. (2005). Grocer bags a Biometric System. URL:
<http://searchcio.techtarget.com/magazineContent/Grocer-Bags-a-Biometric-System> (13/11/2013).
Yi, M.Y., Jackson, J.D., Park, J.S. and Probst, J.C. (2006). Understanding information
technology acceptance by individual professionals: Toward an integrative view. Information &
Management, 43(3), 350-363.
Zhang, W. and Gutierrez, O. (2007). Information Technology Acceptance in the Social Services Sector
Context: An Exploration. Social Work, 52(3), 221-231.
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,
<http://www.mindfully.org/Technology/2005/Piggly-WigglyFingerprint11feb05.htm>.
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,
<http://www.mindfully.org/Technology/2005/Piggly-WigglyFingerprint11feb05.htm>.
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 -

VK GROUP, opgericht in 1952, is een toonaangevend Belgisch

Name Affiliation Albert van Dijk Australian National University

Ernst ten Heuvelhof - TU Delft Medewerkers

NL - BusinessForensics

Teamindeling MB

Samenvatting Summary

Caddy CUP leaflet 04 2014.indd

Appendix D Members of the Medicines Evaluation Board and

Acer Aspire E1-510-28202G32Dnkk (NX.MGREH.007)