exploring trust of mobile applications based on user behaviors: an empirical study

Exploring trust of mobile applications based on userbehaviors: an empirical studyZheng Yan1,4, Yan Dong2, Valtteri Niemi3, Guoliang Yu2

1The State Key Lab of ISN, Xidian University2Institute of Psychology, Renmin University of China3Department of Mathematics and Statistics, University of Turku4Department of Communications and Networking, Aalto University

Correspondence concerning this article shouldbe addressed to Zheng Yan, The State KeyLaboratory of ISN, Xidian University, Box 119,No. 2 South Taibai Road, 710071 Xi’an, China.E-mail: [email protected]

Author NotesZheng Yan is a computer scientist with aninterest in trust and security; she is a professorin XiDian University, China, and a docent inthe Aalto University, Finland; she received herPhD in Electrical Engineering from HelsinkiUniversity of Technology. Yan Dong is apsychological researcher with an interest inacademic emotions, mental health, andinterpersonal trust; she is a lecturer in theInstitute of Psychology, Renmin University ofChina; she received her PhD in psychology fromChinese Academy of Sciences. Valtteri Niemi isa professor in University of Turku, Finland,doing privacy and security research. Hereceived PhD in Mathematics from University ofTurku in 1989. Guoliang Yu is a psychologistwith an interest in psychology of developmentand education, and personality and socialdevelopment of adolescent; he is a professor inthe Institute of Psychology, Renmin Universityof China; he received his PhD in Psychologyfrom Beijing Normal University.

doi: 10.1111/j.1559-1816.2013.01044.x

Abstract

This paper explores trust of mobile applications based on users’ behaviors. It pro-poses a trust behavior construct through principal component analysis, reliabilityanalysis, and confirmatory factor analysis based on the data collected from a ques-tionnaire survey with more than 1,500 participants. It is indicated that a user’s trustbehavior is composed of three principal constructs: using behavior, reflectionbehavior, and correlation behavior. They are further delineated into 12 measurablesub-constructs and relate to a number of external factors. The data analysis showedthat the questionnaire has positive psychometric properties with respect to con-struct validity and reliability. We also discuss the practical significance and limita-tions of our work toward usable trust management.

Introduction

Mobile device has evolved into an open platform capable ofexecuting various applications. A mobile application is a soft-ware package that can be installed and executed in the mobiledevice, for example, a mobile e-mail client to access e-mailsvia a mobile phone. Generally, mobile applications developedby various vendors can be downloaded for installation.

Whether a mobile application is trustworthy for a user to pur-chase, download, install, consume, or recommend becomes acrucial issue that impacts its final success.

Trust is a multidimensional, multidisciplinary, and multi-faceted concept. The concept of trust has been studied indisciplines ranging from economics to psychology, fromsociology to medicine, and to information and computerscience. Trust has been defined by researchers in many

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Journal of Applied Social Psychology 2013, 43, pp. 638–659

© 2013 Wiley Periodicals, Inc. Journal of Applied Social Psychology 2013, 43, pp. 638–659

different ways. Common to many definitions of trust are thenotions of confidence, belief, expectation, dependence, andreliance on the goodness, reliability, integrity, ability, or char-acter of an entity (i.e., trustee) (Yan, 2007). Herein, we definea user’s trust in a mobile application as his/her belief onwhether the application could fulfill a task as expected.The trustworthiness of mobile applications relates to theirdependability, security, and usability (Avizienis, Laprie,Randell, & Landwehr, 2004).

A user’s trust in a mobile application is highly subjectiveand inherently hard to measure (Antifakos, Kern, Schiele, &Schwaninger, 2005). It is built up over time and changes withthe use of the application due to the influence of manyfactors.As it is seen as an internal“state”of the user, there is noway of measuring it directly. However, trust is an importantfactor that impacts usage. In the literature, numerousresearchers have found a positive correlation between trustand use (Lee & Moray, 1992; Muir, 1994; Muir & Moray, 1996;Sheridan, 1980). Trust is important because it helps consum-ers overcome perceptions of uncertainty and risk and engagein“trust-related behaviors”(Lewis & Weigert, 1985); in short,trust behavior. Trust behaviors are a trusting subject’s actionsto depend on or make him/her vulnerable to a trusting object(McKnight, Choudhury, & Kacmar, 2002). Examples areusing the application regularly to fulfill a routine task, or con-tinuing to consume the application even while facing someerrors.After the trusting subject (i.e., trustor) and the trustingobject (i.e., trustee) have interacted for some time, credibleinformation has been gleaned.

One issue that contributes to whether the users would liketo use a new product is how much they trust it. Muir is one ofthe first researchers to look at a decision process betweensupervisors and automated systems. Her study found a posi-tive correlation between trust and use (Muir & Moray, 1996).Lee and Moray (1992) found that trust in a system partiallyexplained system use. McKnight et al. (2002) also proposedthat a consumer’s “subjective probability of depending” (i.e.,the perceived likelihood that one will depend on the other)involves the projected intention to engage in some specificbehaviors.

Marsh (1994) reasoned that it might be more appropriateto model trust behavior rather than trust itself, removing theneed to adhere to specific definitions. Furthermore, modelingtrust behavior overcomes the challenges of measuring a sub-jective concept. Instead, measuring may be done throughobjective behavior observation, which provides a concreteclue of trust. Regarding mobile application usage, we positthat credible information is gained only after a mobile userhas engaged in trust behaviors (e.g., acting on using a mobileapplication) and assessed the trustworthiness of the applica-tion by observing the consequences of its performance andrelying on it in his/her routine life. The goal of our research isto study what interaction behaviors are related to the user’s

trust in a mobile application. We hypothesize that theuser’s trust in a mobile application can be studied throughthe user’s behaviors, which can be monitored via the user–device interaction during the application usage.

However, few existing trust models explore trust in theview of human behaviors (Grabner-Kräuter & Kaluscha,2003; Yan, 2010; Yan & Holtmanns, 2008). Some studies focuson human’s trust in an automatic and intelligent machine(Lee & Moray, 1992; Muir, 1994; Muir & Moray, 1996). Anumber of trust models have been proposed in the context ofe-commerce (Grabner-Kräuter & Kaluscha, 2003; McKnightet al., 2002) while little work has been done in the context ofmobile applications. Numerous researchers have conceptual-ized trust as a behavior which has been validated in thecontext of work collaboration and social communications(Anderson & Narus, 1990; Deutsch, 1973; Fox, 1974). Priorresearch has also confirmed a strong correlation betweenbehavioral intentions and actual behavior, especially for soft-ware system usage (Sheppard, Hartwick, & Warshaw, 1988;Venkatesh & Davis, 2000). However, still very few studiesexamined trust from the view of trust behaviors. Some worksstudy the trust behavior in e-banking (Grabner-Kräuter &Kaluscha, 2003). To our knowledge, no existing work explorestrust behavior of mobile application usage, which is a differ-ent context from the above research domains as regards appli-cation execution environment and user interface, as well asusage experiences.

In the work presented in this paper, we first review the lit-erature of trust model and trust behavior study. Then, wepropose a trust behavior research model based upon theo-retic justification and practical experiences. We continueexploring and confirming the construct of trust behavior formobile application usage through a large-scale user survey.Furthermore, the findings and implications, as well as the sig-nificance and limitations of current empirical study, are dis-cussed. Finally, conclusions and future work are presented inthe last section.

Literature background

Trust model

The method to specify, evaluate, set up, and ensure trust rela-tionships among entities is referred to as a trust model. Trustmodeling is the technical approach used to represent trust.One of the earliest formalizations of trust in computingsystems was done by Marsh (1994). He integrated the variousfacets of trust from the disciplines of economics, psychology,philosophy, and sociology. Since then, many trust modelshave been constructed for various computing paradigmsincluding ubiquitous computing, distributed systems(e.g., peer-to-peer systems, ad hoc networks, GRID virtual

Yan et al. 639


organizations), multi-agent systems, Web services, e-commerce (e.g., Internet services), and component software(Yan & Holtmanns, 2008). In almost all of these studies, trustis accepted as a subjective notion, thus bringing us to aproblem of how to measure trust. Translation of this subjec-tive concept into a machine readable language is the mainobjective of trust modeling.

In information and computer science, a trust model aimsto process and/or control trust digitally. Most of the trustmodels are designed based on the understanding of trustcharacteristics (e.g., subjective and dynamic) by consideringthe factors that influence trust. Current work covers a widearea, mostly in distributed systems and e-commerce. Acommon approach that can be found in the literature is com-putational trust, e.g., Song, Hwang, Zhou, and Kwok (2005);Sun, Yu, Han, and Liu (2006); Theodorakopoulos and Baras(2006); and Xiong and Liu (2004). Although a variety of trustmodels have been proposed, it is still not well understoodwhat fundamental criteria trust models must follow. Withouta good answer to this question, the design of trust models isstill at an empirical stage (Sun et al., 2006). Current workfocuses on concrete solutions in specific systems.

One promising approach of trust modeling aims to con-ceptualize trust based on user studies through a psychologi-cal or sociological approach (e.g., using a measurementscale). This kind of research aims to prove the complicatedrelationships among trust and other multiple factors indifferent facets. Two typical examples are the initial trustmodel proposed by McKnight et al. (2002) and the technol-ogy trust formation model (TTFM) studied by Li, Valacich,and Hess (2004). Initial trust refers to trust in an unfamiliartrustee, a relationship in which the actors do not yet havecredible, meaningful information about, or affective bondswith, each other (Bigley & Pearce, 1998). McKnight et al.proposed and validated the measures for a multidisciplinaryand multidimensional model of initial trust in e-commerce(McKnight et al., 2002). The TTFM is a comprehensivemodel of initial trust formation. It is used to explain andpredict people’s trust toward a specific information system(Li et al., 2004). Both models made use of the framework ofthe theory of reasoned action (TRA) created by Fishbeinand Ajzen (1975) to explain how people form initial trust inan unfamiliar entity, and both models integrated importanttrusting antecedents into their frameworks in order to effec-tively predict people’s trust. Since the objective of bothstudies was to predict initial trust (trusting intention) beforeany actual interaction with the trusting object, trust behav-ior was not considered.

In addition to the initial trust, there are short-term trustthat is built up over the first interactions with a system andlong-term trust that is developed with the continuous use of asystem over a longer period of time. Ongoing trust appearedin McKnight et al. (2002) and it consists of the short-term

trust and the long-term trust. In our study, we mainly focuson the ongoing trust evaluation based on the usage behaviorswith regard to mobile applications. In particular, the ongoingtrust could contribute to the trustee’s reputation and thusgreatly help other entities to generate their initial trust.

For other examples of psychometrical studies ontrust, Gefen (2000) proved that familiarity builds trust;Pennington, Wilcox, and Grover (2004) tested that one trustmechanism, vendor guarantees, has direct influence onsystem trust; Bhattacherjee (2002) studied three key dimen-sions of trust: trustee’s ability, benevolence, and integrity;Pavlou and Gefen (2004) explained that institutional mecha-nisms engender a buyer’s trust in the community of onlineauction sellers.

Existing studies have recognized that trust is a multidimen-sional construct and examined the types of trust (e.g.,knowledge-based trust, cognition-based trust, calculative-based trust, institution-based trust, and personality-basedtrust) (Gefen, Karahanna, & Straub, 2003; Wu & Chen, 2005;Zucker, 1986). Most of existing trust studies fall into theareas of organizational settings and electronic commerce(Abrams, Cross, Lesser, & Levin, 2003; Corritore, Kracher, &Wiedenbeck, 2003; Gefen et al., 2003; McAllister, 1995;Parkhe, 1998; Paul & McDaniel, 2004; Ratnasingam, 2005;Zucker, 1986). However, few studies explore trust within thecontext of mobile applications, especially through trustbehavior experiments.

Current trust models have been developed by consideringspecific security issues and also knowledge, experience,practices, and performance history (Daignault & Marche,2002). Much of the prior research in trust of automationhas focused primarily on its psychological aspects (Muir,1994). A number of proposals have been presented to linksome of the psychological aspects of trust with engineeringissues. For example, attempts have been made to map psy-chological aspects of trust (e.g., reliability, dependability,and integrity) to human–computer trust associated withengineering trust issues such as reliability and security(Crocker & Algina, 1986). Lance, Hoffman, Kim, and Blum(2006) studied trust from a number of influencing factorsfrom both the engineering and psychological points of viewand tried to combine these factors in order to provide acomprehensive model.

Quite a number of computational trust models have beendeveloped in the literature of computer science and engineer-ing (Yan, 2010). However, some factors that influence trustare hard to model, e.g., the subjective factor of a user. Forassessing a user’s trust in a computer product, the user’s trustcriteria have to be understood in different contexts. Thisintroduces additional challenges on human–computer inter-action (e.g., usability). Therefore, in order to overcome theabove challenges, it might be more appropriate to model trustbehavior rather than trust itself.

640 Trust can be evaluated based on trust behaviors


Trust behavior study

Trust enables people to live in a risky and uncertain situation(Mayer, Davis, & Schoorman, 1995). It provides the means todecrease complexity by reducing the number of options onehas to consider in a given situation (Barber, 1983; Lewis &Weigert, 1985; Luhmann, 1979). Trust is the key to decisionmaking. Research in ergonomics has examined how trust isestablished, maintained, lost, and regained in human–computer systems (Corritore et al., 2003). Trust has been seenas an intervening variable that mediates the users’ behaviorswith computers (Muir, 1994).

McKnight et al. (2002) proposed that consumer subjectiveprobability of depending involves the projected intention toengage in three specific risky behaviors—provide the vendorpersonal information, engage in a purchase transaction, oract on vendor information (e.g., financial advice). Followingthis theory, a mobile user gains experience with a specificmobile application, and this experience may be the dominantinfluence on trusting beliefs, instead of dispositional orinstitution-based trust. In addition, perceived mobile appli-cation quality should relate positively to both trusting beliefsand trusting intentions. McKnight et al. (2002) also pointedout a number of opportunities for future research. Some ofthem relate directly to overcoming the limitations of theirstudies. One particular suggestion was to conduct a study inwhich the ultimate outcome of interest—trust behavior—isdirectly measured. They identified a handful of commonlydiscussed trust-related behaviors in e-commerce—there maybe others. Because of the difficulty of asking subjects toundertake such behavior, they did not measure actual behav-ior in their study. Note that trust and trust behavior are differ-ent concepts: Trust is the willingness to assume risk; trustbehavior is the assuming of risk (Mayer et al., 1995, p. 724).Whether or not the trustor will take a specific risk is influ-enced by trusting beliefs, trusting intentions, and the per-ceived risk of the trust behavior (Grabner-Kräuter &Kaluscha, 2003).

The TRA explains how people form initial trust in an unfa-miliar entity (Fishbein & Ajzen, 1975). It posits that beliefslead to attitudes, which lead to behavioral intentions, whichlead to the behavior itself (Fishbein & Ajzen, 1975). Applyingthis theory, we propose that trusting beliefs (perceptions ofspecific mobile application attributes) lead to trusting inten-tions (intention to engage in using a mobile applicationthrough user–computer interaction), which in turn result intrust behaviors (trying to use and continuously using theapplication in various contexts). Additionally, numerousresearchers have conceptualized trust as a behavior. Valida-tion has been done in the context of work collaboration andsocial communications (Anderson & Narus, 1990; Deutsch,1973; Fox, 1974), software system usage (Sheppard et al.,1988; Venkatesh & Davis, 2000), and e-banking and economy

(Grabner-Kräuter & Kaluscha, 2003). However, no priorwork explores trust behavior of mobile application usagebased on our current knowledge.

The trust and technology acceptance model (Gefen et al.,2003) has been well studied in online shopping and the resultsshowed that understanding both the Internet technology andissues around trust is important in determining behavioralintention to use (Gefen et al., 2003; Wu & Chen, 2005). Thismodel placed the use of online system into two system fea-tures: ease of use and usefulness (related to importance andurgency) (Davis, 1989) as well as trust in e-vendors. The resultindicated that these variables are good predictors for behaviorintention to use online shopping (Wu & Chen, 2005). Wecould try to further extend the above conclusions to thecontext of mobile applications.

One important issue that contributes to whether the userswould like to use a new product or system (e.g., a mobileapplication) is how much they trust it. Muir is one of the firstresearchers to look at a decision process between supervisorsand automated systems. She verified the hypothesis proposedby Sheridan et al. that the supervisor’s intervention behavioris based upon his/her trust in automation (Sheridan, 1980). Apositive correlation between trust and use has been proved(Lee & Moray, 1994; Muir, 1994; Muir & Moray, 1996). Leeand Moray (1992) found that trust in a system partiallyexplains system use, but other factors (such as the user’s ownability to provide manual control) also influence the systemuse. Muir and Moray (1996) further argued that trust in auto-mated machines is based mostly on users’ perceptions on theexpertise of the machine, i.e., the extent to which the automa-tion performs its function properly. The relationship betweentrust and interaction behavior is obvious. Trust has a directinfluence on behavioral intention to use (Bandura, 1986;Davis, 1989). Wu and Chen (2005) also pointed out that trustis apparently an important antecedent of attitude toward theonline transaction behavior. All above studies lay the founda-tion for our hypothesis: A user’s trust in a mobile applicationcan be evaluated based on the user’s trust behaviors.

Many studies have empirically examined the determinantsof continued usage (continuance) (Hsu, Yen, Chiu, & Chang,2006). These studies provided preliminary evidence that con-tinued usage behaviors are determined by different factors.Most studies aimed to examine the change of users’ cognitivebeliefs and attitudes from pre-usage stage to usage stage andhow they influence users’ intentions to continue their usage,especially within the context of online shopping. Forexample, expectancy disconfirmation theory (Oliver, 1980),which is a consumer behavior model, explained and pre-dicted consumer satisfaction and repurchase intentions.Based on this theory, satisfied consumers form intentions toreuse a product or service in the future, while dissatisfiedusers discontinue subsequent use. Users would evaluatewhether their initial cognitions are consonant or dissonant

Yan et al. 641


with actual experience (Bhattacherjee & Premkumar, 2004).In our study, we examine the relationship between continu-ance and trust since trust is obviously relevant to use.Continuance implies users’ ongoing trust based on actualexperiences.

Grabner-Kräuter and Kaluscha (2003) reviewed 11 trustconstruct studies. Trusting intentions and their antecedentsare examined in several studies (in Bhattacherjee, 2002;Gefen, 2000; Gefen & Straub, 2003; Jarvenpaa, Tractinsky, &Saarinen, 1999; Jarvenpaa, Tractinsky, & Vitale, 2000)whereas trust-related behaviors are only investigated in twostudies (in Kim & Prabhakar, 2004; Pavlou, 2003). In order toinvestigate trust-related behavior as a consequent of trust, theformer has to be measured in terms of actual behavior, notwillingness to engage in behavior (Mayer et al., 1995). Insome of the past studies (e.g., Jarvenpaa et al., 1999, 2000;Pavlou, 2003; de Ruyter et al., 2001), company reputationwas discussed and included, but none of the 11 studiesexplicitly investigated the interrelationship between trustand branding. Another largely unexplored factor is productquality. The interdependencies between consumers’ trust andproduct performance could be a basis for upcoming research(Shankar, Urban, & Sultan, 2002). Varying the trustor ortrustee or context may result in different sets of antecedentand consequence trust.All of the above offer additional direc-tions for our research.

Existing studies focus on human’s trust in an automationand intelligent machine. A number of trust models have beenproposed in the context of e-commerce. Little work has beendone in the context of mobile applications although the sig-nificance of such study is obvious. Prior arts also lackstudy on the influence of recommendations, personality, andusage context with regard to human–computer trust. Withthe rapid development of mobile computing technology, amobile device has become a multi-application system formultipurpose and multi-usage. It is an open platform thatallows deploying new or upgraded applications at anytimeand anywhere through a network connection. Therefore,such a dynamically changed system, different from the exist-ing research domains, introduces new challenges for human–computer trust. We believe that research should go into depthin the newly thrived domain of mobile applications.

Research model andscale development

We applied a psychometric method to examine our hypoth-esis, i.e., the user’s trust in a mobile application can be studiedthrough the user’s behaviors, which can be monitored via theuser–device interaction during the application usage. Wedesigned a questionnaire (i.e., measures or a measurementscale), taking short message service (SMS) as a concreteexample of mobile application. Each item in the question-

naire is a statement for which the participants need to indi-cate their levels of agreement. The questionnaire is anchoredusing a 7-point Likert scale ranging from “strongly disagree”to “strongly agree.” First, a preexperiment with 318 partici-pants was conducted in order to optimize our questionnaire(Yan, Niemi, Dong, & Yu, 2008). Further, we ran a formalexperiment with more than 1,500 participants to explore andevaluate the trust behavior construct for mobile applications.

Because there are scarce theory and evidence about whatusers’ trust behaviors are regarding mobile applications, westarted our study in an exploratory manner. Thus, we soughtfirst to generate a measurement scale that would define thenumber and the nature of the dimensions that underlie theusers’ perceptions of trust behaviors. For this purpose, weused exploratory factor analysis in the preexperiment study(Yan et al., 2008). Exploratory factor analysis is particularlysuitable to identify “a set of latent constructs underlyinga battery of measured variables” (Fabrigar, Wegener,MacCallum, & Strahan, 1999, p. 275). Once we obtained sucha set of latent constructs, we conducted the formal experi-ment study that used both exploratory factor analysis andconfirmatory factor analysis (CFA) to refine and test themodel that emerged from our preceding study (Anderson &Gerbing, 1988; Gerbing & Anderson, 1988; MacCallum &Austin, 2000).

From our preexperiment study, we found that three con-structs play significant roles as direct determinants of trustbehavior (Yan et al., 2008).We call them using behavior (UB),reflection behavior (RB), and correlation behavior (CB).They comprise the user’s trust behavior and contribute to thecalculation of the estimate (from the device point of view) onthe user’s trust in the mobile application. Meanwhile, weposit that trust behavior is also related to or impacted by anumber of external factors that could play as the basis of theuser’s initial or potential trust. The labels used for the con-structs describe the essence of the construct and are meant tobe independent of any particular theoretical perspective. Inthe remainder of this section, we define each of the determi-nants, specify the role of external factors, and provide thetheoretical justification for the hypotheses. Figure 1 presentsour research model. The definitions of constructs and meas-urement scale items are described in Table 1.

Personal

motivation

Brand impact

Perceived quality

Personality

Using behavior

Reflection behavior

Correlation

behavior

Trust

Behavior

Figure 1 Research model.



Tab

le1

Con

stru

cts,

Defi

nitio

ns,a

ndSc

ales

Con

stru

ctD

efini

tion

Item

s

UB:

usin

gbe

havi

or(r

ootc

onst

ruct

)Th

ese

tofb

ehav

iors

abou

tnor

mal

appl

icat

ion

usag

eU

B1:n

orm

alus

age

beha

vior

(sub

-con

stru

ct)

The

elap

sed

usag

etim

e,th

enu

mbe

rofu

sage

,and

usag

efr

eque

ncy

1.Th

em

ore

times

you

use

the

mes

sagi

ng,t

hem

ore

you

trus

tit.

2.Th

em

ore

freq

uent

lyyo

uus

eth

em

essa

ging

,the

mor

eyo

une

edit.

3.Th

elo

nger

time

you

use

the

mes

sagi

ng,t

hem

ore

you

trus

tit.

UB2

:beh

avio

rrel

ated

toco

ntex

t(s

ub-c

onst

ruct

)Th

epe

rspe

ctiv

eth

atth

ech

arac

teris

tics

ofus

age

cont

ext

such

asus

age

risk,

impo

rtan

ce,a

ndur

genc

yco

uld

influ

ence

the

trus

tbeh

avio

r

1.Yo

udo

mor

eim

port

antt

asks

thro

ugh

the

mes

sagi

ngif

you

trus

titm

ore.

2.Yo

udo

mor

eris

kyta

sks

thro

ugh

the

mes

sagi

ngif

you

trus

titm

ore

(e.g

.,SM

Spa

ymen

t).

3.Yo

udo

mor

eur

gent

task

sth

roug

hth

em

essa

ging

ifyo

utr

usti

tmor

e.U

B3:f

eatu

re-r

elat

edus

age

beha

vior

(sub

-con

stru

ct)

The

pers

pect

ive

that

the

appl

icat

ion

feat

ures

expe

rienc

edby

aus

erim

ply

the

profi

cien

cyof

the

user

inth

eap

plic

atio

nus

age

1.Yo

uw

ould

try

mor

efe

atur

esof

the

mes

sagi

ngif

you

trus

titm

ore.

2.A

fter

tryi

ngm

ore

feat

ures

ofth

em

essa

ging

,you

gain

mor

eex

pert

ise

onit.

3.G

ood

qual

ityof

the

mes

sagi

ngw

ould

enco

urag

eyo

uto

try

new

feat

ures

ofit.

RB:r

eflec

tion

beha

vior

(roo

tco

nstr

uct)

The

usag

ebe

havi

ors

afte

rthe

user

conf

ront

sap

plic

atio

npr

oble

ms/

erro

rsor

has

good

/bad

usag

eex

perie

nces

RB1:

bad

perf

orm

ance

refle

ctio

nbe

havi

or(s

ub-c

onst

ruct

)H

owth

eba

dpe

rfor

man

ceof

anap

plic

atio

nin

fluen

ces

aus

er’s

trus

tin

it1.

You

coul

dde

crea

seth

etim

esof

usin

gth

em

essa

ging

due

toits

bad

perf

orm

ance

.2.

Your

usag

ein

tere

stan

dus

age

freq

uenc

yco

uld

bede

crea

sed

due

toth

eba

dm

essa

ging

perf

orm

ance

.3.

You

coul

dde

crea

seth

etim

eof

usin

gth

em

essa

ging

due

toits

bad

perf

orm

ance

.RB

2:ba

dpe

rfor

man

cere

flect

ion

beha

vior

rela

ted

toco

ntex

t(s

ub-c

onst

ruct

)

How

the

bad

perf

orm

ance

ofan

appl

icat

ion

influ

ence

sa

user

’str

usti

ndi

ffer

entc

onte

xts

1.Ba

dpe

rfor

man

ceof

the

mes

sagi

ngco

uld

disc

oura

geyo

uto

doim

port

antt

hing

sw

ithit.

2.Ba

dpe

rfor

man

ceof

the

mes

sagi

ngco

uld

disc

oura

geyo

uto

dohi

ghly

risky

thin

gsw

ithit.

3.Ba

dpe

rfor

man

ceof

the

mes

sagi

ngco

uld

disc

oura

geyo

uto

dour

gent

thin

gsw

ithit.

RB3:

good

perf

orm

ance

refle

ctio

nbe

havi

or(s

ub-c

onst

ruct

)H

owth

ego

odpe

rfor

man

ceof

anap

plic

atio

nin

fluen

ces

aus

er’s

trus

tin

it1.

You

coul

din

crea

seth

etim

eof

usin

gth

em

essa

ging

due

toits

good

perf

orm

ance

.2.

You

coul

din

crea

seth

etim

eof

usin

gth

em

essa

ging

due

toits

good

perf

orm

ance

.3.

Your

usag

ein

tere

stan

dus

age

freq

uenc

yco

uld

bein

crea

sed

due

toth

ego

odm

essa

ging

perf

orm

ance

.RB

4:go

odpe

rfor

man

cere

flect

ion

beha

vior

rela

ted

toco

ntex

t(s

ub-c

onst

ruct

)

How

the

good

perf

orm

ance

ofan

appl

icat

ion

influ

ence

sa

user

’str

usti

ndi

ffer

entc

onte

xts

1.G

ood

perf

orm

ance

ofth

em

essa

ging

coul

den

cour

age

you

todo

high

lyris

kyth

ings

with

it.2.

Goo

dpe

rfor

man

ceof

the

mes

sagi

ngco

uld

enco

urag

eyo

uto

doim

port

antt

hing

sw

ithit.

3.G

ood

perf

orm

ance

ofth

em

essa

ging

coul

den

cour

age

you

todo

urge

ntth

ings

with

it.RB

5:ba

dex

perie

nce

refle

ctio

nto

cont

ext(

sub-

cons

truc

t)H

owth

eba

dus

age

expe

rienc

eof

anap

plic

atio

nin

fluen

ces

aus

er’s

trus

tin

diff

eren

tcon

text

s1.

Aft

erve

ryba

dex

perie

nces

ofus

ing

the

mes

sagi

ng,y

ouco

uld

use

itto

dole

ssris

kyta

sk.

2.A

fter

very

bad

expe

rienc

esof

usin

gth

em

essa

ging

,you

coul

dus

eit

todo

less

impo

rtan

tta

sk.

3.A

fter

very

bad

expe

rienc

esof

usin

gth

em

essa

ging

,you

coul

dus

eit

todo

less

urge

ntta

sk.

RB6:

good

expe

rienc

ere

flect

ion

toco

ntex

t(su

b-co

nstr

uct)

How

the

good

usag

eex

perie

nce

ofan

appl

icat

ion

influ

ence

sa

user

’str

usti

ndi

ffer

entc

onte

xts

1.A

fter

very

good

expe

rienc

esof

usin

gth

em

essa

ging

,you

coul

dus

eit

todo

mor

eris

kyta

sks.

2.A

fter

very

good

expe

rienc

esof

usin

gth

em

essa

ging

,you

coul

dus

eit

todo

mor

eim

port

ant

task

s.3.

Aft

erve

rygo

odex

perie

nces

ofus

ing

the

mes

sagi

ng,y

ouco

uld

use

itto

dom

ore

urge

ntta

sks.

Yan et al. 643


Tab

le1

(Con

tinue

d)

Con

stru

ctD

efini

tion

Item

s

CB:

corr

elat

ion

beha

vior

(roo

tco

nstr

uct)

The

usag

ebe

havi

ors

corr

elat

edto

mob

ileap

plic

atio

nsw

ithsi

mila

rfun

ctio

nalit

ies

CB1

:com

paris

onof

norm

alus

age

beha

vior

(sub

-con

stru

ct)

How

the

diff

eren

tusa

gebe

havi

ors

ofsi

mila

rlyfu

nctio

ned

appl

icat

ions

indi

cate

trus

t1.

Usi

ngth

em

essa

ging

mor

etim

esth

anan

othe

rsim

ilarly

func

tione

dm

obile

appl

icat

ion

mea

nsyo

utr

usti

tmor

e.2.

Usi

ngth

em

essa

ging

mor

efr

eque

ntly

than

anot

hers

imila

rlyfu

nctio

ned

mob

ileap

plic

atio

nm

eans

you

trus

titm

ore.

3.Sp

endi

ngm

ore

time

inus

ing

the

mes

sagi

ngth

anan

othe

rsim

ilarly

func

tione

dm

obile

appl

icat

ion

mea

nsyo

utr

usti

tmor

e.C

B2:c

ompa

rison

rela

ted

toco

ntex

t(s

ub-c

onst

ruct

)H

owth

edi

ffer

entu

sage

deci

sion

sar

ere

late

dto

trus

tin

vario

usco

ntex

tsw

ithre

gard

tosi

mila

rlyfu

nctio

ned

appl

icat

ions

1.U

sing

the

mes

sagi

ng,n

otan

othe

rsim

ilarly

func

tione

dm

obile

appl

icat

ion,

tofu

lfill

am

ore

impo

rtan

ttas

km

eans

you

trus

titm

ore.

2.U

sing

the

mes

sagi

ng,n

otan

othe

rsim

ilarly

func

tione

dm

obile

appl

icat

ion,

tofu

lfill

am

ore

risky

task

mea

nsyo

utr

usti

tmor

e.3.

Usi

ngth

em

essa

ging

,not

anot

hers

imila

rlyfu

nctio

ned

mob

ileap

plic

atio

n,to

fulfi

lla

mor

eur

gent

task

mea

nsyo

utr

usti

tmor

e.C

B3:r

ecom

men

datio

nbe

havi

or(s

ub-c

onst

ruct

)Th

epe

rspe

ctiv

eth

atpo

sitiv

ere

com

men

datio

nbe

havi

orim

plie

sth

eus

er’s

trus

t1.

Ifyo

uha

veve

rygo

odex

perie

nces

inus

ing

the

mes

sagi

ng,y

ouge

nera

llyw

ould

like

tore

com

men

dit.

2.Fo

rtw

osi

mila

rlyfu

nctio

ned

mes

sagi

ngap

plic

atio

ns,y

outr

ustm

ore

inth

eon

eyo

uw

ould

like

tore

com

men

d.3.

Aft

erve

ryba

dex

perie

nces

inus

ing

the

mes

sagi

ng,y

ouge

nera

llydo

n’tw

antt

ore

com

men

dit.

Exte

rnal

fact

ors

The

fact

ors

orva

riabl

esth

atin

fluen

cetr

ustb

ehav

iors

BI:b

rand

impa

ctTh

epe

rspe

ctiv

eth

attr

ustb

ehav

iori

sin

fluen

ced

bybr

and

and

repu

tatio

ns1.

You

like

am

obile

appl

icat

ion

deve

lope

dby

afa

mou

sve

ndor

.2.

You

like

usin

ga

mob

ileph

one

with

afa

mou

sbr

and.

3.Yo

uw

ould

like

tore

com

men

da

mob

ileap

plic

atio

nde

velo

ped

bya

fam

ous

vend

or.

4.Fo

ram

obile

appl

icat

ion

deve

lope

dby

anin

fam

ous

vend

or,y

ouw

illca

utio

usly

use

itor

stop

usin

git

ifyo

um

eets

ome

prob

lem

s.PM

:per

sona

lmot

ivat

ion

The

pers

pect

ive

that

the

user

’sge

nera

lpro

pens

ityto

trus

tis

one

ofim

port

ante

xter

nalf

acto

rsre

late

dto

trus

tbeh

avio

rs

1.A

mob

ileph

one

isve

ryim

port

antt

oyo

urlif

e.2.

You

ofte

nus

ea

mob

ileph

one

totr

ansf

erim

port

anti

nfor

mat

ion.

3.Be

caus

ea

mob

ileph

one

isim

port

antt

oyo

u,yo

uw

illco

ntin

uous

lyus

eit

insp

iteof

itsso

ftw

are

havi

ngso

me

prob

lem

s.P:

pers

onal

ityTh

epe

rspe

ctiv

eth

atpe

rson

ality

ison

eof

the

impo

rtan

tfa

ctor

sth

atm

ayaf

fect

trus

tbeh

avio

rs1.

Inyo

urop

inio

n,m

obile

appl

icat

ions

have

apr

omis

ing

futu

re.

2.Yo

ube

lieve

the

mob

ileap

plic

atio

nw

illbe

cont

inuo

usly

impr

ovin

gan

dup

grad

ing.

3.Yo

uha

veco

nfide

nce

onth

efu

ture

ofm

obile

phon

eus

age.

4.Yo

uth

ink

the

mob

ileph

one

isa

pers

onal

com

mun

icat

orlik

edby

mos

tpeo

ple.

5.Yo

uth

ink

the

mob

ileap

plic

atio

nsbe

nefit

your

life

and

stud

yve

rym

uch.

DQ

:per

ceiv

edde

vice

qual

ityTh

epe

rspe

ctiv

eth

atth

equ

ality

ofus

edm

obile

devi

ces

coul

dbe

ane

cess

ary

(but

nots

uffic

ient

)con

ditio

nfo

rm

obile

appl

icat

ion

trus

t

1.G

ener

ally

spea

king

,the

phon

eyo

uar

eus

ing

isw

orki

ngw

ell.

2.U

sing

am

obile

phon

efo

ryou

isea

syan

dco

nven

ient

.3.

Usi

nga

mob

ileph

one

isec

onom

ical

and

usef

ulfo

ryou

.4.

Usi

nga

mob

ileph

one

can

satis

fyyo

urpe

rson

alde

man

dsw

ell.

SMS

=sh

ortm

essa

gese

rvic

e.



Using behavior

UB is defined as the set of behaviors about normal applica-tion usage. Trust has tight correlation with use (Muir &Moray, 1996). Trust in a system partially explains system use(Lee & Moray, 1992). Using an application is a behaviorimplying trust in it (McKnight et al., 2002), thus it is highlyrelated to trust behavior.We posit that trust could be reflectedby normal usage behavior (UB1), which is indicated byelapsed usage time, the number of usage, and usage fre-quency. Meanwhile, characteristics of the usage contextsuch as risk, importance, and urgency could also influencethe trust behavior (UB2) (Grabner-Kräuter & Kaluscha,2003). McKnight et al. proposed some high-risk andhigh-importance task-related behaviors as trust behaviors(McKnight et al., 2002). Herein, we try to examine trust couldleverage consumers to overcome perceptions of uncertaintyand risk and engage in “trust-related behaviors.” In addition,the ability of a user to use a mobile application also influencesthe usage (Lee & Moray, 1992). Generally, a mobile applica-tion provides a number of functionalities, i.e., features. Themore features experienced by the user, the more proficienthe/she is in the application usage (UB3). In summary, wepropose that:

Hypothesis 1a. Trust in a mobile application will havea positive influence on the user’s usage reflectedby elapsed usage time and number and frequency ofusages.

Hypothesis 1b. Trust in a mobile application will have apositive influence on the user’s behavior regardingrisky, urgent, or important tasks.

Hypothesis 1c. Experiences on the features of a mobileapplication will have a positive influence on the user’sproficiency in using the application.

Reflection behavior

RB consists of usage behaviors after the user confrontsapplication problems/errors or has good/bad usage experi-ences. Notably, the difference of the RB and the UB lies inthe fact that the first one is related to the type of eventwhereas the second one is related to general usage statistics.Their contributions to trust evaluation could be different.Some researchers have found the strong effect of computererrors or performance on trust (Corritore et al., 2003;Kantowitz, Hankowski, & Kantowitz, 1997; Lee & Moray,1992; Muir, 1994; Muir & Moray, 1996). Empirical studies oftrust in automated machines show that performance andtrust increase following a similar learning curve as long asthere are no errors (Lee & Moray, 1992). But machine errorshave a strong effect on trust. The magnitude of an error isan important factor in loss of trust (Lee & Moray, 1992;

Muir & Moray, 1996). On the other hand, trust couldrecover even when small errors continue, if the user is ableto understand and compensate for the errors; but trust maynot be restored to its level prior to the series of errors (Lee &Moray, 1992; Muir & Moray, 1996). Errors encountered inone function of a system can lead to distrust of related func-tions, but do not necessarily generalize to the entire system(Muir & Moray, 1996). However, even in the face of compu-ter errors, a user may continue to trust a computer system incertain situations, for example, if workload is high or if theerrors are predictable (Muir & Moray, 1996). From the lit-erature, we can see that the RB can imply the user’s trustand continuous trust in an application, especially in thecontext when he/she is facing an application problem. Thus,it is one of important constructs of trust behavior. Herein,we propose that:

Hypothesis 2a. Good/bad performance of a mobileapplication will have a positive/negative influence onthe user’s trust in the application.

Hypothesis 2b. Good/bad application performance orusage experience will positively/negatively influencethe user’s behavior related to risky, urgent, or impor-tant tasks.

We designed a number of items (as shown in Table 1) toexamine bad and good application performance’s influenceon usage (RB1, RB3) and context (e.g., importance, risk, andurgency), related usage decision (RB2, RB4), as well as theimpact of user experience on contextual usage decision(RB5, RB6).

Correlation behavior

Future mobile market would probably be very competitive. Anumber of similarly functioned mobile applications devel-oped by different vendors would be available at the same timefor consumption. CB concerns the usage behaviors correlatedto similarly functioned mobile applications. Since trust isobviously related to use (Lee & Moray, 1992; Muir, 1994;Muir & Moray, 1996), the usage implies trust. The usagebehavior differences with regard to similar applications implythe user’s different trust in them (Yan et al., 2008). Thereby,the CB is also one important construct of trust behavior.Herein, we propose that for two similarly functioned applica-tions, higher usage rate (i.e., elapsed usage time and fre-quency, the number of usages) means more trust. Meanwhile,trust is also influenced by various contexts (Grabner-Kräuter& Kaluscha, 2003; Yan, 2010; Yan & Holtmanns, 2008). There-fore, we expect that for two similarly functioned applications,the user would like to use more trustworthy one to do risky,urgent, or important tasks. We designed a number of items(as shown in Table 1) about similarly functioned applications

Yan et al. 645


to examine the correlation of different usage behaviors totrust (CB1) and the relation of different usage decisions totrust in various contexts (CB2).

Hypothesis 3a. For two similarly functioned applica-tions, higher usage rate (i.e., elapsed usage time andfrequency, the number of usages) implies more trust.

Hypothesis 3b. For two similarly functioned applica-tions, the user would like to use more trustworthy oneto do risky, urgent, or important tasks.

In addition, we posit that a positive recommendation (abehavior to suggest other people using a mobile application)also implies trust (CB3). Current literature has widely studiedrecommendation’s impact on trust (Koh & Sundar, 2010;Wang & Benbasat, 2005). We would like to argue that positiverecommendation behavior indicates a recommender’s trustin a recommended entity. For example, a person who highlyrecommends his/her friend to install and use an applicationindicates his/her favorite and trust in it (Yan et al., 2008). Thisbehavior can be observed by the mobile device since quite anumber of mobile applications provide a possibility to share(and, hence, recommend) via SMS, or multimedia service, orshort-range connection (e.g., Bluetooth or infrared), a linkfor an application installation with other mobile devices.Thus, we propose that:

Hypothesis 3c. Positive recommendation behaviorimplies the user’s trust.

External factors

Apart from the trust behavior exploration, we also designeda number of items in order to do external nomological vali-dation. It is widely accepted that trust is influenced by brandand reputations (Corritore et al., 2003; Grabner-Kräuter &Kaluscha, 2003; Yan, 2010). Trustor’s general propensity totrust is one of important external factors related to trust(Corritore et al., 2003). Personality is one of the importantfactors that may affect usage decision and usage behavior.Different personalities attribute different importance levelsto each of the accepted trust cues (such as branding andprofessional user interface design, etc.) Extroversion andopenness to experience lead to a higher disposition to trust.However, neuroticism and conscientiousness lead to a lowerdisposition to trust (Lumsden & MacKay, 2006). On theother hand, the quality of used mobile devices could be anecessary (but not sufficient) condition for mobile applica-tion trust (Kim & Prabhakar, 2004; Pavlou, 2003). Hence,we attempt to study the influence of the following fourexternal variables on the user’s trust behavior: (a) brandimpact (BI); (b) personal motivation (PM); (c) personality(P); and (d) perceived device quality (DQ). We proposethat:

Hypothesis 4a. The brand of mobile application willinfluence the user’s trust behavior.

Hypothesis 4b. The PM will influence the user’s trustbehavior.

Hypothesis 4c. The user’s personality will influence theuser’s trust behaviors.

Hypothesis 4d. The perceived DQ will be a necessarycondition for application trust.

As shown in Table 1, most items about the external factorsare adapted from prior related research conducted in thefields of e-commerce, and are modified to fit the mobileapplication context. The items on PMs and perceived DQare designed based on the definitions in McKnight et al.(2002). The items about BI and P are designed on the basisof the theoretic results achieved in Corritore et al. (2003);Grabner-Kräuter and Kaluscha (2003); and Yan andHoltmanns (2008).

Analysis and results

Data collection

The experiment was conducted by three psychologists inBeijing, China. The participants were chosen from BeijingJiao Tong University, Renmin University of China, andBeijing International Studies University. The questionnairewas provided to the participating undergraduates enrolled inpsychology lectures. At the beginning, the participants werearranged to answer the questionnaires in a group. Then,the questionnaires were collected and each participant wasoffered a small gift. Almost all participants had past experi-ences of answering a questionnaire survey. They were familiarwith the basic rules of such a survey. Meanwhile, the conduc-tors explained the basic concepts in the questionnaire beforethe survey was carried out. It took the participants about 20minutes to answer the questionnaire.

Totally, 1,575 subjects participated; 1,120 responses(71.1%) were valuable and usable based on three selectioncriteria: (1) no missed item response; (2) no regular patterncan be found from the responses; and (3) the responses on allitems are not the same (i.e., serious response). Among theselected subjects with valid responses, 671 (59.9%) werewomen and 449 (40.1%) were men; 43 participants werebelow 18 years and others were between 19 and 35 years.There were 502 (44.8%) participants who majored in scienceor technology, while 480 (42.9%) in arts. Except for onesample where information was missing, the rest majored inhumanities and social science. The personality types of theparticipants were presented in Table 2 based on the partici-pants’ personal specifications. About half of the participantshad sanguine personality type. One fourth of the participants



were phlegmatic facing different issues. Thus, most of partici-pants had positive personality types.

Table 3 provides the information about the participants’experiences on the usage of mobile phones and SMS.According to the survey, 419 (37.4%) participants hadexperiences of using the Internet-accessed applications (e.g.,a Web browser), 864 (77.1%) had experiences of usingmobile network-accessed applications (e.g., SMS), and 796(71.1%) had that of non-network-accessed applications(e.g., Profile). Most of the participants (87.9%) used mobile

phones more than half an hour and 62.1% more than 1hour/day. This indicates that mobile phone usage was verycommon and popular in Chinese universities, and Chineseundergraduates and graduates are typical samples of mobileusers. In addition, SMS was regularly and frequently used byChinese university students. Altogether, 71.4% of partici-pants sent or received SMS more than 10 times per day. Thisimplies that adopting SMS as an example of mobile applica-tion in our experiment was appropriate and easy to be fol-lowed by the participants.

Table 4 provides the information about the general experi-ences of weekly usage of some specific mobile applications.We observed that most of Chinese university students hadexperiences on different kinds of mobile applications, amongwhich chatting with friends and mobile communicationswere the most popular ones, whereas mobile advertisementand mobile commerce were not popular at the time of ourexperiment. The statistical data indicated that the mobilephone mainly served as a communication tool for Chineseuniversity students. Thereby, it was suitable to examine thetrust behavior via SMS in our experiment.

Table 2 Personality Type of Participants Based on Direct Indication

Personality type Frequency Percent

Sanguine 506 45.2Melancholy 91 8.1Choleric 76 6.8Phlegmatic 282 25.2Others 142 12.7Missing 23 2.1Total 1,120 100

Table 3 Experience on Mobile Phone and SMS Usage

The experience on mobile phone and SMS No. of subjects Percent

Mobile phone usage time Below 0.5 hour/day 131 11.70.5–1 hour/day 289 25.81–5 hours/day 335 29.9More than 5 hours/day 361 32.2Missing 4 .3Total 1,120 100

SMS usage times Below 10 times/day 320 28.610–30 times/day 581 51.930–50 times/day 144 12.9More than 50 times/day 74 6.6Missing 1 .1Total 1,120 100.0

SMS = short message service.

Table 4 General Experience on Mobile Applications

General experience

Chatting with friends Mobile communicationsReceiving serviceinformation Shopping and business

Frequency Percent Frequency Percent Frequency Percent Frequency Percent

Times Uncertain 193 17.2 173 15.4 159 14.2 347 31.0<5 times/week 257 22.9 238 21.3 418 37.3 695 62.15–10 times/week 209 18.7 204 18.2 258 23.0 24 2.111–15 times/week 88 7.9 109 9.7 116 10.4 12 1.116–20 times/week 44 3.9 50 4.5 28 2.5 1 .121–25 times/week 37 3.3 45 4.0 21 1.9 1 .1>25 times/week 280 25.0 294 26.3 105 9.4 10 .9Missing 12 1 7 .6 15 1. 30 2.Total 1,120 100 1,120 100 1,120 100 1,120 100

Yan et al. 647


Data processing and analysis

Data analysis took place in three phases, as described below.In the process, we analyzed three types of validity: conver-gent, discriminant, and nomological. Together, these types ofvalidity constitute construct validity, or “the extent to whichan operationalization measures the concept it is supposed tomeasure” (Bagozzi, Yi, & Phillips, 1991, p. 421). Convergentvalidity (CV) means the extent to which the measures for avariable act as if they are measuring the underlying theoreti-cal construct because they share variance (Schwab, 1980).Internal consistency reliability is generally considered a nec-essary, but not sufficient, condition for CV (Schwab, 1980).Discriminant validity (DV) investigates the degree to whichmeasures of two constructs are empirically distinct (Bagozziet al., 1991; Davis, 1989). Nomological validity refers towhether the construct performs as expected within its nomo-logical network (Schwab, 1980), such as relating to other con-structs as theory suggests (Boudreau, Gefen, & Straub, 2001;Webster & Martocchio, 1992).

We leveraged large sample size to provide better confi-dence in the results. The samples were randomly dividedinto two approximately equal parts. One part (n = 567) wasused for principal component analysis (PCA), which is oneimportant method of exploratory factor analysis, while theremaining samples (n = 553) were used for CFA. Mean-while, we also conducted correlation analysis and reliabilityanalysis.

Phase 1: PCA

Because some items were added and revised according to theresults of the preexperiment (Yan et al., 2008), in the firstphase, exploratory, principal components factor analysis andinternal consistency reliability analysis were conducted todetermine the extent to which trust constructs were discrimi-nant (using SPSS v11.5, SPSS Inc., Chicago, IL). The purposeof using PCA was to cull out the items that did not load on theappropriate high-level construct and extract principal factorsfor making a predictive model. Kaiser’s criterion was appliedin the PCA, which considers factors with an eigenvaluegreater than 1 as common factors (Nunnally, 1978). Inaddition, the items’ loadings should be greater than .4, andno cross-loading above .4 on the “wrong” trust construct(Boudreau et al., 2001; Hair, Anderson, Tatham, & Black,1998). If the item had a cross-loading above .4, and droppingthe item did not improve the reliability of its sub-constructs,the item was retained in its main sub-construct.

PCA was performed using an orthogonal rotation withquartimax. McKnight, Cummings, and Chervany (1998)argued that if the trust constructs form a model of causallylinked factors/variables (which implies positive correla-tions), oblique rotation should be applied in the PCA. The

orthogonal rotation assumes that constructs are not corre-lated. Selecting orthogonal rotation is based on the resultsachieved in our preexperiment: no much correlation amongfactors/variables (Yan et al., 2008).

Phase 2: CFA

The second phase was a CFA using structural equation mod-eling to assess the CV and the DV of the latent sub-constructsin each of the three high-level trust behavior constructs (i.e.,UB, RB, and CB). We conducted this analysis by creating aLISREL v8.53 (Scientific Software International, Inc., Skokie,IL) path diagram for each construct, its constituent sub-constructs, and their items. We applied the following indicesand criteria to assess model fitness: goodness-of-fit index(GFI) and normed fit index (NFI) greater than .90, adjustedgoodness-of-fit index (AGFI) greater than .80 (Gefen, Straub,& Boudreau, 2000), comparative fit index (CFI) greater than.90 (Jiang & Klein, 1999/2000), and root mean square error ofapproximation (RMSEA) lower than .08 for a good fit andlower than .05 for an excellent fit (Browne & Cudeck, 1993).The c2 statistic is particularly sensitive to sample size (i.e., theprobability of model rejection increases with an increasingsize of samples, even though the model is minimally false),and hence adjusted c2 (c2/df; df is the degree of freedom) issuggested as a better fit metric (Bentler & Bonnett, 1980). It isrecommended that this metric should not exceed 5 for amodel with good fitness (Bentler, 1989).

If the fitness of the model is good, we further assessedthe CV and the DV of the latent sub-constructs insideeach of the three root constructs. CV was assessed usingthree criteria: (a) individual item lambda coefficients aregreater than .5 (Fornell & Larcker, 1981); (b) t statistic has asignificant .05 level for each path (Gefen et al., 2000); and(c) each path’s loading is greater than twice its standarderror (Anderson & Gerbing, 1988). DV among the latentvariables is shown without question if the intercorrelationbetween different latent variables is less than .6 (Carlsonet al., 2000).

Phase 3: discriminate validity analysis andnomological validity analysis

In the third phase, we used second-order models (throughLISREL 8.53) to analyze cross-construct relationships andreaffirm DV among the constructs. This analysis was per-formed at both the item and scale levels to ensure cross-validation and interpretation of results. We assessed internalnomological validity by testing proposed relationshipsamong the trust constructs themselves, and external nomo-logical validity by examining relationships between the trustbehavior constructs and other factors or variables influencingthe trust behavior.



Results

PCA results

Table 5 shows each item’s loading and extracted factorsachieved from PCA, exploratory factor analysis. The UB2.1had a cross-loading above .4. The reliability analysis showedthat dropping this item reduced the alpha value for the UBfrom .734 to .701. (Note that alpha is a reliability coefficient,which is an index to retain the item.) In addition, this item’sloading on its expected/intended construct was higher thantheir loadings on a “wrong” construct. Hereby, we decided toretain the item for the UB.

As shown in Table 5, all the item loadings were over .486regarding the UB. Five factors formed the RB. Their itemloadings were over .553. No items had a cross-loading above.4. However, RB1 and RB2 were merged. The CB was in formof two well-defined factors regarding behavior comparison(CB1 and CB2 were merged) and recommendation behavior.All their item loadings were over .589.

CFA results

Table 6 provides the results of CFA analysis on three root trustbehavior constructs based on the extracted components in

Table 5 Rotated Component Matrix of Exploratory Factor Analysis for Trust Behavior

Items

Loadings of components

UB1 UB2 UB3 RB1/2 RB4 RB6 RB3 RB5 CB1/2 CB3

UB1.1 .768UB1.3 .766UB1.2 .704UB3.3 .783UB3.2 .727UB3.1 .700UB2.2 .831UB2.3 .756UB2.1 .438 .486RB1.3 .776RB1.2 .770RB1.1 .743RB2.2 .737RB2.1 .714RB2.3 .553RB4.2 .746RB4.3 .671RB4.1 .653RB6.2 .742RB6.3 .718RB6.1 .711RB3.2 .764RB3.1 .758RB3.3 .661RB5.2 .820RB5.3 .747RB5.1 .697CB1.2 .770CB1.3 .743CB2.1 .711CB1.1 .688CB2.3 .685CB2.2 .676CB3.1 .778CB3.2 .744CB3.3 .589

Note. Extraction method: principal component analysis. Rotation method: quartimax with Kaiser normalization.

Yan et al. 649


PCA. Table 7 shows the results of CV and DV for each of sub-constructs of UB, RB, and CB.

Using behavior

Figure 2 shows the result of CFA analysis of the using behav-ior model (Model-UB). CV for each of the three sub-constructs was conducted according to the criteria discussedabove. The lambda coefficients of items were above .54 (.54–.75), each path was significant (t values are between 8.45 and10.36, p is at .05 level), and each path loading was greater than

twice its associated standard error (.08–.12). With GFI, NFI,and GFI statistics above .9, AGFI above .8, and RMSEA below.08, Model-UB fit was good (as shown in Table 6).

Reflection behavior

Figure 3 shows the result of the CFA analysis of the reflectionbehavior model (Model-RB). CV for each of its six sub-constructs was conducted based on the criteria discussedabove. As shown in Figure 3, each item’s lambda coefficientwas above .57 (.57–.85), each path was significant (t values are

Table 6 CFA Indices of Using Behavior, Reflection Behavior, and Correlation Behavior

c2 df c2/df GFI AGFI RMSEA NFI CFI IFI

Model-UB 100.59 24 4.19 .96 .93 .076 .91 .93 .93Model-RB 323.96 120 2.97 .94 .91 .055 .95 .97 .97Model-CB 60.96 24 2.54 .98 .96 .053 .97 .98 .98

AGFI = adjusted goodness-of-fit index; CB = correlation behavior; CFI = comparative fit index; GFI = goodness-of-fit index; IFI = incremental fit index;NFI = normed fit index; RB = reflection behavior; RMSEA = root mean square error of approximation; UB = using behavior.

Table 7 Results of Convergent Validity and Discriminant Validity

Lambdacoefficientsof items

t value ofeach path

p level ofeach path

Path loading(standard error)

Intercorrelation betweendifferent latent variables

Model-UB >.54 (.54–.75) 8.45–10.36 .05 level .54–.75 (.08–.12) <.30 (Table 8)Model-RB >.57 (.57–.85) 11.06–17.98 .05 level .57–.85 (.06–.12) <.60 (Table 8)Model-CB >.50 (.50–.80) 7.61–14.43 .05 level .50–.80 (.09–.21) <.56 (Table 8)

CB = correlation behavior; RB = reflection behavior; UB = using behavior.

UB1.1

UB1

UB1.2

UB1.3

UB2.1

UB2.2

UB2.3

UB3.1

UB3.2

UB3.3

UB2

UB3

0.68

0.54

0.75

0.68

0.54

0.56

0.60

0.62

0.63

0.54

0.71

0.43

0.53

0.71

0.68

0.64

0.61

0.60

1.00

1.00

1.00

0.45

0.48

0.32

Figure 2 The confirmatory factor analysis of using behavior. Note: UB1 = normal usage behavior; UB2 = usage behavior related to context;UB3 = usage behavior related to application features.



between 11.06 and 17.98, p is significant at .05 level), and eachpath loading was greater than twice its associated standarderror (.06–.12). With GFI, NFI, and GFI statistics above .9,AGFI above .8, and RMSEA below .08, Model-RB fit was good(as shown in Table 6). From the results of CFA, we found thatthe results of PCA and CFA are not so well matched. One pos-sible reason is that the model of RB is not stable; anotherreason may be caused by the difference of subject samples.

The covariance matrix of latent variables regarding the RBthat is not displayed in Figure 3 is shown below.

RB1 RB2 RB3 RB4 RB5 RB6

RB1 1RB2 .72 1RB3 .43 .44 1RB4 .17 .13 .38 1RB5 .31 .32 .25 .34 1RB6 .32 .29 .42 .65 .47 1

Correlation behavior

Figure 4 shows the result of the CFA analysis of the correla-tion behavior model (Model-CB). CV for each of its three

sub-constructs was inferred based on the criteria discussedabove. As can be seen from Figure 4, each item’s lambda coef-ficient was above .50 (.50–.80), each path was significant (tvalues are between 7.61 and 14.43, p values are at .05 level),and each path loading was greater than twice its associatedstandard error (.09–.21). With GFI, NFI, and GFI statisticsabove .9, AGFI above .8, and RMSEA below .08, Model-CB fitwas good (as shown in Table 6).

Reliability analysis results

Next, internal consistency reliability analyses were conductedusing Cronbach’s alphas. Table 8 reports results based on thefactor pattern in the CFA. The three behavior constructs werereliable in Nunnally’s (1978) heuristics.

Table 8 Cronbach’s Alphas of Internal Consistency Reliability

Behavior No. of subjects No. of items a

Using behavior 553 9 .71Reflection behavior 553 18 .85Correlation behavior 553 9 .79Trust behavior 553 36 .90

RB1

RB2

RB3

0.75

0.730.85

0.790.800.66

0.660.840.66

1.00

1.00

1.00

0.43 RB1.1

0.28 RB1.2

0.47 RB1.3

0.37 RB2.1

0.36 RB2.2

0.56 RB2.3

0.57 RB3.1

0.29 RB3.2

0.57 RB3.3

0.67 RB4.1

0.33 RB4.2

0.40 RB4.3

0.58 RB5.1

0.46 RB5.2

0.56 RB5.3

0.58 RB6.1

0.45 RB6.2

0.63 RB6.3

RB4

RB5

RB6

0.57

0.780.82

0.65

0.660.74

0.65

0.610.74

1.00

1.00

1.00

Figure 3 The confirmatory factor analysis of reflection behavior. Note: RB1 = bad performance reflection behavior; RB2 = bad performance reflectionbehavior related to context; RB3 = good performance reflection behavior; RB4 = good performance reflection behavior related to context; RB5 = badexperience reflection to context; RB6 = good experience reflection to context.

Yan et al. 651


Correlations of constructs

Table 9 shows the correlations of the sub-constructs compos-ing the UB, the RB, and the CB. We found that the sub-constructs had significant correlations with their rootconstructs at .01 level, which indicates that the sub-constructscan represent their root constructs (or the root constructs aremainly affected by their sub-constructs). The correlationbetween each internal sub-construct (e.g., UB1, UB2, andUB3) and its corresponding root construct (e.g., UB) wasalmost at the same level (except CB3’s correlation with CBwas a bit lower than CB1-CB’s and CB2-CB’s). This correla-tion was also higher than the correlations among the sub-constructs. This indicates that the sub-constructs belongingto a concrete root construct can measure not only the generalaspects but also the specific aspects of the represented type oftrust behavior. The above results show that the current sub-constructs of UB, RB, and CB are feasible and reasonable.

In particular, all three root constructs of trust behaviors(UB, RB, and CB) had correlations with each other at .01level. But these correlations are less significant than their cor-relations with the trust behavior. This indicates that thesethree root constructs can represent not only the generalaspects but also the specific aspects of the trust behavior.

External nomological validation

Based on the theoretical development of relationshipsbetween the trust behavior constructs and other variables,external nomological validity was assessed through the fol-lowing variables: (a) BI; (b) PM; (c) P; and (d) perceived DQ.

The results are shown in Table 10, which also includes thecorrelation coefficients between the external variables and theroot constructs of trust behaviors.We found all expected rela-tionships. Thus, the achieved results showed that the con-structs have adequate external nomological validity.

Discussions

Findings and implications

According to the aforementioned results, a 36-item scale wascreated that measures the UB, the RB, and the CB of the trustbehaviors. Meanwhile, we also developed items for studyingthe influence of external variables on the trust behaviors (asshown in the Appendix).

A trust behavior construct (the trust model) for mobileapplications is achieved based on the above data analysisaccording to the listed criteria with sound reliability (UB:a = .71; RB: a = .85; CB: a = .79; overall trust behavior:a = .90), as shown in Figure 5. The relationships of differentcomponents (i.e., the edge values in Figure 5) are set based onthe correlation analysis. Notably, the mutual correlations ofthe root constructs are around .5, which implies that theseconstructs may influence or impact each other. But theassumed relationships cannot be well proved by internalnomological validity of our experiment and the theories fromthe literature. This means that these factors could be corre-spondingly in parallel, without any causal relationships. Wealso found the influence of a number of external variables(i.e., PM, BI, perceived DQ, and P) on UB, RB, and CB; their

CB1.1

CB1

CB1.2

CB1.3

CB2.1

CB2.2

CB2.3

CB3.1

CB3.2

CB3.3

CB2

CB3

0.64

0.75

0.77

0.76

0.72

0.67

0.51

0.80

0.50

0.59

0.43

0.40

0.43

0.48

0.55

0.74

0.36

0.75

1.00

1.00

1.00

0.75

0.41

0.34

Figure 4 The confirmatory factor analysis of correlation behavior. Note: CB1 = comparison of normal usage behavior; CB2 = comparison related tocontext; CB3 = recommendation behavior.



correlations are also shown in Figure 5. Figure 6 further illus-trates the sub-constructs of the UB, RB, and CB according tothe CFA and correlation analysis.

Based on the achieved results, we proved most of ourhypotheses. Concretely, all hypotheses related to the UB werewell proved, i.e., Hypothesis 1a was proved by UB1; Hypoth-esis 1b was proved by UB2; and Hypothesis 1c was proved byUB3. In addition, Hypothesis 2a was well proved by RB1 andRB3; and Hypothesis 2b was well proved by RB2, RB4, RB5,and RB6. Regarding the CB, all hypotheses were well proved:Hypothesis 3a was proved by CB1; Hypothesis 3b was provedby CB2; and Hypothesis 3c was proved by CB3. In addition,Hypothesis 4a–Hypothesis 4d were verified by externalnomological validation.

We also detected the internal nomological validity of trustbehavior constructs although we cannot find theoreticalsupport on their causal relationships.The detection was basedonourhypothesesduetothe lackof existingtheoreticsupport.First, we set up a model with UB, RB, and CB. The model had abad fit according to the criteria indicated in “Data processingand analysis” section because GFI, AGFI, NFI, CFI, and incre-mental fit index were all below .9, and RMSEA was above .08.We further explored the causal relationships among RB, UB,and CB based on different hypotheses. However, the casualrelationships were not so clear among RB,UB,and CB becauseit lacked sound confirmatory analysis support. This resultindicated that the main three types of trust behavior (UB, RB,and CB) could be correspondingly independent.

In summary, the UB, RB, and CB represent the user’s trustbehaviors. They are further delineated into 12 measurablesub-constructs and relate to a number of external factors. ThePCA, CFA, and reliability analysis showed that the question-naire has positive psychometric properties with respectto model construct validity and reliability. We statisticallyproved the proposed research model.

Practical significance

Exploring such a trust behavior construct (i.e., a conceptualtrust model) has practical significance. First, our model pro-vides a valuable guideline on what kind of user data should bemonitored and collected for the purpose of user trust evalua-tion. Second, applying this model helps us ease the loadof extra human–device interaction that may be requiredby some existing trust management solutions (Yan &Holtmanns, 2008). This is because it is possible to monitorthe trust behavior through an auto-observation mechanismlocated at the mobile device. No additional usability study isneeded if such a trust management solution is deployed basedon this model. Thus, through auto-monitoring users’ trustbehaviors via user–device interactions during applicationconsumption, we can automatically extract useful informa-tion for trust evaluation. In Yan and Yan (2009), weTa

ble

9C

orre

latio

nsof

the

Fact

ors

Com

posi

ngTr

ustB

ehav

iors

UB1

UB2

UB3

UB

RB1

RB2

RB3

RB4

RB5

RB6

RBC

B1C

B2C

B3C

BTB

UB1

1U

B2.2

79**

1U

B3.2

35**

.296

**1

UB

.714

**.7

44**

.690

**1

RB1

1RB

2.5

94**

1RB

3.3

34**

.383

**1

RB4

.132

**.1

04*

.307

**1

RB5

.232

**.2

48**

.209

**.2

68**

1RB

6.2

33**

.223

**.3

31**

.514

**.3

52**

1RB

.561

**.6

63**

.671

**.6

59**

.595

**.5

99**

.676

**1

CB1

1C

B2.5

60**

1C

B3.2

31**

.302

**1

CB

.493

**.5

38**

.798

**.8

25**

.653

**1

TB.7

76**

.897

**.7

78**

1

TB=

trus

tbe

havi

or;

RB=

refle

ctio

nbe

havi

or;

CB

=co

rrel

atio

nbe

havi

or;

UB

=us

ing

beha

vior

.*C

orre

latio

nis

sign

ifica

ntat

the

0.05

leve

l(tw

ota

iled)

.**

Cor

rela

tion

issi

gnifi

cant

atth

e0.

01le

vel

(tw

ota

iled)

.

Yan et al. 653


Table 10 External Nomological Validity and Its Relationship with Trust Behavior with Path Coefficients and Correlation Coefficients

Personal motivation Brand impact Perceived quality Personality

Lambda coefficients(selected value>0.3) basedon CFA

PM.1 .57 BI.1 .63 PQ.1 .58 P.1 .69PM.2 .77 BI.2 .85 PQ.2 .71 P.2 .67PM.3 .32 BI.3 .68 PQ.3 .64 P.3 .73

BI.4 .31 PQ.4 .65 P.4 .59P.5 .45

Pathcoefficients

Correlationcoefficients

Pathcoefficients


Pathcoefficients


Pathcoefficients


Using behavior .00 .264** .15 .348** .21 .342** .38* .386**Reflection behavior .03 .355** .20** .464** .13 .453** .50** .536**Correlation behavior .06 .307** .16* .379** .14 .385** .27 .436**

CFA = confirmatory factor analysis. *p < .05, correlation is significant at the .05 level (two tailed). **p < .01, correlation is significant at the .01 level(two tailed).

Personal

motivation

Brand impact

Perceived quality

Personality

Using behavior

Reflection behavior

Correlation

behavior

.264**

.355**

.307**

.348**

.464**

.379**

.342**

.453**

.385**

.386**

.536**

.436**

.561**

.538**

.493**Trust

Behavior

.776**

.897**

.778**

Figure 5 Trust behavior construct of mobile applications. Note that ** indicates correlation is significant at the .01 level (two tailed).

RB1 BR2 RB3 RB4

Reflection Behavior

(RB)

.663** .671** .659** .595**

.594 ** .383** .307**

.334**

.104 **

.132**

RB5 RB6

.599** .676**

.268** .352**

.209**

.514**

.248**

.331**

.232 **

.223**

.233**

(a)

(c)

(b)

Using Behavior (UB)

UB1 UB2 UB3

.714** .744** .690**

.279** .296**

.235**

Correlation Behavior

(CB)

CB1 CB2 CB3

.798** .825** .653**

.560** .302**

.231**

Figure 6 Internal relationships of (a) using behavior (UB), (b) reflection behavior (RB), and (c) correlation behavior (CB). Note that ** indicates correla-tion is significant at the .01 level (two tailed).



formalized this trust behavior construct with a mathematicmeasure and proposed a computational trust model. Thismodel can be directly used to evaluate an individual user’strust in a mobile application, thus assisting the evaluationand management of the mobile application’s trust in a user-friendly manner. Third, this model is examined through auser study. The trust explanation mechanism based on thismodel could be easily understood and accepted by the users(Yan & Niemi, 2009). Meanwhile, a recommendation from auser or a mobile application service provider can be furtherassessed and explained with this trust behavior construct inorder to help other users in selecting a trustworthy mobileapplication. Therefore, conducting such an empirical studyhelps us explore a trust model based on human behaviors inorder to realize usable trust management in practice.

Limitations and suggestions forfuture research

The formal experiment was conducted in three Chinese uni-versities. There were more than 1,500 students with differ-ent majors who participated in the experiment. But only1,120 responses (71.1%) were valuable and usable. Most ofthe invalid and blank questionnaires were dug out from thesubjects in the Renmin University of China. After theexperiment, we interviewed some of the participants.They commented that some items were so similar andconfusing and they were afraid of providing impreciseanswers.

Some results of PCA and CFA are not so well matched, e.g.,RB1/RB2 and CB1/CB2 were merged as an independent com-ponent in PCA. The main reasons could be the following. (a)The measurement scale is still not stable enough. The CFAresults indicated that the explored model by PCA is notperfect. (b) The selected samples used for PCA and CFA aredifferent. The mismatch of the result could be caused by sam-pling errors. This kind of inconsistency occurred often in pre-vious researches. That is also the reason we need to multi-prove a measurement scale. No matter which reason, weshould further confirm our measurement scale. We may takeadditional samples to repeat CFA if we think CFA results aremore reasonable based on theoretic analysis.

We found that the questionnaire has good convergent andDV regarding the latent sub-constructs within each of theroot constructs. The achieved results were not good enoughregarding the causal relationship assumption. This maybe caused by two reasons. First, the number of variables/principal factors in the trust model was small. We onlyselected three principal variables and other variables may alsocontribute to the model. Second, the path of the trust modelwe identified was possibly not good enough.

The internal nomological validity was examined throughthe causal relationships among the three root constructs of

trust behaviors. In our reported results, the fit of causal rela-tionship model was not good. In the future, we will furtherexplore these causal relationships. If the result is still notgood, it will imply that there are no causal relationships, orthe relationships are in parallel, or not linear.

In our study, we used samples made up of Chinese univer-sity students. Note that China is the biggest mobile phonemarket in the world and it is very common for Chinese uni-versity students to use mobile phones in their routine life(refer to Tables 3 & 4), the samples we adopted have certainuniversality. As students are not representative of the entiremobile user population, the results may not be generalizableto other types of users. Future study would be useful tofurther prove the result with other representative samples. Inaddition, we plan to conduct our empirical study in differentcountries with different cultures in order to explore the cul-tural influence on trust behaviors.

Conclusions

User–application trust is becoming more and more impor-tant for developing and facilitating mobile applicationsand mobile Internet-based services. Studying the trustbehavior greatly helps in explaining trust status becausereal behavior-based explanation is more convincing. In thispaper, we explored a conceptual trust model for mobileapplications based on trust behaviors. This model is a trustbehavior construct achieved from a large-scale user experi-ment. The construct has been examined and proved withsound validity and reliability by PCA, reliability analysis,and CFA. It provides the main factors and constructs oftrust behavior that contribute to the calculation of the user’strust in a mobile application. More significantly in practice,the investigated trust behaviors can be auto-monitored bythe mobile device. Thus, applying our model for trust evalu-ation could greatly reduce the need on explicit human–computer interaction.

Regarding the future work, we plan to continue alongseveral directions. First, we plan to further improve thecurrent measures and conduct further empirical studiesbased on the suggestions discussed in the previous section.Second, we are going to prototype a secure trust evaluatorin a mobile device on the basis of the computationally for-malized trust behavior model with usage privacy preserva-tion. Additionally, we plan to develop a credible and usablereputation system for mobile applications by aggregatingindividual trust calculated based on the model exploredherein.

Acknowledgment

This work is sponsored by the Fundamental Research Fundsfor the Central Universities under Grant No. K5051201032.

Yan et al. 655


The authors thank Prof. Rong Yan for his efforts on projectcoordination and user experiments. The authors would like

to thank the anonymous reviewers’ valuable comments forthe improvement of the paper.

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Appendix

Measures

1. Using behavior (UB)

UB1: normal usage behavior1. The more times you use the messaging, the more you

trust it.2. The more frequently you use the messaging, the more you

need it.3. The longer time you use the messaging, the more you trust

it.UB2: behavior related to context1. You do more important tasks through the messaging if you

trust it more.2. You do more risky tasks through the messaging if you trust

it more (e.g., SMS payment).3. You do more urgent tasks through the messaging if you

trust it more.UB3: feature-related usage behavior1. You would try more features of the messaging if you trust

it more.2. After trying more features of the messaging, you gain more

expertise on it.3. Good quality of the messaging would encourage you to try

new features of it.

2. Reflection behavior (RB)

RB1: bad performance reflection behavior1. You could decrease the times of using the messaging due to

its bad performance.2. Your usage interest and usage frequency could be

decreased due to the bad messaging performance.3. You could decrease the time of using the messaging due to

its bad performance.RB2: bad performance reflection behavior related tocontext1. Bad performance of the messaging could discourage you

to do important things with it.2. Bad performance of the messaging could discourage you

to do highly risky things with it.3. Bad performance of the messaging could discourage you

to do urgent things with it.RB3: good performance reflection behavior1. You could increase the time of using the messaging due to

its good performance.2. You could increase the times of using the messaging due to

its good performance.3. Your usage interest and usage frequency could be

increased due to the good messaging performance.RB4: good performance reflection behavior related tocontext1. Good performance of the messaging could encourage you

to do highly risky things with it.



2. Good performance of the messaging could encourage youto do important things with it.

3. Good performance of the messaging could encourage youto do urgent things with it.

RB5: bad experience reflection to context1. After very bad experiences of using the messaging, you

could use it to do less risky task.2. After very bad experiences of using the messaging, you

could use it to do less important task.3. After very bad experiences of using the messaging, you

could use it to do less urgent task.RB6: good experience reflection to context1. After very good experiences of using the messaging, you

could use it to do more risky tasks.2. After very good experiences of using the messaging, you

could use it to do more important tasks.3. After very good experiences of using the messaging, you

could use it to do more urgent tasks.

3. Correlation behavior (CB)

CB1: comparison of normal usage behavior1. Using the messaging more times than another

similarly functioned mobile application means you trustit more.

2. Using the messaging more frequently than another simi-larly functioned mobile application means you trust itmore.

3. Spending more time in using the messaging than anothersimilarly functioned mobile application means you trust itmore.

CB2: comparison related to context1. Using the messaging, not another similarly functioned

mobile application, to fulfill a more important task meansyou trust it more.

2. Using the messaging, not another similarly functionedmobile application, to fulfill a more risky task means youtrust it more.

3. Using the messaging, not another similarly functionedmobile application, to fulfill a more urgent task means youtrust it more.

CB3: recommendation behavior1. If you have very good experiences in using the messaging,

you generally would like to recommend it.2. For two similarly functioned messaging applications, you

trust more in the one you would like to recommend.3. After very bad experiences in using the messaging, you

generally don’t want to recommend it.PM: personal motivation1. A mobile phone is very important in your life.2. You often use a mobile phone to transfer important

information.3. Because a mobile phone is important to you, you will con-

tinuously use it in spite of having some problems.BI: brand impact1. You like a mobile application developed by a famous

vendor.2. You like using a mobile phone with a famous brand.3. You would like to recommend a mobile application devel-

oped by a famous vendor.4. For a mobile application developed by an infamous

vendor, you will cautiously use it or stop using it if youmeet some problems.

DQ: perceived device quality1. Generally speaking, the phone you are using is working

well.2. Using a mobile phone for you is easy and convenient.3. Using a mobile phone is economical and useful for you.4. Using a mobile phone can satisfy your personal demands

well.P: Personality1. In your opinion, mobile applications have a promising

future.2. You believe the mobile application will be continuously

improving and upgrading.3. You have confidence on the future of mobile phone usage.4. You think the mobile phone is a personal communicator

liked by most people.5. You think the mobile applications benefit your life and

study very much.

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exploring trust of mobile applications based on user behaviors: an empirical study

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