The Model of Trust Factors in
Paying through the Internet
(Dissertation)
Friday, 22nd October
Franc Bračun, PhD
Merkur Day 2004
The Context of Research
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An exchange of consumer’s money for the merchant’s goods or
services
An electronic payment
A critical factor supporting innovative processes in electronic
commerce
Becoming increasingly important in the context of B2C ecommerce
Research is needed to gain a better understanding of the
behavior of online consumers in the context of B2C e-payments
The Problem (behavioral beliefs)
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Perceived usefulness and perceived ease of use
(advanced by Davis and his colleagues - TAM) have
been identified as key factors explaining and
predicting a consumer’s intention to use the Internet
as a medium for purchase
The role of trust in reducing perception of risks and
the influence of risk and trust on adoption of eservices
Conceptualizations of the effect of trust and risk on
behavioral intentions tend to be inconclusive.
The Problem (external factors)
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In the context of TAM all other factors not explicitly included in the
model are posited to be completely mediated by perceived usefulness
and perceived ease of use
Recent studies found evidence of a direct relationship between trust
and behavioral intention
Trust and perceived risk are not defined as a set of beliefs about one’s
own actions (e.g., engaging in a transaction with the other party) and
the outcomes of this behavior
A set of specific beliefs about the intentions and behavior of the other
party
A direct influence of trust and perceived risk on one’s behavioral
intentions is, from a TRA and TAM perspective, a surprising finding.
Additional explanatory variables are needed to explain the effect of risk
and trust on behavioral intentions
An Additional Explanatory Variable
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One of the possible variables
–
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confidence
Confidence is the individual’s assumption that
the outcome he desires, rather than the outcome
he fears, will occur
An outcome expectation is “a judgment of the likely
consequences [one’s own] behavior will produce”
(Bandura 1986, p. 391).
Utilitarian and Adversative Consequences of Using
the Internet
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Utilitarian (positive) consequences (e.g, perceived
usefulness) of technology use
–
–
–
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Increased effectiveness
Efficiency
Convenience
Undesirable consequences are the adverse effects of using
the technology such as the loss of important data or money
In regulating their behavior, people adopt courses of action that
are likely to produce desirable outcomes and generally discard
those that can lead to undesirable outcomes
User acceptance intention can be influenced by beliefs about
the adversative consequences of using new technologies.
Confidence toward …
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In the context of uncertain environment users tend to use or not
use new technologies primarily to the extent they expect it will
not result in adverse consequences
This expectation is confidence toward using new technologies.
Confidence toward using new technologies is defined here
as the extent to which users expect that using new
technologies in conducting transactions will not result in
adverse consequences
This definition reflects an aspect of expectancy – that is,
confidence is an expectation of a nonnegative outcome of
engaging in an interaction characterized by uncertainty
Research Model
Perception of the
degree of risk
H8
Party trust
Behavioral
uncertainty
H13
H15
H9
H14
Trust propensity
Environmental
uncertainty
H11
H2
H3
H10
H1
H20
H19
H18
Perceived security
Confidence
H12
Possible negative
consequences
H17
Risk propensity
H16
H7
Trust in a third
party
H6
Perceived posttransaction control
H4
H5
Willingness
Research Methodology
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A cross-sectional field study via a questionnaire
Prior to administering a large-scale survey, both instruments
have been refined by a pretest and pilot tested
Out of 1,889 e-mails sent to the Internet users, 232 of
responses were adequate. A total of 346 questionnaires was
sent via postal mail to company managers in charge of
accounting or finance. The final sample of 132 (a response rate
of 38.2%) responses was used in the analysis.
The research model was tested using covariance-based
Structural Equation Modeling (SEM)
Results (goodness-of-fit)
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Estimation of the model resulted in a good overall fit
–
for consumers:
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–
for merchants
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2 = 732.269, df = 474, p < .001; RMSEA = .049, P = .628; CFI
= .988; NFI = .967; NNFI/TLI = .986; IFI = .988
2 = 610.148, df = 474, p < .001; RMSEA = .047, P = .680; CFI
= .986; NFI = .941; NNFI/TLI = .983; IFI = .986
It can be concluded that the model obtained
adequate degrees of fit for both samples
Results (the Measurement Model)
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Composite reliability of the measures included in
the model is supported; ranging from .81 to .96, and
exceeding minimum value of .60.
Convergent validity is also supported; all loadings
are highly statistically significant (p < .01) and the
factor regression coefficients (R2) exceed the
recommended value .50
Discriminant validity is supported; the square root
of the AVE of each construct is larger than its
correlations with the other constructs
Results (the Structural Model for
Consumer Sample)
Perception of the degree
of risk
- 0.23**
Party trust
Behavioral
uncertainty
SMC = 0.24
SMC = 0.22
- 0.24**
0.38**
- 0.18*
0.19**
Environmental
uncertainty
-0.05
Trust propensity
SMC = 0.30
0.23**
0.34**
- 0.17**
- 0.34**
- 0.56**
Confidence
0.40**
Perceived security
0.66**
Willingness
SMC = 0.43
SMC = 0.46
SMC = 0.38
0.37**
-0.30**
Risk propensity
Possible negative
consequences
- 0.17*
0.31**
SMC = 0.30
0.31**
0.41**
Trust in a third party
SMC = 0.10
0.56**
Perceived posttransaction control
SMC = 0.69
* Significant at the p < 0,05 level
** Significant at the p < 0,01 level
Covariance-based Structural Equation Modeling Results for Consumer Sample
Results (cont.)
Perception of the degree
of risk
- 0.13
Party trust
Behavioral
uncertainty
SMC = 0.24
SMC = 0.33
- 0.31*
0.53**
0.09
- 0.19
Environmental
uncertainty
- 0.26**
Trust propensity
0,00
0.12
- 0.53**
SMC = 0.41
Perceived security
- 0.17
- 0.31**
Confidence
0.35**
SMC = 0.47
0.48**
SMC = 0.51
Willingness
SMC = 0.23
0.69**
- 0.28**
Risk propensity
Possible negative
consequences
- 0.25*
0.29**
SMC = 0.21
0.35**
0.40**
Trust in a third party
SMC = 0.12
0.57**
Perceived posttransaction control
SMC = 0.79
* Significant at the p < 0,05 level
** Significant at the p < 0,01 level
Covariance-based Structural Equation Modeling Results for Merchant Sample
Findings
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The first observation is that confidence reveals a
significant and strong relationship with willingness to
transact
confidence  willingness
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Results revel complex interrelationship among trust,
perceived risk and confidence;
trust  perceived risk  confidence
Implications for Theory and
Research
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This study introduces a new variable that
predicts willingness – i.e., the notion of
confidence toward performing payments via
the Internet (a variety of an outcome
expectation)
The present study reformulated the meaning
of trust by explicitly introducing different
types of trust according to different bases of
expectations
Implications for Theory and
Research (cont.)
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Another important implication of this study is the
causal relationship between trust and perceived risk
The examination of structural influences on causal
links:
–
–
–
‘party trust  behavioral uncertainty  confidence’
‘trust propensity  environmental uncertainty’
‘party trust  perceived security  behavioral
uncertainty’
is a promising field of future research
Implications for Practice
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Managers responsible for implementing new
technologies must design Web sites to communicate
trustworthiness through the Web interface
Managers should make thoughtful choices about
which trust-mark seal and/or logo to use
The implementation of technical security measures
on its own does not diminish concerns regarding
security of electronic payment transactions
Limitations of the Study
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The present study is of a cross-sectional nature and since no
experimental research has been conducted, no definite conclusions
can be drawn concerning the causality of the relationships in the
conceptual model
Study data have been collected from consumers and merchants using
a questionnaire survey administered in Slovenia
It is possible to identify additional factors that influence willingness to
transact; for example, one could integrate the model proposed in the
present study in TAM
Future research could examine the differences between experienced
and novice consumers as well as the difference between experienced
and novice on-line merchants
The study focused on Internet payments
Questions
?