The Model of Trust Factors in Paying through the Internet

(Dissertation)

Franc Bračun, PhD

Merkur Day 2004

Friday, 22nd October

The Context of Research

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 e-

commerce 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)

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 e-services

Conceptualizations of the effect of trust and risk on behavioral intentions tend to be inconclusive.

The Problem (external factors)

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

One of the possible variables– 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

Utilitarian (positive) consequences (e.g, perceived usefulness) of technology use

– 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 …

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

Behavioral uncertainty

Environmental uncertainty

Possible negative consequences

Perceived post-transaction control

Perceived security

Trust propensity

Risk propensity

Party trust

Trust in a third party

H13

H11

H14

H6

H2

H12

H17

H8

H16

H9

H7

H5

H4

H3

H10

H15

Perception of the degree of risk

H1 Confidence Willingness

H20 H19

H18

Research Methodology

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)

Estimation of the model resulted in a good overall fit – for consumers:

2 = 732.269, df = 474, p < .001; RMSEA = .049, P = .628; CFI = .988; NFI = .967; NNFI/TLI = .986; IFI = .988

– for merchants 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)

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)

Covariance-based Structural Equation Modeling Results for Consumer Sample

Behavioral uncertainty

SMC = 0.24

Environmental uncertainty

SMC = 0.30

Possible negative consequences

SMC = 0.30

Perceived post-transaction control

SMC = 0.69

Perceived security

SMC = 0.38

Trust propensity

Risk propensity

Party trust

SMC = 0.22

Trust in a third party

SMC = 0.10

0.19**

- 0.24**

0.23**

-0.05

0.56**

Confidence

SMC = 0.46

Willingness

SMC = 0.43

0.66**

- 0.17**

0.37**

-0.30**

- 0.23**

0.34**

0.31**

- 0.18*

0.41**

0.40**

0.31**

- 0.17*

- 0.34**

- 0.56**

0.38**

* Significant at the p < 0,05 level ** Significant at the p < 0,01 level

Perception of the degree of risk

Results (cont.)

Covariance-based Structural Equation Modeling Results for Merchant Sample

Behavioral uncertainty

SMC = 0.24

Environmental uncertainty

SMC = 0.41

Possible negative consequences

SMC = 0.21

Perceived post-transaction control

SMC = 0.79

Perceived security

SMC = 0.47

Trust propensity

Risk propensity

Party trust

SMC = 0.33

Trust in a third party

SMC = 0.12

0.09

- 0.31*

0,00

- 0.26**

0.57**

Confidence

SMC = 0.51

Willingness

SMC = 0.23

0.48**

- 0.17

0.69**

- 0.28**

- 0.13

0.12

0.35**

- 0.19

0.40**

0.35**

0.29**

- 0.25*

- 0.31**

- 0.53**

0.53**

* Significant at the p < 0,05 level ** Significant at the p < 0,01 level

Perception of the degree of risk

The first observation is that confidence reveals a significant and strong relationship with willingness to transact

confidence willingness

Results revel complex interrelationship among trust, perceived risk and confidence;

trust perceived risk confidence

Findings

Implications for Theory and Research

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.)

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

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

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

?