The document summarizes a research presentation on consumer satisfaction and repurchase intention from cross-border e-commerce. The study examines how consumer trust expectations, risk expectations, and disconfirmation of expectations influence post-purchase trust, risk perception, and repurchase intention. A conceptual model is developed based on expectation-disconfirmation theory and previous literature. Survey data is collected from 440 Thai e-commerce consumers and analyzed using structural equation modeling. The results show that pre-purchase expectations significantly impact post-purchase perceptions and that domestic and cross-border expectations differ. The study contributes to understanding consumer decision-making in cross-border e-commerce contexts.

1. CONSUMER SATISFACTION
AND REPURCHASE INTENTION
FROM CROSS-BORDER E-COMMERCE:
A TRUST-RISK-BASED STUDY
International Conference of Electronic Business (ICEB) 2019
8-12 December 2019, Newcastle upon Tyne, United Kingdom
Author & Presenter: Natthakorn Khayaiyam

2. About the author
AGENDA
Research Introduction
Methodology
Result
e-Commerce and Cross-border e-Commerce
Cause of study
Literatures review and conceptual model
Sample, population, and research tools
Structural Model & Fit Indices
Theoretical and Practical Contribution
Limitation and Recommendation
2

3. ABOUT
THE AUTHOR
International Conference of Electronic Business (ICEB) 2019
8-12 December 2019, Newcastle upon Tyne, United Kingdom
Author & Presenter: Natthakorn Khayaiyam

4. Mr. Natthakorn Khayaiyam (Pete)
AUTHOR
Management Information System Programme,
Faculty of Commerce and Accountancy,
Thammasat University, Thailand.
Master of Science
in Management Information System (2019)
English Language Department,
Faculty of Liberal Arts,
Thammasat University, Thailand.
Bachelor of Arts
in English (2014)
Education Background
Data Scientist (2017 - 2019)
Data Engineer (2018 - 2019)
Business Analyst (2016 - 2017)
Data Scientist
Krungthai-AXA Life Insurance (2016 - Present)
Cash Management Products
Implementation.
Implementation & Services
Deutsche Bank A.G., Bangkok (2016 - 2017)
Work Experience
Data Analysis and business
dashboard development.
Data Specialist & Client Reporting
Market Pulse Internation (2013 - 2016)
4

5. INTRODUCTION
International Conference of Electronic Business (ICEB) 2019
8-12 December 2019, Newcastle upon Tyne, United Kingdom
Author & Presenter: Natthakorn Khayaiyam

6. INTRODUCTION:
E-COMMERCE &
CROSS-BORDER E-COMMERCE
International Conference of Electronic Business (ICEB) 2019
8-12 December 2019, Newcastle upon Tyne, United Kingdom
Author & Presenter: Natthakorn Khayaiyam

7. Global E-commerce
E-COMMERCE & CROSS-BORDER E-COMMERCE
7
Market
Value
Globally
$ 3 Bn
In 2017
Source: Euromonitor,
2018.
Growth
Rate
Annually
20%
Source: DHL Express,
2016
Thailand E-commerce
Growth Rate
Thailand
14%
Source: U.S. Department of
Commerce Manages Export,
2018
Market Value
Thailand
$ 4,239 M
Source: Priest, 2017
Amazon
Alibaba
JD
Other

8. INTRODUCTION:
CAUSE OF STUDY
International Conference of Electronic Business (ICEB) 2019
8-12 December 2019, Newcastle upon Tyne, United Kingdom
Author & Presenter: Natthakorn Khayaiyam

9. CAUSE OF STUDY
“Studying on Thai Population Is Interesting Due to a High
Growth in Both Digital Commerce Adopters and Thailand
Is Part of South East Asia Region Which Is a Fast-Growing
Region in E-Commerce Market” - Priest, 2017.
“Cross-Border E-Commerce Has Received Relatively Less
Research Attention Than in Domestic E-Commerce
Context While the Business Has Been Changing Very
Fast.” - Xiao, Wang, & Liu, 2018.
9

10. INTRODUCTION:
RESEARCH QUESTIONS
International Conference of Electronic Business (ICEB) 2019
8-12 December 2019, Newcastle upon Tyne, United Kingdom
Author & Presenter: Natthakorn Khayaiyam

11. RESEARCH QUESTIONS
11
• Do consumer trust expectation and risk expectation influence consumer disconfirmation and later trust and risk
perception towards cross-border e- commerce or not?
• Do disconfirmation of consumer trust and risk expectation, consumer satisfaction, post-purchase trust and risk
perception influence repurchase intention from cross-border e-commerce or not?
• Does consumer trust affect perceived risk at the pre-purchase stage as well as post-purchase stage or not?
• Do consumers expect trust and risk from cross-border e-commerce sellers and domestic e-commerce sellers
differently across the pre-purchase and post-purchase stage or not?

12. METHODOLOGY
International Conference of Electronic Business (ICEB) 2019
8-12 December 2019, Newcastle upon Tyne, United Kingdom
Author & Presenter: Natthakorn Khayaiyam

13. METHODOLOGY:
LITERATURES REVIEW
AND CONCEPTUAL MODEL
International Conference of Electronic Business (ICEB) 2019
8-12 December 2019, Newcastle upon Tyne, United Kingdom
Author & Presenter: Natthakorn Khayaiyam

14. Trust and Risk Theory
Perceived Risk
Risks in context of e-business cab be classified into 7 facets
which are performance risk, financial risk, time risk, psychological
risk, social risk, privacy risk, and overall risk (Featherman &
Pavlou, 2003).
Trust
Trust has been addressed in many previous researches
with vast definition and stages (Bonsón Ponte, Carvajal-
Trujillo, & Escobar-Rodríguez, 2015).
Several theories regarding trust are diverse into many
stages of the interaction between trustee and trustor
(Stouthuysen, Teunis, Reusen, & Slabbinck, 2018).
THEORY AND PREVIOUS STUDY
Featherman and Pavlou’s Risk Facets (Featherman & Pavlou, 2003)

15. THEORY AND PREVIOUS STUDY
Expectation-disconfirmation Theory
Expectation-Confirmation Decision Theory (Oliver, 1980)
Expectation-disconfirmation Theory
Trust-based Consumer Decision Making Model (Kim et al., 2008)

16. Information System Trust with Expectation-disconfirmation Theory (N. Lankton, McKnight, & Thatcher, 2014)
PREVIOUS THEORETICAL FRAMEWORK
1616

17. Conceptual Model Path in SEM for cross-border e-commerce constructs
Post-purchasePre-purchase
CONCEPTUAL FRAMEWORK
17
Trusting
Expectation
Perceived Risks
Expectation
Trusting
Performance
Expectation
Disconfirmation
Perceived Risks
Performance
Satisfaction
Repurchase
Intention
H1(-)
H2(+)
H6(+)
H7(-)
H3(-)
H8(-)
H5(-)
H4(+)
H9(+) H10(+)

18. METHODOLOGY:
SAMPLE, POPULATION,
AND RESEARCH TOOLS
International Conference of Electronic Business (ICEB) 2019
8-12 December 2019, Newcastle upon Tyne, United Kingdom
Author & Presenter: Natthakorn Khayaiyam

19. SAMPLE, POPULATION, AND TOOLS
19
Thailand
E-Shopper
Population
12.1 M
Source: Statista.Com
Research Tools
Data Collection
‣ Questionnaire Form (print-out version).
‣ Survey Monkey (for e-survey development and
distribution).
‣ Facebook, Line (for e-servey distribution).
Data Analysis
‣ R Statistics Package for Power Analysis and
Exploratory Analysis.
‣ IBM SPSS and IBM AMOS for Path Analysis.
416
Minimum Samples
Based on Power Analysis
(U = 6, F 2 = 0.05, Power Test
(B) = 0.95, Significant Level (a)
=0.05)

20. SAMPLE DEMOGRAPHY
20
Eligible
Samples
440
Experienced CBEC
Purchase

21. RESULT AND CONTRIBUTION
International Conference of Electronic Business (ICEB) 2019
8-12 December 2019, Newcastle upon Tyne, United Kingdom
Author & Presenter: Natthakorn Khayaiyam

22. Post-purchasePre-purchase
PATH DIRECT EFFECT
22
Trusting
Expectation
Perceived Risks
Expectation
Trusting Performance
Expectation
Disconfirmation
Perceived Risks
Performance
Satisfaction
Repurchase
Intention
22
0.589 **
-0.300 **
Note. 𝜒2= 518.700; DF = 328; R2 = 0.642;
Standardised Root Mean Square Residuals (SRMR) = 0.030;
Root Mean Square Error of Approximation (RMSEA) = 0.037;
Normed Fit Index (NFI) = 0.901;
Non-normed Fit Index (TLI) = 0.955;
Comparative Fit Index (CFI) = 0.960.
0.177
-0.625 ** 0.585 **
-0.436 **
-0.08
-0.04
0.80 ** 0.51 **
R2 = 0.642
Significant Path
Non-significant Path

23. GOODNESS OF FIT INDEX
23
Fit Index Criteria Model Result Model Fit Acceptance
Comparative Fit Index
NFI NFI > 0.95 0.901 Not Accepted
TLI TLI > 0.95 0.955 Accepted
CFI CFI > 0.95 0.960 Accepted
Other Fit Index
SRMR SRMR < 0.08 0.055 Accepted
RMSEA RMSEA < 0.07 (RMSEA < 0.03 excellent fit.) 0.037 Accepted
Combination Criteria
NNFI (TLI) and SRMR NNFI (TLI) 0.96 and SRMR 0.09 Not Accepted
RMSEA and SRMR RMSEA and SRMR 0.09. Accepted
CFI and SRMR CFI 0.96 and SRMR 0.09 Accepted
Recommendation of Evaluation of Goodness of Fit using Index Combination (Hu & Bentler, 1999)

24. Paired Differences
t df
Sig. (2-
tailed)Mean
Std.
Deviation
Std. Error
Mean
95% Confidence Interval
of the Difference
Lower Upper
Pair 1
Cross-border
vs
Domestic
Trusting Expectation -0.260 0.613 0.029 -0.317 -0.202 -8.898 439 .000**
Pair 2 Perceived Risk Expectation -0.138 0.765 0.036 -0.209 -0.066 -3.784 439 .000**
Pair 3 Trusting Performance -0.193 0.649 0.030 -0.254 -0.132 -6.262 439 .000**
Pair 4 Perceived Risk Performance 0.007 0.970 0.046 -0.082 0.098 0.172 439 0.864
Pair 5 Pre Purchase
vs
Post Purchase
Trusting Belief -0.018 0.488 0.023 -0.063 0.027 -0.780 439 0.436
Pair 6 Perceived Risk 0.178 0.658 0.031 0.116 0.240 5.681 439 .000**
Note. CBTRE = Cross-border Trusting Expectation, CBPRE = Cross-border Perceived Risk Expectation,
CBTRP = Cross-border Trusting Performance, CBPRP = Cross-border Perceived Risk Performance,
DMTRE = Domestic Trusting Expectation, DMPRE = Domestic Perceived Risk Expectation,
DMTRP = Domestic Trusting Performance, DMPRP = Domestic Perceived Risk Performance
PAIRED T-TEST FOR MEAN DIFFERENCE
Expect insignificant in the mean difference.

25. THEORETICAL AND PRACTICAL CONTRIBUTION
25
Theoretical
• Expand the EDT to incorporate risks and trust
together.
• Using EDT in the context of cross-border e-
commerce.
• The expectation constructs do not have both direct
and indirect impact on Expectation-disconfirmation
as in the previous study.
Practical
•Domestic e-sellers competitiveness in increasing Trust and
reducing risk to differentiate themselves to CBEC sellers.
•This model and analysis can be applied on business as
BAU evaluation.

26. Limitation
LIMITATION AND RECOMMENDATION
26
• This study was conducted on Thai samples and
population only.
• Risks in this study are evaluated as a general
perception in combination of financial risk, delivery
risk, and overall risk.
• This study measures the pre- purchase expectation
as consumer recall of their past-experience rather
than measuring before the actual purchase.
Recommendation
• The perceived risk in this study may be separated
and emphasise independently such as financial risk,
performance risk (product defection or bogus), and
overall risk.
• Conduct longitudinal study separating the pre-
purchase and post-purchase measurement
conducting on the same samples.

27. THANK YOU
International Conference of Electronic Business (ICEB) 2019
8-12 December 2019, Newcastle upon Tyne, United Kingdom
Author & Presenter: Natthakorn Khayaiyam

28. REFERENCE
28
Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: a perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4),
451-474. doi:10.1016/S1071-5819(03)00111-3
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in Online Shopping: An Integrated Model. MIS Quarterly, 27(1), 51-90. doi:10.2307/30036519
Kim, D. J. (2014). A Study of the Multilevel and Dynamic Nature of Trust in E- Commerce from a Cross-Stage Perspective. International Journal of Electronic Commerce,
19(1), 11-64. doi:10.2753/jec1086-4415190101
Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents.
Decision Support Systems, 44(2), 544-564. doi:10.1016/j.dss.2007.07.001
Kim, D. J., Ferrin, D. L., & Rao, H. R. (2009). Trust and Satisfaction, Two Stepping Stones for Successful E-Commerce Relationships: A Longitudinal Exploration. Information
Systems Research, 20(2), 237-257. doi:10.1287/isre.1080.0188
Lankton, N., McKnight, D. H., & Thatcher, J. B. (2014). Incorporating trust-in-technology into Expectation Disconfirmation Theory. The Journal of Strategic Information
Systems, 23(2), 128-145. doi:10.1016/j.jsis.2013.09.001
Lankton, N. K., McKnight, D. H., Wright, R. T., & Thatcher, J. B. (2016). Research Note— Using Expectation Disconfirmation Theory and Polynomial Modeling to Understand
Trust in Technology. Information Systems Research, 27(1), 197- 213. doi:10.1287/isre.2015.0611
McKnight, D. H., Carter, M., Thatcher, J. B., & Clay, P. F. (2011). Trust in a specific technology. ACM Transactions on Management Information Systems, 2(2), 1- 25.
doi:10.1145/1985347.1985353
McKnight, D. H., & Chervany, N. L. (2014). What Trust Means in E-Commerce Customer Relationships: An Interdisciplinary Conceptual Typology. International Journal of
Electronic Commerce, 6(2), 35-59. doi:10.1080/10864415.2001.11044235
McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and Validating Trust Measures for e-Commerce: An Integrative Typology. Information Systems Research,
13(3), 334-359. doi:10.1287/isre.13.3.334.81
Mou, J., Shin, D.-H., & Cohen, J. F. (2015). Trust and risk in consumer acceptance of e- services. Electronic Commerce Research, 17(2), 255-288. doi:10.1007/s10660-
015-9205-4
Stouthuysen, K., Teunis, I., Reusen, E., & Slabbinck, H. (2018). Initial trust and intentions to buy: The effect of vendor-specific guarantees, customer reviews and the role of
online shopping experience ☆. Electronic Commerce Research and Applications, 27, 23-38. doi:10.1016/j.elerap.2017.11.002

29. APPENDIX

30. APPENDIX
Construct Measurement Item Factor Loading 𝛼 AVE CR
Cross-border Perceived Risk Expectation
(CBPRE)
CBPRE4 0.674 0.839 0.730 0.862
CBPRE3 0.701
CBPRE2 0.781
CBPRE1 0.765
Cross-border Trusting Performance
(CBTRP)
CBTRP1 0.747 0.778 0.691 0.910
CBTRP2 0.678
CBTRP3 0.635
CBTRP4 0.702
Cross-border Trusting Expectation
(CBTRE)
CBTRE4 0.669 0.721 0.630 0.876
CBTRE3 0.628
CBTRE2 0.652
CBTRE1 0.571
Cross-border Perceived Risk Performance
(CBPRP)
CBPRP1 0.654 0.807 0.714 0.881
CBPRP2 0.593
CBPRP3 0.919
CBPRP4 0.688
Expectation Disconfirmation
(DCE)
DCE4 0.773 0.868 0.785 0.938
DCE3 0.768
DCE2 0.780
DCE1 0.820
Satisfaction
(SAT)
SAT4 0.751 0.813 0.699 0.881
SAT3 0.698
SAT2 0.618
SAT1 0.730
Repurchase Intention
(CON)
CON1 0.715 0.804 0.712 0.896
CON2 0.712
CON3 0.674
CON4 0.748
Note: = Cronbach’s Alpha; AVE = Average Variance Extracted; CR = Construct Validity

31. APPENDIX
No. Hypothesis Result
H1 Trusting Expectation from cross-border e-commerce has negative effect on Perceived Risk Expectation from
cross-border e-commerce.
Not Supported
H2 Trusting Expectation from cross-border e-commerce has positive effect on Expectation Disconfirmation
from cross-border e-commerce.
Not Supported
H3 Perceived Risk Expectation from cross-border e-commerce has negative effect on Expectation Confirmation
from cross-border e-commerce.
Not Supported
H4 Trusting Performance from cross-border e-commerce has positive effect on Expectation Confirmation from
cross-border e-commerce.
Supported
H5 Perceived Risk Performance from cross-border e-commerce has negative effect on Expectation
Confirmation from cross-border e-commerce.
Supported
H6 Trusting Expectation has positive effect on Trusting Performance. Supported
H7 Perceived Risk Expectation has negative effect on Perceived Risk Performance. Supported
H8 Trusting Performance has negative effect on Perceived Risk Performance Supported
H9 Expectation Disconfirmation has positive effect on Satisfaction. Supported
H10 Satisfaction has positive effect on Repurchase Intention. Supported

32. APPENDIX
Observed
Variable
Latent
Construct
𝛼 B S.E. C.R. SMC P-value
CBPRE4 CBPRE 0.674 1.000 0.447
CBPRE3 0.701 1.088 0.082 13.282 0.394 ***
CBPRE2 0.781 1.481 0.139 10.656 0.425 ***
CBPRE1 0.765 1.369 0.130 10.516 0.326 ***
CBTRP1 CBTRP 0.747 1.000 0.558
CBTRP2 0.678 1.084 0.085 12.780 0.459 ***
CBTRP3 0.635 1.182 0.098 12.030 0.404 ***
CBTRP4 0.702 1.135 0.086 13.198 0.493 ***
CBPRP1 CBPRP 0.654 1.000 0.414
CBPRP2 0.593 0.965 0.067 14.417 0.356 ***
CBPRP3 0.919 1.489 0.120 12.450 0.847 ***
CBPRP4 0.688 0.950 0.094 10.152 0.445 ***
CON1 CON 0.715 1.000 0.474
CON2 0.712 1.033 0.082 12.582 0.505 ***
CON3 0.674 0.930 0.077 12.049 0.439 ***
CON4 0.748 1.038 0.080 13.001 0.524 ***
Observed
Variable
Latent
Constru
ct
𝛼 B S.E. C.R. SMC P-value
DCE4
DCE
0.773 1.000 0.598
DCE3 0.768 1.020 0.063 16.218 0.589 ***
DCE2 0.780 0.968 0.059 16.537 0.609 ***
DCE1 0.820 0.927 0.052 17.659 0.672 ***
SAT4
SAT
0.751 1.000 0.564
SAT3 0.698 0.998 0.076 13.090 0.487 ***
SAT2 0.618 0.901 0.081 11.182 0.382 ***
SAT1 0.730 1.070 0.079 13.596 0.533 ***
CBTRE4 CBTRE 0.669 1.000 0.447
CBTRE3 0.628 0.957 0.097 9.894 0.488 ***
CBTRE2 0.652 0.918 0.091 10.125 0.617 ***
CBTRE1 0.571 0.616 0.067 9.264 0.586 ***
Note. = Standardized Coefficient Estimate; B = Unstandardized
Coefficients; S.E. = Standard Error; P = p-value; C.R. = Critical Ratio (t-
value); SMS = Squared Multiple Correlation
Confirmatory Factor Analysis Coefficients and Squared Multiple Coefficients.

33. APPENDIX
CBTRE CBPRE CBTRP CBPRP DIS SAT CON
CBTRE 1
CBPRE .512** 1
CBTRP 0.083 -.191** 1
CBPRP .189** -0.090 .431** 1
DIS -.115* -.389** .282** .475** 1
SAT -0.006 -.256** .162** .439** .636** 1
CON -.120* -.168** .123** .192** .335** .222** 1
Note. CBTRE = Trusting Expectation; CBPRE = Perceived Risk Performance; CBTRP = Trusting Performance; CBPRP =
Perceived Risk Performance; DIS = Disconfirmation; SAT = Satisfaction; CON = Repurchase Intention
Cross-border E-commerce Variable Correlations for Discriminant Validity