Understanding Consumer Buying Intention of E-Commerce Airfares Based on Multivariate Demographic Segmentation: A Multigroup Structural Equation Modeling Approach
Abstract
:1. Introduction
2. Literature Review
2.1. The Technology Acceptance Model
2.1.1. Perceived Usefulness
2.1.2. Perceived Ease of Use
2.1.3. Price Sensitivity
2.1.4. Hedonic Motivation
2.1.5. Behavioral Intention
2.2. Generations and E-Commerce Airfares
3. Materials & Methods
3.1. Sampling and Data Collection
3.2. Data Analysis
4. Results
4.1. Step 1: Cluster Analysis
4.2. Step 2: Measurement Model (CFA)
4.3. The Goodness of Fit (GOF)
4.4. Convergent Validity
4.5. Discriminant Validity
4.6. Step 3: Structural Model
4.7. Step 4: Multigroup Moderation Analysis
5. Discussion
5.1. Research Implications
5.2. Research Limitations and Future Research
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
- Yes
- No
- (1)
- Gender: a. Male b. Female
- (2)
- Generation: a. Gen X (born 1960–1979) b. Gen Y (born 1980–1994) c. Gen Z (born 1995–2010)
- (3)
- The income per month: a. less than 25,000 baht b. more than 25,000 baht
- (1)
- Perceived usefulness
- I find airline company e-commerce websites or online travel agencies’ websites very useful in the purchasing process.
- Using airline company e-commerce websites or online travel agencies’ websites helps me accomplish things more quickly in the purchasing process.
- I can save time when I use airline company e-commerce websites or online travel agency websites in the purchasing process.
- (2)
- Perceived ease of use
- The airline website or online travel agency websites are easy to use and simple to use.
- It is easy for me to become skillful at using airline company e-commerce websites or online travel agent websites.
- Using airline websites or online travel agency websites helps me purchase an airline ticket more conveniently.
- (3)
- Price sensitivity
- I can save money by examining the prices of different airline companies’ e-commerce websites or online travel agency websites.
- I like to search for cheap travel deals on different airline companies’ e-commerce websites or online travel agency websites.
- Airline company e-commerce websites or online travel agencies’ websites offer better value for my money.
- (4)
- Hedonic motivation
- Using airline company e-commerce websites or online travel agencies’ websites is fun.
- Using airline company e-commerce websites or online travel agency websites is very entertaining.
- Using airline company e-commerce websites or online travel agencies’ websites is enjoyable.
- (5)
- Behavioral intentions
- I will continue using airline e-commerce websites or online travel agency websites to purchase a ticket in the future.
- I am addicted to using airline company e-commerce websites or online travel agency websites.
- I plan to continue to use airline company e-commerce websites or online travel agency websites frequently to purchase a ticket.
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Variable Constructs | Indicators | Definitions | Source/Reference |
---|---|---|---|
Perceived Usefulness | PU1 | Perceived usefulness was defined as the level to which utilizing technology will prepare customers to execute specific activities. | [12,15,16] |
PU2 | |||
PU3 | |||
Perceived Ease of Use | PE1 | Perceived ease of use is described as the degree of ease related to the utilization of technology. | [12] |
PE2 | |||
PE3 | |||
Price Sensitivity | PS1 | Price sensitivity is described as the scope of consciousness and response exhibited by customers when discovering differences in prices of goods and services. | [31] |
PS2 | |||
PS3 | |||
Hedonic Motivation | HM1 | Hedonic motivation was described as the pleasure or enjoyment acquired from employing a technology. | [6,16,39] |
HM2 | |||
HM3 | |||
Behavioral Intention | BI1 | Behavioral intention refers to how an individual has prepared conscious objectives regarding whether to conduct a specified future behavior. | [47] |
BI2 | |||
BI3 |
Segment 1 | Segment 2 | Total | Significance | |||||
---|---|---|---|---|---|---|---|---|
Demographic Profile | Measure | n | % | n | % | n | % | Chi-Square Test |
Segment Size | 1504 | 49 | 1560 | 51 | 3064 | 100 | ||
Age | Gen X | 887 | 29 | 0 | 0 | 887 | 29 | *** |
Gen Y | 617 | 20 | 1255 | 41 | 1872 | 61 | *** | |
Gen Z | 0 | 0 | 305 | 10 | 305 | 10 | *** | |
Income | Less than 25,000 Baht | 288 | 9 | 1486 | 49 | 1774 | 58 | *** |
More than 25,000 Baht | 1216 | 40 | 74 | 2 | 1290 | 42 | *** |
Psychographic Profile | Measure | Segment 1 | Segment 2 | Mean Diff | t | t-Test | ||
---|---|---|---|---|---|---|---|---|
Mean | SD | Mean | SD | |||||
Perceived Usefulness | PU1 | 3.44 | 0.62 | 3.53 | 0.60 | 0.09 | −4.02 | *** |
PU2 | 3.55 | 0.70 | 3.65 | 0.72 | 0.10 | −3.99 | *** | |
PU3 | 3.48 | 0.71 | 3.59 | 0.69 | 0.12 | −4.59 | *** | |
Perceived Ease of Use | PE1 | 3.14 | 0.77 | 3.32 | 0.76 | 0.18 | −6.59 | *** |
PE2 | 3.23 | 0.94 | 3.48 | 0.80 | 0.25 | −8.01 | *** | |
PE3 | 3.20 | 0.95 | 3.50 | 0.81 | 0.30 | −9.34 | *** | |
Price Sensitivity | PS1 | 3.13 | 0.90 | 3.40 | 0.82 | 0.26 | −8.48 | *** |
PS2 | 3.20 | 0.89 | 3.35 | 0.85 | 0.15 | −4.71 | *** | |
PS3 | 3.19 | 0.93 | 3.42 | 0.86 | 0.23 | −7.05 | *** | |
Behavioral Intention | BI1 | 3.10 | 0.94 | 3.28 | 0.85 | 0.18 | −5.59 | *** |
BI2 | 3.21 | 0.96 | 3.36 | 0.90 | 0.15 | −4.34 | *** | |
BI3 | 3.37 | 0.90 | 3.51 | 0.79 | 0.14 | −4.65 | *** | |
Hedonic Motivation | HM1 | 3.11 | 0.91 | 3.34 | 0.82 | 0.23 | −7.26 | *** |
HM2 | 3.35 | 0.78 | 3.41 | 0.73 | 0.06 | −2.15 | 0.032 | |
HM3 | 3.19 | 0.86 | 3.31 | 0.78 | 0.11 | −3.86 | *** |
Fit Indices | Value | Threshold | Assessment |
---|---|---|---|
p-value | ≤0.001 | Acceptable for complex model | |
CFI | 0.939 | >0.900 | Pass |
IFI | 0.940 | >0.900 | Pass |
TLI | 0.921 | >0.900 | Pass |
NFI | 0.937 | >0.900 | Pass |
GFI | 0.927 | >0.900 | Pass |
RMSEA | 0.085 | <0.100 | Pass |
Construct | Indicator | Loading | p-Value | Cronbach’s Alphas (Threshold = 0.70) | AVE (Threshold = 0.50) | CR (Threshold = 0.70) |
---|---|---|---|---|---|---|
Perceived Usefulness | PU1 | 0.671 | *** | 0.724 | 0.456 | 0.715 |
PU2 | 0.72 | *** | ||||
PU3 | 0.632 | *** | ||||
Perceived Ease of Use | PE1 | 0.619 | *** | 0.705 | 0.439 | 0.7 |
PE2 | 0.621 | *** | ||||
PE3 | 0.74 | *** | ||||
Price Sensitivity | PS1 | 0.739 | *** | 0.821 | 0.597 | 0.816 |
PS2 | 0.735 | *** | ||||
PS3 | 0.84 | *** | ||||
Behavioral Intention | BI1 | 0.818 | *** | 0.841 | 0.64 | 0.841 |
BI2 | 0.878 | *** | ||||
BI3 | 0.692 | *** | ||||
Hedonic Motivation | HM1 | 0.812 | *** | 0.815 | 0.601 | 0.817 |
HM2 | 0.637 | *** | ||||
HM2 | 0.859 | *** |
Fornell and Larcker Criterion | |||||
---|---|---|---|---|---|
HM | BI | PS | PE | PU | |
HM | 0 . 775 | - | - | - | - |
BI | 0.466 | 0 . 800 | - | - | - |
PS | 0.416 | 0.474 | 0 . 773 | - | - |
PE | 0.307 | 0.350 | 0.330 | 0 . 663 | - |
PU | 0.241 | 0.274 | 0.250 | 0.213 | 0 . 675 |
HTMT Ratio Approach | |||||
HM | - | - | - | - | - |
BI | 0.787 | - | - | - | - |
PS | 0.804 | 0.891 | - | - | - |
PE | 0.803 | 0.892 | 0.965 | - | - |
PU | 0.712 | 0.789 | 0.827 | 0.973 | - |
Fit Indices | Value | Threshold | Assessment |
---|---|---|---|
p-value | ≤0.001 | Acceptable for complex model | |
CFI | 0.938 | >0.90 | Pass |
IFI | 0.938 | >0.90 | Pass |
TLI | 0.919 | >0.90 | Pass |
NFI | 0.935 | >0.90 | Pass |
GFI | 0.925 | >0.90 | Pass |
RMSEA | 0.086 | <0.10 | Pass |
Hypothesis | Endogenous Variable | Exogenous Variable | Standardized Estimate | p-Value | Result |
---|---|---|---|---|---|
H1 | Perceived usefulness | Behavioral intention | −0.076 | 0.183 | Rejected |
H2 | Perceived ease of use | Behavioral intention | 0.406 | *** | Supported |
H3 | Price sensitivity | Behavioral intention | 0.448 | *** | Supported |
H4 | Hedonic motivation | Behavioral intention | 0.194 | *** | Supported |
Fit Indices | Configural Invariance | Metric Invariance | Scalar Invariance | Threshold |
---|---|---|---|---|
p-value | ≤0.001 | ≤0.001 | ≤0.001 | |
CFI | 0.919 | 0.917 | 0.914 | >9.00 |
IFI | 0.919 | 0.917 | 0.917 | >0.90 |
NFI | 0.914 | 0.912 | 0.908 | >0.90 |
GFI | 0.903 | 0.901 | 0.896 | >0.90 |
RMSEA | 0.070 | 0.069 | 0.067 | <0.10 |
Acceptable | Acceptable | Not Passed |
Fit Indices | Value | Threshold | Assessment |
---|---|---|---|
p-value | ≤0.001 | Acceptable for complex model | |
CFI | 0.916 | >0.90 | Pass |
IFI | 0.917 | >0.90 | Pass |
NFI | 0.912 | >0.90 | Pass |
GFI | 0.899 | >0.90 | Acceptable |
RMSEA | 0.071 | <0.10 | Pass |
Hypothesis | Relationship | Segment 1 | Segment 2 | Critical Ratio Difference | Threshold | ||||
---|---|---|---|---|---|---|---|---|---|
Std. Est. | p-Value | Std. Est. | p-Value | ||||||
H1 | PU | → | BI | −0.097 | 0.093 | 0.024 | 0.852 | 0.839 | |1.96| |
H2 | PE | → | BI | 0.383 | *** | 0.353 | 0.001 ** | −0.805 | |1.96| |
H3 | PS | → | BI | 0.420 | *** | 0.475 | *** | −0.828 | |1.96| |
H4 | HM | → | BI | 0.258 | *** | 0.132 | *** | −2.116 ** | |1.96| |
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Naruetharadhol, P.; Wongsaichia, S.; Zhang, S.; Phonthanukitithaworn, C.; Ketkaew, C. Understanding Consumer Buying Intention of E-Commerce Airfares Based on Multivariate Demographic Segmentation: A Multigroup Structural Equation Modeling Approach. Sustainability 2022, 14, 8997. https://doi.org/10.3390/su14158997
Naruetharadhol P, Wongsaichia S, Zhang S, Phonthanukitithaworn C, Ketkaew C. Understanding Consumer Buying Intention of E-Commerce Airfares Based on Multivariate Demographic Segmentation: A Multigroup Structural Equation Modeling Approach. Sustainability. 2022; 14(15):8997. https://doi.org/10.3390/su14158997
Chicago/Turabian StyleNaruetharadhol, Phaninee, Sasichakorn Wongsaichia, Shenying Zhang, Chanchai Phonthanukitithaworn, and Chavis Ketkaew. 2022. "Understanding Consumer Buying Intention of E-Commerce Airfares Based on Multivariate Demographic Segmentation: A Multigroup Structural Equation Modeling Approach" Sustainability 14, no. 15: 8997. https://doi.org/10.3390/su14158997