Exploring Factors Affecting Consumer Behavioral Intentions toward Online Food Ordering in Thailand
Abstract
:1. Introduction
2. Literature Review
2.1. Definitions about Food Delivery through Apps
2.2. Theory of Planned Behavior (TPB)
2.3. Technology Acceptance Model
2.4. Sustainable Development Goals (SDGs)
3. Research and Methodology
3.1. Data Collections
- Questionnaire design: The questionnaire was divided into 3 parts. Part 1 concerned personal and household characteristics of the respondents (sex, age, highest education level, occupation, average income) and their experience with using food-ordering services apps. Part 2 concerned the behavior of users ordering food through food-ordering apps. Part 3 involved other suggestions related to the use of food-ordering apps.
- Scale: Part 2 consisted of 22 items, assessed on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Although these are ordinal variables, these can also be estimated using maximum likelihood (ML), according to [66], who described it as “the second option for the ordinal variable in which the parcel is being analyzed.” A parcel is a total score across a set of homogeneous items, each with a Likert-type scale. Parcels are generally treated as continuous variables, and their score reliability tends to be for the collective, rather than for individual, items. If the distribution of all parcels is normal, then the default ML estimate could be used to analyze the data.
- Sample size: This study analyzed data with CFA in a SEM model; the optimal sample size was 20 times the number of variables [66]. With 22 variables, the sample size was 440.
- Participants: The respondents were service users who ordered food online through the apps. The survey was conducted from January to February 2021 in the six regional economic provinces of Thailand: Central, Northern, Northeastern, Eastern, Western, and Southern. The total sample size was 1320 people, comprising 220 from each region.
- Table 2 shows the frequency and percentage analysis of basic data from all the samples, such as service users ordering online food delivery, task characteristics, and service frequency. The samples of respondents had the following characteristics—gender: 805 females (61%) and 515 males (39%); age: most were 21–30 (556, 42.1%) and 31–40 (358, 27.1%); education: most had a bachelor’s degree (817: 61.9%), followed by high school (299: 22.7%); occupation: most respondents were students (395: 29.9%), followed by company employees (366: 27.7%); income per month: most earned TBH 10,001–20,000 (413: 31.3%), followed by TBH 5000–10,000 (266: 20.2%). The highest frequency of online food ordering was less than 4 times a month (683: 51.7%), followed by 5–10 times per month (405: 30.7%)
3.2. Reliability
3.3. Structural Equation Modeling
4. Findings
4.1. Descriptive Statistics
4.2. Structural Equation Model
4.2.1. Goodness-of-Fit Statistics
4.2.2. Measurement Model
Construct | Variables | Mean | SD | R2 |
---|---|---|---|---|
Behavioral Intention [53] | I1: I intend to use the food delivery app. | 3.82 | 0.839 | 0.792 |
I2: If I have an opportunity, I will order food through the delivery app. | 3.85 | 0.814 | 0.790 | |
I3: I intend to keep ordering food through the delivery app. | 3.80 | 0.845 | 0.799 | |
Attitude [53] | I4: Using the food delivery app is useful. | 4.11 | 0.823 | 0.548 |
I5: I am strongly in favor of ordering food through the delivery app. | 3.69 | 0.927 | 0.579 | |
I6: I desire to use the delivery app when I purchase food. | 3.77 | 0.870 | 0.579 | |
Subjective Norms [48] | I7: How do you think your friends would respond if they thought you had used a food delivery application? | 3.72 | 0.803 | 0.728 |
I8: How do you think your parents would respond if they thought you had used a food delivery application? | 3.50 | 0.917 | 0.550 | |
Perceived Behavioral Control [73] | I9: In general, ordering food online is very complex. | 3.04 | 1.033 | 0.793 |
I10: With ordering food online via application creates anxiety for you. | 2.94 | 1.083 | 0.850 | |
I11: In general, ordering food online yields (will yield) few problems for me. | 3.10 | 1.048 | 0.546 | |
Perceived Ease of Use [48] | I12: I would find it easy to order food using a food delivery application. | 3.93 | 0.784 | 0.779 |
I13: My operation of a food delivery application would be clear and understandable. | 3.91 | 0.788 | 0.770 | |
I14: Using a food delivery application would not require a lot of mental effort. | 3.84 | 0.807 | 0.693 | |
Perceived Usefulness [48] | I15: Using a food delivery application would enable me to better check the ordering and receiving process of delivery food. | 3.93 | 0.797 | 0.758 |
I16: Using a food delivery application would make it more convenient to order and receive delivery food. | 3.97 | 0.783 | 0.754 | |
I17: Food delivery application would be useful for ordering and receiving delivery food. | 3.95 | 0.787 | 0.803 | |
Trust [53] | I18: I trust the food delivery app. | 3.85 | 0.760 | 0.742 |
I19: The information provided by the food delivery app is reliable. | 3.85 | 0.758 | 0.687 | |
Task–Technology Fit [41] | I20: The functions of FDAs are enough for me to order and receive the delivery food. | 3.85 | 0.760 | 0.741 |
I21: The functions of FDAs are appropriate to help manage the ordering and receiving the delivery of food. | 3.87 | 0.780 | 0.784 | |
I22: The functions of FDAs fully meet my requirements of ordering and receiving the delivery of food. | 3.88 | 0.772 | 0.774 |
4.2.3. Structural Model and Hypothesis Testing
5. Discussion
6. Conclusions
7. Limitations and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Author | Method | Perceived Task- Technology Fit | Trust | Perceived Usefulness | Perceived Ease of Use | Perceived Behavioral Control | Subjective Norms | Attitude toward Online Food | Behavioral Intention |
---|---|---|---|---|---|---|---|---|---|
Song et al. (2021) [28] | CFA | √ | √ | √ | √ | ||||
Hwang et al. (2019) [19] | CFA, SEM | √ | √ | ||||||
Kim et al. (2020) [38,57] | SEM | √ | √ | √ | √ | ||||
Namkung et al. (2018) [9,19,52,53,54,55,56] | CFA, SEM | √ | √ | √ | √ | √ | |||
Roh et al. (2018) [48] | SEM | √ | √ | √ | √ | ||||
Cho et al. (2019) [53] | CFA, SEM | √ | √ | ||||||
Rahi et al. (2020) [63] | PLS-SEM | √ | √ | √ | |||||
Troise et al. (2020) [38] | PLS-SEM | √ | √ | √ | √ | √ | √ | √ | |
This Study | √ | √ | √ | √ | √ | √ | √ | √ |
Characteristics | Types | Frequency | Percentage |
---|---|---|---|
Sex | Male | 515 | 39 |
Female | 805 | 61 | |
Age (years) | <20 | 149 | 11.3 |
21–30 | 556 | 42.1 | |
31–40 | 358 | 27.1 | |
41–50 | 169 | 12.8 | |
51–60 | 83 | 6.3 | |
>60 | 5 | 0.4 | |
Education | Lower than junior high school | 37 | 2.8 |
Junior high school | 64 | 4.8 | |
Senior high school | 299 | 22.7 | |
Bachelor’s degree | 817 | 61.9 | |
Higher than bachelor’s degree | 103 | 7.8 | |
Occupation | Student/College student | 395 | 29.9 |
General contractor | 162 | 12.3 | |
Government employee/ State enterprises | 174 | 13.2 | |
Company employee | 366 | 27.7 | |
Business owner | 184 | 13.9 | |
Other | 39 | 3.0 | |
Income | <5000 | 221 | 16.7 |
THB 5000–10,000 | 266 | 20.2 | |
THB 10,001–20,000 | 413 | 31.3 | |
THB 20,001–30,000 | 241 | 18.3 | |
THB 30,001–40,000 | 89 | 6.7 | |
THB < 40,001 | 90 | 6.8 | |
Use frequency | Less than 4 times/month | 683 | 51.7 |
5–10 times/month | 405 | 30.7 | |
More than 10 times/month | 219 | 16.6 | |
Other | 13 | 1.0 |
Construct | Variables | Standardized Loadings * | Standard Error | t-Value | CR | AVE | Cronbach’s Alpha |
---|---|---|---|---|---|---|---|
Behavioral Intention | I1 | 0.89 | 0.007 | 122.101 | 0.92 | 0.794 | 0.92 |
I2 | 0.889 | 0.007 | 121.894 | ||||
I3 | 0.894 | 0.007 | 125.624 | ||||
Attitude | I4 | 0.74 | 0.015 | 50.053 | 0.798 | 0.569 | 0.836 |
I5 | 0.761 | 0.014 | 53.954 | ||||
I6 | 0.761 | 0.014 | 53.599 | ||||
Subjective Norms | I7 | 0.853 | 0.015 | 58.774 | 0.779 | 0.639 | 0.771 |
I8 | 0.742 | 0.016 | 45.165 | ||||
Perceived Behavioral Control | I9 | 0.89 | 0.009 | 94.027 | 0.889 | 0.729 | 0.885 |
I10 | 0.922 | 0.009 | 104.494 | ||||
I11 | 0.739 | 0.014 | 52.546 | ||||
Perceived Ease of Use | I12 | 0.883 | 0.008 | 111.383 | 0.899 | 0.748 | 0.898 |
I13 | 0.878 | 0.008 | 108.423 | ||||
I14 | 0.833 | 0.01 | 83.748 | ||||
Perceived Usefulness | I15 | 0.871 | 0.008 | 104.782 | 0.91 | 0.772 | 0.91 |
I16 | 0.868 | 0.008 | 103.582 | ||||
I17 | 0.896 | 0.007 | 122.945 | ||||
Trust | I18 | 0.861 | 0.011 | 75.631 | 0.833 | 0.714 | 0.833 |
I19 | 0.829 | 0.012 | 68.189 | ||||
Task–Technology Fit | I20 | 0.861 | 0.009 | 94.467 | 0.903 | 0.757 | 0.903 |
I21 | 0.886 | 0.008 | 108.25 | ||||
I22 | 0.863 | 0.009 | 95.084 |
Hypotheses | Description | Standardized Path Coefficient | t-Value | Result |
---|---|---|---|---|
H1 | ATT→BI | 0.720 | 24.005 | Supported |
H2 | SN→BI | 0.236 | 6.437 | Supported |
H3 | PBC→BI | −0.018 | −0.881 | Not Supported |
H4 | SN→ATT | −0.045 | −1.062 | Not Supported |
H5 | PEOU→ATT | 0.625 | 8.734 | Supported |
H6 | PU→ATT | 0.258 | 5.506 | Supported |
H7 | TR→ATT | 0.197 | 3.484 | Supported |
H8 | PEOU→PU | 0.751 | 23.923 | Supported |
H9 | TTF→PEOU | 0.252 | 7.283 | Supported |
H10 | TTF→PU | 1.185 | 39.604 | Supported |
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Inthong, C.; Champahom, T.; Jomnonkwao, S.; Chatpattananan, V.; Ratanavaraha, V. Exploring Factors Affecting Consumer Behavioral Intentions toward Online Food Ordering in Thailand. Sustainability 2022, 14, 8493. https://doi.org/10.3390/su14148493
Inthong C, Champahom T, Jomnonkwao S, Chatpattananan V, Ratanavaraha V. Exploring Factors Affecting Consumer Behavioral Intentions toward Online Food Ordering in Thailand. Sustainability. 2022; 14(14):8493. https://doi.org/10.3390/su14148493
Chicago/Turabian StyleInthong, Chidchanok, Thanapong Champahom, Sajjakaj Jomnonkwao, Vuttichai Chatpattananan, and Vatanavongs Ratanavaraha. 2022. "Exploring Factors Affecting Consumer Behavioral Intentions toward Online Food Ordering in Thailand" Sustainability 14, no. 14: 8493. https://doi.org/10.3390/su14148493
APA StyleInthong, C., Champahom, T., Jomnonkwao, S., Chatpattananan, V., & Ratanavaraha, V. (2022). Exploring Factors Affecting Consumer Behavioral Intentions toward Online Food Ordering in Thailand. Sustainability, 14(14), 8493. https://doi.org/10.3390/su14148493