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Marketing mix factors affecting the frequency and loyalty in online transactions of Nakhon Pathom teenagers |
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| รหัสดีโอไอ | |
| Creator | Wisit Rittiboonchai |
| Title | Marketing mix factors affecting the frequency and loyalty in online transactions of Nakhon Pathom teenagers |
| Publisher | Phetchaburi Rajabhat University |
| Publication Year | 2564 |
| Journal Title | Interdisciplinary Research Review (IRR) |
| Journal Vol. | 16 |
| Journal No. | 1 |
| Page no. | 32-35 |
| Keyword | Marketing mix, loyalty, online transactions |
| URL Website | https://ph02.tci-thaijo.org/index.php/jtir |
| Website title | Interdisciplinary Research Review (IRR) |
| ISSN | 2697-536X |
| Abstract | This research is aimed at 1. To compare the frequency and loyalty in online transactions of Nakhon Pathom teenagers when classified by personal factors and 2. To study the influence of marketing mix factors affecting the frequency and loyalty in online transactions of Nakhon Pathom teenagers. The researchers collected data from a sample of 400 teenagers interested in buying and doing online transactions in Nakhon Pathom Province by snowball sampling. The statistics used in the research were frequency, percentage, mean, standard deviation, T-test, one-way variance analysis and multiple regression analysis.The findings of the research are as follows. (1) The frequency and loyalty in online transactions of Nakhon Pathom teenagers were different when classified by gender, age, education and income with statistical significance. (2) The marketing mix factors affecting the frequency of online transactions of Nakhon Pathom teenagers comprised the price (b=0.21), distribution promotion (b=0.16) and product (b=0.15). The equation has a predictive power equal to 41 percent. This can be written as follows: Y=1.46+0.15X1**+0.21X2**+0.03X3+0.16X4** (3) The marketing mix factors affecting loyalty in online transactions of Nakhon Pathom teenagers consisted of the product (b=0.45), price (b=0.30) and distribution promotion (b=0.13). The equation has a predictive power equal to 44 percent. This can be written as follows: Y=1.85+0.45X1**+0.30 X2**+0.01X3+0.13X4** |
