Marketing Mix Factors and Online Household Products Purchases in Sukhothai Province
DOI:
https://doi.org/10.60101/jla.2025.6.2.7127Keywords:
marketing mix factors, purchasing decision, household products, online channelsAbstract
This research aims to: 1) study the level of online purchasing decision for household products among consumers in Sukhothai Province, 2) compare personal factors affecting the decision to purchase household products through online channels among consumers in Sukhothai Province, and 3) examine the marketing mix factors influencing the purchase of household products through online channels among consumers in Sukhothai Province. The sample for this study consisted of 400 consumers who purchase household products online in Sukhothai Province. Data were collected using questionnaires through simple random sampling. The analysis included frequency, percentage, mean, and standard deviation, while hypothesis testing was conducted using t-tests, one-way analysis of variance (ANOVA), and multiple regression analysis. The findings revealed that: 1) the overall decision-making to purchase household products through online channels among consumers in Sukhothai Province was at the highest level (ðĨĖ = 4.22, SD = 0.38), with the highest-rated factor being "staff's immediate attention to customer service and after-sales service" (ðĨĖ = 4.37, SD = 0.62), followed by "products regularly and consistently used" (ðĨĖ = 4.24, SD = 0.61), 2) differences in personal factors, including gender, age, marital status, education level, occupation, average monthly income, expenditure on products, product types, purchasing frequency, and purchasing objectives, did not significantly affect consumers' decisions to purchase household products through online channels in Sukhothai Province at the 0.05 significance level, and 3) five marketing mix factors, including product, price, promotion, people, and process, had a statistically significant influence on consumers' online purchasing decisions at the 0.05 significance level, with a predictive power of 80.0%, while distribution channels and physical evidence factors did not significantly affect purchasing decisions.
References
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