A Structural Equation Model of Marketing Mix Factors Influencing Purchase Decisions: An Analysis of Economic Opportunity Perception in Real Estate in Chonburi Province
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Abstract
The application of the marketing mix as a strategic tool to stimulate consumer purchasing decisions served as the foundation for this research, which aimed to investigate consumer perceptions regarding the marketing mix elements (7Ps) and purchasing decisions assessed through perceived economic opportunity. The objectives of the study were to develop a structural relationship model and to examine the degree of model fit between the marketing mix (7Ps) and purchasing decisions-measured by levels of perceived economic opportunity-in the context of low-rise residential real estate (housing project type) located in Mueang District, Chonburi Province, using empirical data. A quantitative research design was employed. The sample was determined, and data were collected online from individuals who had previously purchased a residential property in Chonburi Province. The data collection was conducted via websites and various digital applications. Descriptive statistics were used to calculate the mean and standard deviation, while inferential statistics were utilized through Structural Equation Modeling (SEM) to evaluate the model’s goodness-of-fit against acceptable criteria. The findings revealed that among the marketing mix elements, the ‘Place’ dimension received the highest mean score (Mean = 4.19), with respondents prioritizing proximity to major roads and transportation infrastructure. In terms of purchasing decisions, as measured by perceived economic opportunity, the ‘Loan Interest Rate’ factor received the highest mean score (Mean = 4.32). Both variables exhibited normal distribution within acceptable statistical thresholds, validating their suitability for model development. The resulting model demonstrated a satisfactory fit with empirical data (χ² = 105.741, df = 37, χ²/df = 2.858, CFI = .971, TLI = .957, RMSEA = .071, RMR = .017). Furthermore, the marketing mix (7Ps) was found to have a statistically significant positive direct effect on purchasing decisions measured by perceived economic opportunity (β = 0.67, p < 0.001). The model accounted for 45% of the variance in purchasing decisions based on perceived economic opportunity. In conclusion, the study offers valuable insights into consumer behavior and preferences, particularly the impact of the marketing mix (7Ps) on purchasing decisions for low-rise residential real estate. These insights can be leveraged by developers and marketers to formulate marketing strategies that are more closely aligned with the needs and expectations of the target consumer segment.
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