Service Quality and Brand Image Impacting Consumer Behavior and the Decision-Making Process in Buying Gold in the Bangkok Metropolitan Region - Phonghanyudh, Surasaek; Kungvantip, Varintorn (Journal of Management Science Nakhon Pathom Rajabhat University, 2024)
This research investigates the effects of service quality and brand image on consumer behavior and decision-making processes related to gold purchases in the Bangkok Metropolitan Region. The study sets out to: 1) analyze the decision-making processes of gold purchasers in the region based on individual demographic factors; and 2) assess how service quality and brand image influence these consumer behaviors and decisions. The population for this study comprises individuals who have purchased gold in the Bangkok Metropolitan Region. The precise number of consumers involved is unknown. Data were collected through convenience sampling from customers at regional gold retailers, enhanced by online surveys, totaling 400 respondents. The primary instrument was a questionnaire validated for content and reliability. Statistical analysis utilized methods including frequency, percentage, mean, standard deviation, t-tests, chi-square tests, one-way analysis of variance, and multiple regression analysis employing the ENTER method. Findings indicate significant variations in the decision-making processes among consumers, influenced by factors such as gender and marital status. Service quality and brand image emerged as substantial determinants of consumer behavior and decision-making. Specifically, service quality impacts were quantified with coefficients for reliability (b=0.33), assurance (b=0.28), tangibility (b=0.21), empathy (b=0.13), and responsiveness (b=0.12). In terms of brand image, the significant elements ranked by impact include brand attributes (b=0.32), attitudes toward the brand (b=0.28), and perceived product benefits (b=0.10). The predictive power of the developed model stands at 72.00%. Multicollinearity tests, yielding Tolerance values close to 1 and VIF values not exceeding 10, confirm the robustness of the analysis. The resulting regression equation is as follows: Ytot = 0.77+0.21X1**+0.33X2**+0.12X3*+0.13X4*+0.28X5**+0.32X6**+0.10X7*+0.28X8** Article history: Received 25 April 2024 Revised 12 October 2024 Accepted 15 October 2024 SIMILARITY INDEX = 10.86%