University dropout is a major policy concern around the world because of its consequences for the individual, institutions and society. In this study, we offer new evidence by examining the cumulative effect of academic vulnerability and family support on trajectory of dropout among cohorts of undergraduate students in Thailand. Data were drawn from administrative records of two cohorts of students (n = 1,613), and consisted of information on which semester individual's dropped out of university. Using discrete time survival analysis, we first modeled the trajectory of dropout without predictors followed by a conditional model which examined the effects of predictors on the trajectory of dropout. Cumulative effects of predictors were then examined by plotting the probabilities of their combined effects on dropout in each semester. Our findings show that while the beginning of the second year was a critical period of dropout with almost 20% of students leaving by this time, as much as 10% of students drop out between the second and final year. Students with the lowest entry grades were about 2.17 times more likely to dropout while those who were farther away from family support were 1.32 times more likely to drop out across each semester. The cumulative effect of low entry grades and living away from family support resulted in a 30% probability of dropping out in the second year. The dropout rate among this category of students by the final year was 60% compared to only 14% for students with high entry grades and who live close to their families. Among other things, we recommend that interventions to reduce dropout should encompass both helping students to access family support and develop personal connections at university to compensate for absence of family support, as well as academically focused support for student who do not have a strong entry qualification.

The research aimed to examine the influence of personal traits, corporate image, and service marketing mix on customer satisfaction of Shabu Restaurants in Ubon Ratchathani province, Thailand. A questionnaire with a reliability coefficient (0.93) at a very high level was used in this study. Cluster sampling collected 400 customers from Shabu restaurants in Ubon Ratchathani province: Shabu Indy, Wan Moo Shabu, Pra Whale Jaidee Shabu, and others. The statistics were descriptive, correlation coefficient, and multiple regression analysis. The results showed that independent variables correlated with customer satisfaction at a high level (r = 0.782). The service marketing mix 7Ps and the corporate image can predict customer satisfaction by 59.3% (r2 = 0. 593). Three variables affect the satisfaction of customers, which are the regression coefficient of trust (β = 0.30), corporate reputation (β = 0.20), and physical evidence (β = 0.15), respectively. However personal traits of Shabu customers didn't affect their satisfaction with Shabu clients. The research, therefore, recommends Shabu restaurants review and evaluate their customer database to include service marketing mix and corporate image.

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