Student Loyalty Toward Masters Degree Business Administration Curriculum At Srinakharinwirot University

This research aims to study mixed factors of service marketing affecting student loyalty toward the business administration curriculum at the masters degree level at Srinakharinwirot University. It also examines the relationship between student satisfaction and loyalty in the MBA program. The results show that service marketing mixed factors have influenced student satisfaction in the MBA curriculum by approximately 39.4 percent. The product and service aspect is the most important factor in predicting student satisfaction. Furthermore, the service marketing mix has influenced student attitudinal loyalty toward the MBA curriculum by about 26.7 percent, while the service-marketing mix has influenced student behavioral loyalty by nearly 40.5 percent. Student satisfaction has rather a low positive relation to attitudinal loyalty at the 0.01 level of statistical significance. Student satisfaction has a moderately positive relation to behavioral loyalty at the statistical significance level of 0.01

Thai Tourist Commitment And Loyalty To Travel By Sea

The purpose of this research is to study factors influencing Thai tourist loyalty concerning travel by sea to create a competitive advantage and value added to seaside attractions. This research employs quantitative analysis. The researcher collected data by interviewing 440 Thai tourists in three seaside locations: Pattaya, Hua-Hin and Samed. The results show that the majority of respondents are female, aged between 25 and 34 years old.  They are a) single with bachelor degrees, b) employed in the private sector with monthly incomes over THB25,001 and c) resident in Bangkok. The results of testing the hypotheses are as follows: 1.         The developed structural equation model of attitudinal loyalty towards seaside attractions in Pattaya was congruent with empirical data as the criterion as follows: (?2 = 486.091, df = 174, GFI = .909, AGFI = .879, RMSEA = .064, RMR = .063 and CFI = .960).  Factors influencing loyalty towards seaside attractions in Pattaya, include the following: a) affective commitment showed the highest level of direct influence = .688 and b) continuance commitment with direct influence = .328 with statistical significance at the .001 level.  Satisfaction with seaside attractions has an indirect influence equal to .743 with statistical significance at the .001 level. 2.         The developed structural equation model of attitudinal loyalty towards seaside attractions in Hua-Hin, as developed by the researcher, includes goodness of fit with empirical data (?2 =472.086, df = 173, GFI = .909, AGFI = .878, RMSEA = .063, RMR = .072 and CFI = .952). Affective commitment has a direct influence on attitudinal loyalty = .724, followed by continuance commitment = .276 with statistical significance at the .001 level. Satisfaction with seaside attractions has an indirect influence equal to .570 with statistical significance at the .001 level. 3. The developed structural equation model of attitudinal loyalty towards seaside attractions in Samed is congruent with empirical data (?2 = 503.853, df = 172, GFI = .900, AGFI = .866, RMSEA = .066, RMR = .080 and CFI = .953).  Affective commitment shows a level of direct influence = .646, followed by continuance commitment = .328 with statistical significance at the .001 level. Satisfaction with seaside attractions has an indirect influence =.547 with statistical significance at the .001 level

Isolation of Yeast DNA Replication Mutants in Permeabilized Cells

A random population of temperature-sensitive mutants was screened by assaying for defects in DNA synthesis in a permeabilized yeast DNA replication system. Twenty mutants defective in in vitro DNA synthesis have been isolated. In this paper we describe eight of these mutants. Seven of them fall into three complementation groups--cdc2, cdc8, and cdc16--involved in the control of the cell-division cycle. Because synthesis in vitro represents propagation of replication forks active in vivo at the time of permeabilization, our finding that cdc2 and cdc16 mutants can incorporate dTMP into DNA in such permeabilized cells at 23 degrees C but not at 37 degrees C supports the conclusion that these two mutations directly affect DNA synthesis at replication forks. Such an involvement was previously suggested by in vivo analysis for CDC2 but was less clear for CDC16. Finally, the usefulness of our screening procedure is demonstrated by the isolation of replication mutants in previously undescribed complementation groups. One strain shows a serious defect in in vivo DNA synthesis but normal RNA synthesis

A Second Order Confirmatory Factor Analysis of Composite Indicators of Sufficiency Economy Integration of Knowledge Management for Basic Education Principals

The purpose of this study  to develop composite indicators of  Sufficiency Economy (SE)  integration of knowledge management for basic education Principals, and  verify  the fit of the indicators  with empirical data. The basic education principals of Phitsanulok province service area in the year 2011 were collect to the research samples by stratified random sampling , 120 units answer  Likert's 5 scale of questionnaire, but only 110 units that answer completely information were used for  analysis by the second order confirmatory factor analysis (CFA). The research results shown that, Composite Indicator  from development  consisted of 5 main components and 21 indicators were fitted with the empirical data, determined from the Chi-square values = 107.13, and not statistical significance (p = 0.98224) at zero degrees of freedom (df = 140) indicated that model not different from the empirical data. Goodness of fit index (GFI = 0.91), Standardized RMR = 0.048, and Q-plot curve steeper than the diagonal. The main elements that have the highest priority consisted the knowledge acquisition that focused on participation of indicate issue of the SE philosophy into action of school, as with the main elements of knowledge  usage/utilization that have the highest priority an focused on indicators to enhance the links between the knowledge of the same type in school. Keywords: Composite indicators, Sufficiency Economy, Knowledge Managemen

Sufficient Conditions for Starlike Functions Associated with the Lemniscate of Bernoulli

Let -1\leq B<A\leq 1. Condition on \beta, is determined so that 1+\beta zp'(z)/p^k(z)\prec(1+Az)/(1+Bz)\;(-1<k\leq3) implies p(z)\prec \sqrt{1+z}. Similarly, condition on \beta is determined so that 1+\beta zp'(z)/p^n(z) or p(z)+\beta zp'(z)/p^n(z)\prec\sqrt{1+z}\;(n=0, 1, 2) implies p(z)\prec(1+Az)/(1+Bz) or \sqrt{1+z}. In addition to that condition on \beta is derived so that p(z)\prec(1+Az)/(1+Bz) when p(z)+\beta zp'(z)/p(z)\prec\sqrt{1+z}. Few more problems of the similar flavor are also considered

Linear Programming with Random Requirements

Linear programming was first developed by George B. Dantzig, Marshall Wood, and associates of the U.S. Air Force, in 1947. At that time, the Air Force organized a research group under the title of project SCOOP (Scientific Computation of Optimum Programs). This project contributed to the developing of a general interindustry model based on the Leontief input-output model, the Air Force programming and budgeting problem, and the problems which involved the relationship between two-person zero sum games and linear programming. The result was the formal development and application of the linear programming model. This project also developed the simplex computational method for finding the optimum feasible program. Early applications of linear programming were made in the military, in economics, and in the theory of games. During the last decade, however, linear programming applications have been extended to such other fields as management, engineering, and agriculture. As the application of linear programming has extended to many other fields, Dantzig (1955), Tinter (1955), Beale (1955), Madansky (1960), and others have been responsible for the formulation and development of stochastic linear programming. The stochastic linear programming problem occurs when some of the coefficients, in the objective function and/or in the constraint system of the linear programming model, are subject to random variation. In the literature, several methods are indicated for formulating the linear programming problem with random requirements to arrive at a solution. The intention of this study is to review some of these methods, and to compare one wit another in terms of the optimum value of the objective function which results from each method. There are three methods that will be considered. The first method is to replace the random element with its expected value and solve the resulting linear programming problem (Hadley, 1964). The second method is Dantzig’s two-stage linear programming problem with a random requirement (Dantzig, 1955). Suppose the following linear programming problem is considered: Min. (or max.) C’X X ≥ 0 Subject to: AX ≤ b, Where C and X are n by 1 vectors, b an m by 1 vector, and A an n matrix, and C’ is C transpose. If vector b is random and matrix A is known, then in the first stage, a decision is made on X, the random vector b is observed, and AX is compared with b. In the second stage, inaccuracies in the first decision are compensated for by a new decision variable Y with some penalty cost F. The problem then becomes, E min. (or max.) C’X + F’Y, X ≥ 0, Y ≥ 0, Subject to : AX + BY = b, Where B is an m by 2n matrix with elements ones, minus ones, and zeroes, and Y is an 2n by 1 vector with elements yi and y-i. E denotes an expectation. In the third method, the constraints with random requirements are to satisfy a given probability level. The problem then is to find values of the decision variables which optimize the expected objective function without violating the given probability measure (Charnes and Cooper, 1962). This report surveys the literature on basic linear programming and the simplex method of solution, describes random requirements, and illustrates three methods of solution. Finally, the optimal value of the objective function of each method is compared with the others