Generic Business Strategies and Advantage of Tourist Companies - Statistics Project Example

Generic Business Strategies and Competitive Advantage of Tourist Companies 1.0 Introduction Multiple regression analysis is one of the most applied statistical methods of data analysis in econometrics. The method is popular both for scholarly and company-based analysis of business and economic statistics (Ziliak and Kniesner, 1998). The popularity of multiple regressions as a method of data analysis is related to its applicability to a variety of data type and problems (Gordon, 1978). The method is also known for its ease of interpretation, widespread availability, and relative ease by which they can be carried out using different types of data analysis software (Cryer and Chan, 2008). In business research, multiple regression is applied for two main purposes that are closely related. The first purpose it for prediction of relationships. When using it for this purpose the researcher seeks to find the linear combination of a set of predictor variables that give the best estimates for a dependent variable across a number of different observations (Franses and Paap, 2004). The accuracy of prediction of the multiple regressions model is measured by the magnitude of R2 as well as the statistical significance of the entire model. On the other hand, if the prediction provided by the model is statistically significant overall, then multiple regression analysis is used to draw conclusions about all the individual predictor/independent variables (Wei, 2006). Basically, the statistics obtained from a multiple regression model are applied in testing the hypotheses regarding the effects of individual independent variables on the dependent variable (Cryer and Chan, 2008). Multiple regression has been basically described as a statistical technique that allows a researcher to predict an entity’s score on one variable on the basis of its scores on several other variables (Mason and Perreault, 2001). The variable on which the score is being predicted is referred to as the dependent variables while the others are the predictor variables. In this exercise, multiple regressions is applied in the measurement of the relationship between generic business strategies and competitive advantage in tourist companies. In this case, competitive advantage is the dependent variable while the predictor variables consist of three major factors including cost leadership strategy, differentiation strategy, and focus strategy. Each of the predictor variables is defined by a number of sub-variables listed under them in the conceptual model below. The exercise aims to measure the effects of the three predictor variables on the competitiveness of various companies in the tourism industry including museums, hotels and tour companies. A total of ten different companies were included in the research and they were represented by 44 respondents in managerial positions. 2.0 Expected relationships between dependent and explanatory variables The multiple regression method applied in this exercise employed linear regression to test the relationship between competitive advantage of the tourist companies and the three major generic strategies acting as the independent variables. The regression linear model below indicates the expected relationship between the dependent and predictor variables.A linear regression model was used to indicate the extent to which each independent variable affected competitive advantage of the tourist companies. The model applied is presented below; Y = β0 + β1X1 + β2X2 +β3X3 + ε Where Y represents competitive advantage, β0 is a constant term, X1-represents cost leadership, X2 represents differentiation, and X3 represents focus. The notation ε is the disturbance term. This model is represented below by a conceptual framework illustrating the relationship between competitive advantage and the three predictor variables. As illustrated by the framework, all the three predictor variables impact on the competitive advantage of the companies in different ways. While the predictor variables are defined by several other sub-variables, competitive advantage of the companies is defined by two main sub-variables including superior profits and increased market share. Predictor Variables Dependent Variable 3.0 Hypotheses The study aims to measure the following hypotheses; Hypothesis 1: There is a relationship between Cost Leadership strategy and Competitive Advantage of the firms in the tourism industry. Hypothesis 2: There is a relationship between Differentiation strategy and Competitive Advantage of the firms in the tourism industry. Hypothesis 3: There is a relationship between Focus (cost or differentiation) strategy and Competitive Advantage in the firms in the tourism industry. 4.0 Analysis and Findings Coefficient of determination explains the extent to which changes in dependent variable can be explained by the change in the independent variables. It also explains the percentage of the variation in the dependent variable (Competitive advantage) that is explained by all the three independent variables (Cost leadership strategy, Differentiation strategy, and Focus strategy). The table below illustrates the model summary of the multiple regression analysis carried out on the data. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .438a .192 .131 .2891 a. Predictors: (Constant), Focus, Cost Leadership, Differentiation According to the linear correlation model applied by this study, Adjusted R Square (R2) was 0.131 meaning that the independent variables studied explain 13.1% of the effects of tourist company generic strategies on their competitive advantage. This implies that the other variables not studied in this research contributed to 86.9% of the variability in competitive advantage and thus further research may be conducted to investigate these other effects of other factors other than generic strategies on the performance of these companies. The table below represents the ANOVA report which assesses the overall significance of the regression model applied in this analysis. According to the findings, p Read More