Business Problem Proposal Mba 510

Topics: Regression analysis, Correlation and dependence, Pearson product-moment correlation coefficient Pages: 9 (4343 words) Published: November 30, 2008
Running head: BUSINESS PROBLEM PROPOSAL Business Problem Proposal University of Phoenix Business Problem Proposal Wal-Mart is a worldwide retail company. In 2008, Wal-Mart operated 971 discount stores, 2,447 super centers, 132 neighborhood markets, and 591 Sam’s Clubs in the United States (MarketLine, 2008). The company is headquartered in Bentonville, Arkansas and employs about 2.1 million people worldwide. The business problem to be illustrated is the high employee turnover that Wal-Mart experiences. A look into why employee retention is so high and the associated costs of this high turnover will be explored. Illustrations will show Wal-Mart’s problem of high turnover in statistical terms and list a set of recommendations to reduce employee turnover. Dependent and independent variables will be illustrated, the null and alternative hypothesis and the theories to support these hypotheses will be evaluated, primary and secondary data sources will be defined, how samples were selected and produced will be reviewed and finally recommendations will be made for improvement. Problem Statement Wal-Mart has a current need to change their organizational culture in a manner that the change will lead to an increase in employee retention and productivity. Analysis of Importance of the Problem The current situation at Wal-Mart is low paying wages and high turnover rate. Inadequate pay can be a major factor driving and organization’s turnover rate. A high turnover rate can be very costly to any organization, as the organization is constantly spending funds to train new employees. The higher the turnover rate the more funds are spent on training a higher number of new employees each year. These are unnecessary costs that Wal-Mart can invest in other areas of the business. Independent and Dependent Variables: Supported Evidence Wal-Mart’s turnover rate is the dependent variable. The dependent variable is defined as “the variable that is being predicted or estimated” (Lind, Marchal & Wathan, 2004, p. 431). Several independent variables can be found that influence the dependent variable. Independent variables are the variables that provide the basis for estimation, such as the predictor variable. (Lind et al.) Many different factors can contribute to the employee turnover rate at Wal-Mart Corporation. Examples could include the expensive healthcare costs, the inadequate pay, being asked to work off the clock, working conditions, stress level, employing mainly part-time positions, better offers within the industry, bad management, or bad company policy. The independent variables for the purpose of this illustration will be inadequate pay, high benefit costs, and lack of full time employment. Use of Correlation in Making Business Decisions Correlations can be used in making many business decisions. “Correlation analysis is the study of the relationship between variables” (Lind et al., 2004, p. 429). The usual first step for correlation analysis is to plot the related data in a scatter diagram. Here the independent and dependent variables are identified. The coefficient of correlation describes the strength of the relationship between two sets of interval-scaled or ration-scaled variables. Designated r, it is often referred to as Pearson’s r and as the Pearson product-moment correlation coefficient. It can assume any value from – 1.00 to + 1.00 inclusive. A correlation coefficient of - 1.00 or + 1.00 indicates perfect correlation. (Lind et al.) Scatter diagrams can show positive or negative correlations for data. “If there is no relationship between the two sets of variables, Pearson’s r is zero. A coefficient of correlation r close to zero shows that the linear relationship is quite weak” (Lind et al., p. 432). The strength of the correlation between two sets of variables does not depend on the direction in a positive or negative direction. (Lind et al.) The coefficient of determination “is computed by squaring the...

References: Apics (2005). Apics Dictionary, (11th ed). Alexandria, VA: Apics. Cooper, D., & Schindler, P. (2003). Business Research Methods (8th ed.). New York: The McGraw-Hill Companies. Dube, Arindrajit., & Jacobs, Ken. (2004). Hidden Cost of Wal-Mart Jobs. Retrieved online October 18, 2008, from site: html database. Health Care. Retrieved October 18, 2008, from Hexhunterkid , (2006). Wal-Mart: Rolling Back America. part 1: low wages, high turnover. Retrieved, October 18, 2008, from Jain, C. (2004). The Journal of Business Forecasting Methods and Systems. Retrieved October 18, 2008 from ProQuest. Kofinis, Chris (2007). New internal documents show Wal-mart forced to change attendance policy. Retrieved October 20, 2008, 2007, from Lin, Y. (2003). The effects of employees’ perceptions of leader’s leadership style on the job satisfaction of employees at small and medium enterprises in Taiwan. Retrieved October 18, 2008 from ProQuest. Lind, D., Marchal, W., and Wathen S. (2004). Statistical Techniques in Business & Economics (12th ed.). New York: The McGraw-Hill Companies. MarketLine Business Information Center. Wal-Mart Retrieved September 28, 2008 from EBCOShost database. Wal-Mart Watch. (2005). Low Prices at High Cost: Who Really Pays For Wal-Mart Workers ' Health Care. Retrieved October 18, 2008, from Wal-mart State by State Information, Retrieved October 17, 2008 from: Appendix A Strongly Neither Disagree Strongly Disagree Disagree Nor Agree Agree Agree 1 2 3 4 5 I feel I am being paid a fair amount for the work I do. 1 2 3 4 5 Raises are too few. 1 2 3 4 5 I feel satisfied with my chances for salary increases. 1 2 3 4 5 My supervisor is unfair to me. 1 2 3 4 5 I like my supervisor. 1 2 3 4 5 I am not satisfied with the benefits I receive. 1 2 3 4 5 The benefit package we have is satisfactory. 1 2 3 4 5 I would be very happy to spend the rest of my career 1 2 3 4 5 with Wal-Mart. Gender: Male Female Age: 15-19 20-29 30-39 40-49 50 and over Education: High School College How long have you worked with your present supervisor? years and months Prior experience in retail stores? No Yes If yes, how long? _years and months Appendix B {draw:frame} Note: n = is the size of the sample Z = is the standard normal value corresponding to the desired level of confidence S = in an estimate of the population deviation E = maximum allowable margin of error Appendix C {draw:frame} Note: Z = is the standard normal value corresponding to the desired level of confidence X = is the mean of the sample. {draw:line} u =is the hypothesized population mean. {draw:line} s = is the standard deviation of the sample. {draw:line} n = is the number of observations in the sample. Appendix D A stepwise multiple regression analysis of job satisfaction of pay *p<.05 The table above describes a statistically significant in overall perceived leadership styles predicting the job satisfaction of pay (R²=.23, F=162.317, P<.05). The standardized regression coefficient (Beta) of transformational leadership style was .513 (T=13.973, P<.05, the Beta of transactional leadership style was -..081 (T=2.237, P<.05), the Beta of laissez-faire leadership style was -.097 (4.208, P<.05). Therefore, transformation (Appendix D cont.) leadership style significantly and positively predicted job satisfaction of pay; transactional/laissez-faire leadership styles significantly and inversely predicted job satisfaction of pay. Overall perceived leadership style appeared as the significant predictor and explained 23% of the variances in job satisfaction of pay. In addition, transformation leadership style was the strongest single predictor among three perceived leadership styles for job satisfaction of pay and accounted for 21.5% of the variance. Appendix E The linear trend equation is Y’ = a + b (t). Note: Y’ = read Y prime, is projected value of the Y variable for the selected value of t. a = is the Y-intercept. It is estimated value of Y when t = 0. Another way to put it is: a is the estimated value of Y where the line crosses the Y-axis when t is zero. b = is the slope of the line, or the average change in Y’ for each increase of one unit in t. t = is any value of time that is selected. Appendix F The log trend equation: log Y’ = Log a + log b (t). {draw:frame} (Appendix F cont.) {draw:frame} According to Lind et al., 2004, The regression equation is Y’ = 2.053807 + 0.153357t_, _which is the log form. We now have a trend equation in terms of percent change. That is, the value 0.153357 is the percent change in Y_ for each unit increase in t. This value is similar to the geometric mean described in Chapter 3. The log of b is 0.153357 and its antilog or inverse is 1.423498. If we subtract 1 from this value, as we did in Chapter 3, the value 0.423498 indicates the geometric mean annual rate of increase from 1988 to 2002. We conclude that imports increased at a rate of 42.35 percent annually during the period.(p. 666)
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