When Should the Customer Really Be King - Assignment Example

BUSINESS ANALYTICS By Executive Summary In order to show how word-of-mouth in the contemporary business platform is influenced by factors such as income, attitude, and age, this research considered a sample of 332 respondents (N = 332). Quantitative analysis was carried out using SPSS (statistical software) where regression, descriptive analysis, and correlations were used to show how the model fits. Results showed that attitudes, age, and monthly income were variables that significantly correlated but did not have a connection with word-of-mouth. The research concluded by recommending further research that takes into account variables such as demographics, ages below 18 years, and former experience Data Analysis From SPSS’s output on table 1 under Appendix A, the number of respondents that took part in the survey are 332 using varying scales to code their responses. For the word of mouth (WOM), the minimum and maximum values for testing included 1 and 7 respectively while that of trust and attitude was 1 & 7 and 1 & 9 respectively. However, for the age and monthly income, it shows that the minimum age of the participants was 18 years and the maximum was 72 years while the minimum and maximum monthly income was 400 and 10000 respectively. The mean for word of mouth, trust, and attitude is 3.34, 3.35, and 5.12 respectively indicating that the responses were not skewed to the left or the right. This situation shows that respondents showed almost equal distribution around very high and very low account of WOM, trust, and attitudes. The mean age for the respondents was 35.10 year coupled with a standard deviation from the mean of ±13.898 (14 years). Lastly, the mean and standard deviation from the mean for monthly income for the respondents is $1893.07 and $1355.696 respectively. With relation to regression analysis, table 3 under Appendix B shows the r-squared value of 0.183 with an adjusted value of 0.170. This means that monthly income, trust, age, attitude, and word of mouth have a low correlation of 18.3% with an adjusted correlation of 17%. Under R-Squared, this values show that the benchmark expectation and the output do not correlate effectively and therefore, the tested variables do not move in the same direction do not respond to the benchmark values (see full SPSS output under Appendix B). In table 4 under Appendix B, B (beta) denotes the regression coefficients for the tested variables which derive the equation that the fit of the model is given as; WOM = 230.5 + 6.449 (7) = 236.949 Trust =230.5 + -6.111 (7) = 224.389 Age = 230 + -0.113 (72) = 230.387 Attitude = 230 + -18.145 (9) = 212.355 Monthly Income = 230.5 + 0.017 (10000) = 230.517 The interpretation of the output considered the regressions predictor which shows the differences in response per unit. The total responses and the respondents are 332 which in relation to the output, the constant shows the valid number of responses. This, in addition, shows how much each of the units differs from the valid number of responses considered (constant). The standardized coefficients or Beta indicate what the values of each model would be if the model was being fitted to a standardized data. Chart 1 below shows how the units are skewed to the either the right or left from the mean based on the predicted standard deviation of the correlation of the units. It shows how the means of the data are distributed with regards to the number of respondents weight of their responses. With respect to the correlations output, it is observed that the Pearson test aims at showing how each variable fits with the other. Respondents are the dependent variable and do not change, however, based on significance levels of 0.01 and 0.05 for 2-tailed analysis, it is shown that the best fit variables that relate to each other under the conditions include, trust and monthly income under respondents, trust in respondents affects decisions, age and attitude are related, and monthly income is related to the respondents’ decisions of taking vacations in West Africa. Shortcomings of Findings There are three classes of shortcomings in the findings. Firstly, the manner in which each variable is related to the other does not indicate whether a change in one variable affects another variable. For instance, if monthly income reduced, the findings do not show whether the attitude of the respondent would change. Secondly, age differences within the model are not clearly shown in terms of their relation with other variables such as Word-of-Mouth. In this case, if it not clear whether age and word-of-mouth in virtual platform are related. Third, the combination of factors such as age and monthly income do not add up to show whether respondents earnings and their experiences in touring West Africa affects their attitudes or increases their chances of providing digital feedback (Burns, and Bowling, 2010). In order to offset these shortcomings, it is suggested that an additional variable, perception or experience, be added to the model. The significance of the additional variable is to ensure that respondents’ word of mouth is connected to a perception or an experience. The merit of this consideration is that a model testing the relationship between word of mouth without an experience is flawed and does not provide reliable correlation. For instance, customer attitudes are influenced by factors such as preferences, cost, and delivery of service. Hence, while testing for attitude, this variable is concerned with perception which should relate with experience rather than face value opinion. In this case, it is also important that further survey for filtering responses be added to the model that tests whether respondents have in the past had an experience in touring West Africa and how they developed an attitude or a motivational factor to provide digital feedback (Dietz, Pugh, and Wiley, 2004). In addition, it is observed that other factors such as origin of the respondents, as well as conditions of the target destination have an influence in attitude regardless of whether monthly income is above the model’s mean is a factor of consideration influencing how respondents fathom the entire experience and value for their money. Hence, attitudes do not lead to word-of-mouth but experiences shape attitudes which in turn provides the bias in the perception as well as the content of the word-of mouth (Homburg, Klarman, 2011). According to research, word-of-mouth does not amount to accurate perception of an experience. For instance, for a first time visitor to West Africa and a regular visitor differ in their perceptions of the region and their word of mouth is likely to differ. First timers will ignore factors due to increased interests in one factor over another. Regular visitors in the other hand, will ignore one or several factors each visit but collectively, they will have a much reliable perception of the experience. In this, while further research is recommended, the frequency of visits and change in attitude should be considered to show how respondents’ word of mouth changes between first-time experience and multiple experience (Simon, D. et al. (2009). Recommendations Based on the findings, it is shown that monthly income, age, and attitudes are significantly correlated under a confidence level of 95%. These findings do not show any significant correlation between word-of-mouth and any other factor. Poor correlation does not amount to significance in this model. Therefore, three recommendations are essential in ensuring that findings can be significant and with relation to word-of mouth. Firstly, in order to achieve more accurate results that determine whether word-of-mouth is influenced by other factors, the respondents should be global and categorized demographically to show whether different regions and their corresponding inhabitants have preconceived perception of West Africa. Additionally, the demographical approach is to show whether respondents from certain regions visit West Africa more than others. The variability of demographics would suggest the differences in perception between one region and another as well as provide a predictive model for forecasting how positive or negative word of mouth from one region will differ from that of another (Vogel, and Evanschitzky, 2008). Secondly, while the minimum age of respondents is 18 as per the current model, it is observed that tourism in most cases is a family affair which takes adults as well as children as part of tourists. In this case, categorization of young ones and adults should be conducted to ensure that diversity of results may indicate whether children have a better perception of West Africa as compared to the adults. In most UK and US families, Blacks and Whites considered, the average number of children per family is 2 to 3. Considering 2 as the valid number of children in a family of two parents, it is considered inaccurate representation of the research if 50% of West Africa visitors are not given the change to air their word-of-mouth based on age. Third, preferences and tastes differ from one individual to another. Based on former experience and current experience, a respondent is likely to be negative on factors others find positive. Hence, as a recommendation to inform business decision, research on individual perceptions should consider factors such as industry benchmark in the provision of customer service. It may seem to a business model that customers are not receiving the best experience, but in real-life situation, the problem may be associated competitors service packages. References Burns, G., and Bowling, N. (2010). Dispositional Approach to Customer Satisfaction and Behavior. Journal of Business and Psychology, Vol. 25, No. 1; pp. 99-107 Dietz, J., Pugh, D., Wiley, J. (2004). Service Climate Effects on Customer Attitudes: An Examination of Boundary Conditions. The Academy of Management Journal, Vol. 47, No. 1; pp. 81-92 Homburg, C., Klarman, M. (2011). When Should the Customer Really Be King? On the Optimum Level of Salesperson Customer Orientation in Sales Encounters. Journal of Marketing, Vol. 75, No. 2 (March 2011), pp. 55-74. Simon, D. et al. (2009). Employee Attitudes, Customer Satisfaction, and Sales Performance: Assessing the Linkages in US Grocery Stores. Managerial and Decision Economics, Vol. 30, No. 1 (Jan., 2009), pp. 27-41 Vogel, V. and Evanschitzky, H. (2008). Customer Equity Drivers and Future Sales. Journal of Marketing, Vol. 72, No. 6; pp. 98-108 Appendix 1: Descriptive Analysis Table 1: Descriptive Statistics N Minimum Maximum Mean Std. Deviation Skewness Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Respondent 332 1 332 166.50 95.984 .000 .134 WOM 332 1 7 3.34 1.378 .318 .134 TRUST 332 1 7 3.35 1.527 .252 .134 AGE 332 18 72 35.10 13.898 .780 .134 ATTITUDE 332 1 9 5.12 2.139 -.135 .134 MONTHLY_INCOME_ 332 400 10000 1893.07 1355.696 2.702 .134 Valid N (listwise) 332 Appendix B: Regression Analysis Table 2: Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .428a .183 .170 87.427 .183 14.593 5 326 .000 a. Predictors: (Constant), MONTHLY_INCOME_, TRUST, AGE, ATTITUDE, WOM b. Dependent Variable: Respondent Table 3: ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 557723.801 5 111544.760 14.593 .000b Residual 2491779.199 326 7643.494 Total 3049503.000 331 a. Dependent Variable: Respondent b. Predictors: (Constant), MONTHLY_INCOME_, TRUST, AGE, ATTITUDE, WOM Table 4: Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. 95.0% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) 230.500 17.702 13.021 .000 195.676 265.325 WOM 6.449 4.570 .093 1.411 .159 -2.541 15.439 TRUST -6.111 3.630 -.097 -1.683 .093 -13.253 1.031 AGE -.113 .372 -.016 -.304 .762 -.845 .619 ATTITUDE -18.145 2.689 -.404 -6.747 .000 -23.435 -12.854 MONTHLY_INCOME_ .017 .004 .237 4.330 .000 .009 .024 a. Dependent Variable: Respondent Table 5: Residuals Statisticsa Minimum Maximum Mean Std. Deviation N Predicted Value 72.35 354.85 166.50 41.048 332 Residual -204.785 207.455 .000 86.764 332 Std. Predicted Value -2.294 4.589 .000 1.000 332 Std. Residual -2.342 2.373 .000 .992 332 a. Dependent Variable: Respondent Graph 5: Regression Standardized Residual Appendix C: Correlations Table 6: Descriptive Statistics Mean Std. Deviation N Respondent 166.50 95.984 332 WOM 3.34 1.378 332 TRUST 3.35 1.527 332 AGE 35.10 13.898 332 ATTITUDE 5.12 2.139 332 MONTHLY_INCOME_ 1893.07 1355.696 332 Table 7: Correlations Respondent WOM TRUST AGE ATTITUDE MONTHLY_INCOME_ Respondent Pearson Correlation 1 -.115* -.166** .004 -.342** .164** Sig. (2-tailed) .037 .002 .949 .000 .003 Sum of Squares and Cross-products 3049503.000 -5021.500 -8072.000 1571.000 -23269.500 7080750.000 Covariance 9213.000 -15.171 -24.387 4.746 -70.301 21391.994 N 332 332 332 332 332 332 WOM Pearson Correlation -.115* 1 .489** .195** .537** .255** Sig. (2-tailed) .037 .000 .000 .000 .000 Sum of Squares and Cross-products -5021.500 628.539 340.518 1237.108 523.726 157932.831 Covariance -15.171 1.899 1.029 3.737 1.582 477.138 N 332 332 332 332 332 332 TRUST Pearson Correlation -.166** .489** 1 .110* .334** .095 Sig. (2-tailed) .002 .000 .044 .000 .084 Sum of Squares and Cross-products -8072.000 340.518 771.470 775.819 361.373 65053.614 Covariance -24.387 1.029 2.331 2.344 1.092 196.537 N 332 332 332 332 332 332 AGE Pearson Correlation .004 .195** .110* 1 .173** .348** Sig. (2-tailed) .949 .000 .044 .002 .000 Sum of Squares and Cross-products 1571.000 1237.108 775.819 63930.916 1699.241 2170071.687 Covariance 4.746 3.737 2.344 193.145 5.134 6556.108 N 332 332 332 332 332 332 ATTITUDE Pearson Correlation -.342** .537** .334** .173** 1 .201** Sig. (2-tailed) .000 .000 .000 .002 .000 Sum of Squares and Cross-products -23269.500 523.726 361.373 1699.241 1514.419 192720.181 Covariance -70.301 1.582 1.092 5.134 4.575 582.236 N 332 332 332 332 332 332 MONTHLY_INCOME_ Pearson Correlation .164** .255** .095 .348** .201** 1 Sig. (2-tailed) .003 .000 .084 .000 .000 Sum of Squares and Cross-products 7080750.000 157932.831 65053.614 2170071.687 192720.181 608349066.265 Covariance 21391.994 477.138 196.537 6556.108 582.236 1837912.587 N 332 332 332 332 332 332 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed). Read More