Impact of social media marketing on Consumer buying behavior

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Cambridge College *

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574

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Marketing

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Nov 24, 2024

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1 Impact of Social Media Marketing on Consumer Buying Behavior Student’s Name Institutional Affiliation Course Number and Name Instructor’s Name and Title Due Date

2 Impact of Social Media Marketing on Consumer Buying Behavior Purpose Statement and Model This research paper aims at conducting analysis on how social media marketing affects consumer buying behavior in the United States. Consumer buying behavior is the dependent variable in this analysis, which will be measured by the amount of money spent on purchasing products or services. The primary independent variable is social media marketing, this is how social media platforms are used for marketing purposes. This model will be formulated as follows: Consumer Buying Behavior = β0 + β1 (Social Media Marketing) + ε Where: β0: intercept β1: coefficient (how social media marketing affects consumer buying behavior) ε: error term Definition of variables Consumer Buying Behavior: It is defined as the amount of money spent on purchasing products or services. This variable has been chosen as the dependent variable as it helps to measure the impact of social media marketing on consumers' purchase decisions (Chukwu et al., 2019). Social Media Marketing: Refers to how social media platforms can be useful for marketing purposes (Li et al., 2020). This variable is chosen as the primary independent variable as it aids to do analysis on how social media affects consumer purchasing decisions. Expectations for each Independent Variable Social Media Marketing:

3 The variable is defined in the data source as the use of social media platforms for marketing purposes. The unit of measurement used is the amount of money spent on social media marketing campaigns. Social media marketing is expected to determine a change in consumer buying behavior by influencing their purchase decisions through targeted marketing strategies (Li et al., 2020). The sign of the independent variable's coefficient is expected to be positive, indicating that an increase in social media marketing efforts leads to an increase in consumer buying behavior. Data Description The data for this analysis will be obtained from two general sources: academic journals and industry reports. The specific tables used for the analysis will be mentioned in the paper. The data collected for this research paper will be from the years 2015-2020. One of the limitations of the data is that it may not be entirely up to date, as social media platforms' algorithms and marketing techniques are constantly evolving. Presentation and Interpretation of Results SUMMARY OUTPUT Regression Statistics Multiple R 0.96619 R Square 0.93352 2 Adjusted R Square 0.93248 3 Standard Error 664.634 6 Observations 66

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4 ANOVA df SS MS F Significanc e F Regression 1 3.97E+08 3.97E+0 8 898.726 9 2.17E-39 Residual 64 28271308 441739. 2 Total 65 4.25E+08 Coefficient s Standard Error t Stat P-value Lower 95% Upper 95% Intercept -1936.88 250.5015 -7.73202 9.57E-11 -2437.32 -1436.45 Consumer Buying Behavior (in USD) 0.854647 0.028508 29.9787 7 2.17E-39 0.797695 0.91159 9 The regression (prediction) equation based on the results of the analysis is: y = -1936.88 + 0.855x The regression analysis indicates that the independent variable "Consumer Buying Behavior" has a significant positive impact on the dependent variable, which is not specified in the summary output. The value of the multiple correlation coefficient (R) is 0.966, which shows a strong positive relationship between the two variables. The R-squared (0.934) indicates that 93.4% of the variation in the dependent variable can be explained by the independent variable. The Adjusted R-squared (0.932) indicates that the model is a good fit for the data. The ANOVA table shows that the regression model is statistically significance, with an F- statistic of 898.73 and a p-value of 2.17E-39. This suggests that the model is a good fit for the data and that the independent variable "Consumer Buying Behavior" is a significant predictor of the dependent variable. The coefficients table shows that the intercept is -1936.88, indicating that when the value of "Consumer Buying Behavior" is zero, the value of the dependent variable is expected to be approximately -1936.88. The coefficient for "Consumer Buying Behavior" is

5 0.855, indicating that for every one unit increase in "Consumer Buying Behavior", the value of the dependent variable is expected to increase by approximately 0.855 units. The Adjusted R-squared is a statistical measure that evaluates how well a regression model fits the data by accounting for the number of independ variables that exists in the model. In the provided analysis, the Adjusted R-squared value is 0.932, indicating that approximately 93.2% of the variation in the dependent variable can be explained by the independent variable "Consumer Buying Behavior." This shows that the model is a good fit for the data, and that the variable "Consumer Buying Behavior" explains a large portion of the variation in the dependent variable. Additionally, the Adjusted R-squared considers the number of independent variables in the model, which helps to prevent overfitting and ensures that the model is not giving an inflated view of its predictive power. In this case, the high value of the Adjusted R-squared suggests that the model is not overfitting the data and is a good representation of the relationship between the independent and dependent variables. Discussion of Empirical Results The regression analysis shows a strong positive relationship between the independent variable and the dependent variable. The analysis indicates that "Consumer Buying Behavior" is a significant predictor of the dependent variable, with a coefficient of 0.855 and a p-value of 2.17E-39, which suggests that the relationship between the two variables is not due to chance. The value of the multiple correlation coefficient (R) is 0.966, indicating a strong positive relationship between the two variables. The R-squared value of 0.934 indicates that approximately 93.4% of the variation in the dependent variable can be explained by the independent variable, which suggests that "Consumer Buying Behavior" is an important

6 predictor of the dependent variable. The Adjusted R-squared value of 0.932 indicates that the model is a good fit for the data and that the independent variable "Consumer Buying Behavior" is a significant predictor of the dependent variable. Therefore, the empirical results suggest that as "Consumer Buying Behavior" increases, the value of the dependent variable is also expected to increase. Overall, the regression analysis provides evidence that "Consumer Buying Behavior" is an important factor to consider when predicting the value of marketing.

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7 References Chukwu, B., Kanu, E., & Ezeabogu, A. (2019). The impact of advertising on consumers buying behaviour. International Journal of Arts and Commerce , 8 (1), 1–15. https://ijac.org.uk/articles/8.1-1.1-15.pdf Li, F., Larimo, J., & Leonidou, L. C. (2020). Social media marketing strategy: definition, conceptualization, taxonomy, validation, and future agenda. Journal of the Academy of Marketing Science , 49 (1), 51–70. Springer. https://doi.org/10.1007/s11747-020-00733-3

Impact of social media marketing on Consumer buying behavior

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