Progress of Social Media in the UK - Report Example

DATA ANALYSIS RESULTS AND DISCUSSIONS The data was collected room the twitter search engine of two movie bookings and their number of tweets respectively. A Pearson correlation coefficient was conducted to determine the relationship between the dependent and the independent variables. This chapter thus will highlight the analysis results and bring them to perspective. Correlation analysis Testing hypotheses H1. Four factors (Price, genre, tweets, and frequency of tweets) and VOD booking have positive correlation The hypothesis test whether the four factors (Price, genre, tweets, frequency of tweets) and VOD booking have related positively The results of a regression analysis show that the four factors together predict VOD booking significantly, F=105.635, p < .001 as illustrated in Table 1. The Second regression analysis results indicate that the three factors (Price, genre, tweets) together predict VOD booking, F=80.389, p < .001 as illustrated in Table 2. The third regression analysis results indicate tweets, which is a technically an advertisement strategy predict VOD booking, F=128.790, p < .001 as illustrated in Table 3. This is explained by the fact that the Social Media Analytics asserts the intersections of social network including twitter and business at large. Moreover, it has focused more on dynamic capabilities, awareness motivation, organizational benefits and information technology resources (Yeates, 2002). Table 1: ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 21.981 1 21.981 105.635 .000a Residual 19.768 95 .208 Total 41.749 96 a. Predictors: (Constant), four factors (Price, genre, tweets, frequency of tweets) b. Dependent Variable: mean VOD booking Table 2: ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 19.135 1 19.135 80.389 .000a Residual 22.613 95 .238 Total 41.749 96 a. Predictors: (Constant), three factors (Price, genre, tweets,) b. Dependent Variable: VOD booking Table 3: ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 24.026 1 24.026 128.790 .000a Residual 17.723 95 .187 Total 41.749 96 a. Predictors: (Constant), tweets b. Dependent Variable: mean VOD booking Table 4 shows regression analysis results comparing three items including three factirs, four factors, and number of tweets. The four senses (feel, think, sense, act) together predict customer satisfaction, F=80.389. Four factors (Price, genre, tweets, frequency of tweets,) together predict VOD booking, F=105.635. Number of tweets predict VOD booking, F=128.790. The F values of the regression analysis indicate that number of tweets had a higher positive correlation as compared to four factors to VOD bookings. There was a significant affirmation that the number of tweets influenced the number of bookings of the video on demand. According to Kaplan & Haenlein (2010), social media has contributed largely toward customer’s perceptions and attitude on products and services. Social media has provided organizations with a variety of opportunities. In terms of marketing, social media has been in the forefront in cutting marketing cost, and this leads to a higher profit margin. The results is further asserted by the fact thet Tweeter is seen to be in the front position in transmitting information on world happening. The retweeting function is the main strength that makes tweeter stand out among all the social media platforms (Compeau & Higgins, 1995). Business managers can use tweeter in learning the changing consumer behavior for the purposes of business planning. The number of tweets attribute had a fundamental role as a advertising strategy with a significantly positive relationship to VOD bookings. Thus, the study fails to reject the null hypothesis and conclude that the four factors (Price, genre, tweets, and frequency of tweets) and VOD bookings have a positive correlation. Price and frequency of tweets individually had no correlation to VOD bookings. This is explained by the fact that the prices of the VOD were universal and the locations of the participants were not vast enough. The sample tweets were collected from a homogenous population who shared most of their properties across the study area. Table 4: Analysis of regression Predictors & Dependent Variable F Sig. Three factors and VOD booking 80.389 P Read More