Effectiveness in the Use a New Instagram Page by Gogym - Case Study Example

1.0 EXECUTIVE SUMMARY In this paper the result of the effectiveness in the use a new Instragram page by Gogym has been given. Under descriptive analysis it was found that there was some difference among the different psychographic groups where with use of tablet being less popular in the social poster group. Under inferential analysis the female were found to have higher interactive time than their male counterparts but this difference was found not to be statistically significant. Linear regression was used in coming upper with models that relates the number of visits with other variables in the different psychographic groups where models were found to be statistically significant in all three groups. It was recommended that there should be use the Instagram as a promotional tool as it was found to be very effective. Table of contents 2.0Descriptive analysis 4 2.1 Extent to which device used differs across the three psychographic groups 4 2.2 Extent to which time of the day in accessing the instagram page differs across four age groups 5 2.3 The extent to which the interaction time differs across gender 5 2.4The variability of results for the 4 items examining \“Social Posers\ 6 2.5 Use of measures of central tendency in interpretation of the social media dependency of \“New-to-Health\ 6 2.6 Recommendations 6 3.0 Results and interpretation for inferential analyses 7 3.1 The relationship between the gender of users and their interaction times with the Instagram page. 7 3.2 The extent to which the perceptions of the Instagram page differ across four age groups 7 3.3 Comparison of users aged 25-35’s interaction time with the Instagram page with the device used to access the page. [CHI-SQUARE] 8 3.4 Comparison of the relationship between the time of day users access the social media site and the interaction time with the Instagram page for the two genders. [CHI-SQUARE] 8 3.5 Comparison of how the social media dependency, interaction time, and perception of the Instagram site of the three psychographic group’s influences their number of monthly visits to the site (Linear regression ) 9 3.6 Recommendations for inferential analyses 10 References 11 List of tables 2.0Descriptive analysis 2.1 Extent to which device used differs across the three psychographic groups From table 1 it can be seen that in all the psychographic groups mobile was the device with the highest frequency with new-to-fit group having the highest frequency at 56 which was 59.6% of the total in this group. With regard to tablet use it can be seen that the use of this device was lower compared to those using mobile with the lowest score being recorded in the social poster group with 9.1% of those in this group using the tablet. With regard to computer use the highest score was posted by the social poster group with 31.8%. The low tablet use of the social poster group and higher use of computer could be an indication that this group are likely to consist of younger people who are not economically stable and may not be able to afford a tablet. High use of computers by this group could be an indication that they access this facilities easily at home or in public places without incurring high costs. 2.2 Extent to which time of the day in accessing the instagram page differs across four age groups From table it can be seen that morning is the time where there is highest access to instagram in all the age groups except in the over 50 yrs group where afternoon has much higher score. It can also be seen that for morning time the highest frequency is 30 recorded in the under 20% age group this being 36.1% of the total in this age group. In the night the group with the highest score is the under 20 with a frequency of24 representing 28.9% of the total in this age group. The over 50 group also had a higher score at this time with a frequency of 11 representing 23.4% of the age group. The pattern for night use of instagram could be attributed to the fact that both the under 20 and over 50 have free time for themselves because the under 20 have no family responsibilities due to their young age while the over 50 are likely to have grown up children and thus they have a lot a free time for themselves just as in the case of the under 20. 2.3 The extent to which the interaction time differs across gender From table 3 it can be seen that the number of those who had interaction time of over 60s (1min) was higher in female at 23 (21.5%) compared to 24 (19.7%). It can also be seen that for the interaction time of 41-60s female scores was higher at 29.0% compared to 23,0% in male. It can also be seen that for the lower interaction time groups male had higher scores than female. This is an indication that female spend more time on instagram or online in general than their male counterparts. 2.4The variability of results for the 4 items examining “Social Posers” social media dependency From table it can be seen that the highest variability is recorded in item 3 (social media dependency 3) where we have a standard deviation of 1.62. Can also be seen that the item also had the lowest mean score of 3.44. The lower scores can be attributed to the fact that this item was phrased in a way that the response was the reverse of the other three items ie the high values on this items was to be registered for those with dislike to using social media while in the items those with positive attitude towards use of social media were to have high scores. 2.5 Use of measures of central tendency in interpretation of the social media dependency of “New-to-Health” and “Gym Junkies”. From table 5 it can be observed that the mean and mode responses were equal in both the new to health and the gym junky groups with a value of 5 for item 1, and item 2 , and in item 3 the scores for the median and mode were 3 and 2 respectively in both groups while in item 4 we had a median and mode values of 4 in both the groups. It can also be seen that the mean values in the three items were also close in two groups. This shows there is a lot of similarity in the two psychographic groups with regards to the use of social media. 2.6 Recommendations From the results it has been seen that the social posers are likely to be young and less economically stable in comparison to other psychographic groups. This group is likely to be much more online throughout the day and even at night. There is need for GoGym to avail content on line that resonate with this group and also use younger people on the site such that they can interact with this group more easily. Also there is need to come up with much affordable packages and promote this online for the benefit of the social posers. The content should also be content for the other groups where the benefits of exercises need to be highlighted. It can also be seen that female seem to have higher presence on social media than males and thus this avenue should be used more to target them , 3.0 Results and interpretation for inferential analyses 3.1 The relationship between the gender of users and their interaction times with the Instagram page. From table 6 it can be observed that the length of time for female is slightly higher than that of male where the mean for the former is 43.7s and the later the mean is 42.1s. However, from table 7 it can be observed that the difference in length of time in male and female is not statistically significant where t(227)=0.514 (p>0.05). This is an indication that when instagram is used for promotional services both male and female will be reached equally. 3.2 The extent to which the perceptions of the Instagram page differ across four age groups From table 8 it can be observed that there was highest mean value in age group 25-35 where the mean value of 27.4 was registered while the least mean value was in the 36-50 age group with a mean value of 21.9. From table 9 it can be seen that the difference was not statistically significant; F(3)=0.86 (P>0.05). This can be seen as an indication that intagram is not seen to be highly suitable for a particular group than the other but can be used as a tool for promotion targeting all age groups. 3.3 Comparison of users aged 25-35’s interaction time with the Instagram page with the device used to access the page. [CHI-SQUARE] From table 10 it can be seen that in age 25-35 those using mobile were the majority just as in the other age groups with the actual count being 34 slightly lower that the expected value of 36.9. With regards to those using a tablet the actual count was higher in the 25-35 group with a score of 12 compared to expected count of 10.7. From table 11 it can be seen that the relationship between type of device and age group is not statistically significant; F(6)=5.878 (p>0.05) 3.4 Comparison of the relationship between the time of day users access the social media site and the interaction time with the Instagram page for the two genders. [CHI-SQUARE] From table 12 it can be seen that with regards to female in morning hours the actual count was lower than the expected count in for those in 0.05). 3.5 Comparison of how the social media dependency, interaction time, and perception of the Instagram site of the three psychographic group’s influences their number of monthly visits to the site (Linear regression ) Tables 15, 16 and 17 gives the results of linear regressions. From table 15 it can be seen that for the psychographic group new to health variables can explain 52.9% of the variations in the number of visits per month; in psychographic social poster the variables can explain 70.4% of variation while in gym junky the variables can explain 74.9% of variation in the number of visits per month. Table 16 shows that all the 3 models are statistically where for new-to-health F(5)= 68.483 (p=0.000); for social poster F(9)=35.419 (p=0.000) and for gym junky F(9)=23.873(p=0.000). Table 17 gives the details of the details of the coefficients where those that are statistically significant are to be included in the model. For new-to-health there are two B coefficients which statistically significant; the constant -5.494 (p=0.001) and B coefficient for perception of instagram 0.064 (p=0.000). For social poster the B coefficients which are statistically significant are for social media dependency 1 0.049 (p=0.003) and perception of instagram 0.06 (p=0.000). For the case of gym junky the significant B coefficients are for length of time 0.049 (p=0.005) and perception of instagram 0.149 (0.000). Now taking Y to be the number of monthly visits the regression model for the three cases will be For new-to-health Y=-5.494+0.064X6 For social poster Y= 0.049X2+0.06X6 For gym junky Y= 0.049X1+0.149X6 From this result it can be seen the three models can be used in prediction the number of monthly visits. 3.6 Recommendations for inferential analyses From the analysis it has been seen that instagram use is not biased for any particular gender or any particular age group. This shows that it can be used to target a wide range of audience. It is therefore recommended that GoGym should invest more on Instagram have more hosts online for interaction with the audience. The content online should be suitable for all age groups and need to be easy to understand and entertaining. References Malhotra, NK 2010, ’Marketing Research: An Applied Orientation’ Global Edition, Pearson Education, New Jersey Oz, E. (2000) Management Information Systems (2nd ed.). Course Technology Whitley E. and Ball J. (2002). Statistics Reviews 3. Hypothesis testing and p-values. Appendix 1 Table 1 Device Frequencies New to health Social poster Gym junky Mobile 56(59.6%) 52(59.1%) 26(55.3%) Tablet 20(21.3%) 8(9.1%) 11(23.4%) Computer 18(19.1%) 28(31.8%) 10(21.3%) Total 94(100%) 88(100%) 47(100%) Table 2 Device Frequencies 50yrs Morning 30(36.1%) 22(34,9) 13(36.1%) 14(29.8%) Mid-day 14(16.9%) 15(23.8%) 4(11.1%) 6(12.8%) Afternoon 15(18.1%) 13(20.6%) 7(19.4%) 16(34.0%) Night 24(28.9%) 13(20.6%) 12(19,4%) 11(23.4%) Total 83(100%) 63(100%) 36(100%) 47 Table 3 Interaction time Frequencies Female Male 60sec 23(21,5%) 24(19.7%) Total 107(100%) 122(100%) Appendix 2 Table 4 Descriptive Statistics N Minimum Maximum Mean Std. Deviation social media dependancy1 94 1.00 7.00 4.3936 1.60790 social media dependancy2 94 1.00 7.00 4.4468 1.45630 social media dependancy3 94 1.00 7.00 3.4468 1.62386 social media dependancy4 94 1.00 7.00 4.3936 1.50425 Valid N (listwise) 94 Table 5 Descriptive Statistics N Minimum Maximum Mean Std. Deviation social media dependancy1 94 1.00 7.00 4.3936 1.60790 social media dependancy2 94 1.00 7.00 4.4468 1.45630 social media dependancy3 94 1.00 7.00 3.4468 1.62386 social media dependancy4 94 1.00 7.00 4.3936 1.50425 Valid N (listwise) 94 Appendix 3 Table 6 Statistics psychographics social media dependancy1 social media dependancy2 social media dependancy3 social media dependancy4 New-to-health N Valid 94 94 94 94 Missing 0 0 0 0 Mean 4.3936 4.4468 3.4468 4.3936 Median 5.0000 5.0000 3.0000 4.0000 Mode 5.00 5.00 2.00 4.00 Gym junky N Valid 47 47 47 47 Missing 0 0 0 0 Mean 4.5745 4.6809 3.1277 4.4468 Median 5.0000 5.0000 3.0000 4.0000 Mode 5.00 5.00 2.00 4.00 a. Multiple modes exist. The smallest value is shown Appendix 4 Table 8 Table 9 Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper Length of time Equal variances assumed .230 .632 .514 227 .608 1.60257 3.11566 -4.53675 7.74190 Equal variances not assumed .511 215.560 .610 1.60257 3.13610 -4.57877 7.78392 Appendix 5 Table 10 Descriptives Perception of instagram N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum Lower Bound Upper Bound 50 47 23.0851 17.13607 2.49955 18.0538 28.1164 .00 60.00 Total 229 24.7336 18.26255 1.20682 22.3557 27.1116 .00 60.00 Table 11 ANOVA Perception of instagram Sum of Squares df Mean Square F Sig. Between Groups 861.720 3 287.240 .860 .463 Within Groups 75181.031 225 334.138 Total 76042.751 228 Appendix 6 Table 12 Device used * Age group of respondents Crosstabulation Age group of respondents Total 50 Device used mobile Count 48 34 18 34 134 Expected Count 48.6 36.9 21.1 27.5 134.0 % within Device used 35.8% 25.4% 13.4% 25.4% 100.0% tablet Count 13 13 7 6 39 Expected Count 14.1 10.7 6.1 8.0 39.0 % within Device used 33.3% 33.3% 17.9% 15.4% 100.0% computer Count 22 16 11 7 56 Expected Count 20.3 15.4 8.8 11.5 56.0 % within Device used 39.3% 28.6% 19.6% 12.5% 100.0% Total Count 83 63 36 47 229 Expected Count 83.0 63.0 36.0 47.0 229.0 % within Device used 36.2% 27.5% 15.7% 20.5% 100.0% Table 13 Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 5.878a 6 .437 Likelihood Ratio 6.057 6 .417 Linear-by-Linear Association 1.692 1 .193 N of Valid Cases 229 a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 6.13. Appendix 7 Table 14 Time of the day * Interaction time grouping Crosstabulation Gender of respondent Interaction time grouping Total 60 FEMALE Time of the day Morning Count 11 2 14 8 35 Expected Count 11.1 6.2 10.1 7.5 35.0 % within Time of the day 31.4% 5.7% 40.0% 22.9% 100.0% Mid-day Count 8 3 5 4 20 Expected Count 6.4 3.6 5.8 4.3 20.0 % within Time of the day 40.0% 15.0% 25.0% 20.0% 100.0% After noon Count 9 4 5 8 26 Expected Count 8.3 4.6 7.5 5.6 26.0 % within Time of the day 34.6% 15.4% 19.2% 30.8% 100.0% Night Count 6 10 7 3 26 Expected Count 8.3 4.6 7.5 5.6 26.0 % within Time of the day 23.1% 38.5% 26.9% 11.5% 100.0% Total Count 34 19 31 23 107 Expected Count 34.0 19.0 31.0 23.0 107.0 % within Time of the day 31.8% 17.8% 29.0% 21.5% 100.0% MALE Time of the day Morning Count 19 9 8 8 44 Expected Count 14.4 10.8 10.1 8.7 44.0 % within Time of the day 43.2% 20.5% 18.2% 18.2% 100.0% Mid-day Count 2 4 6 7 19 Expected Count 6.2 4.7 4.4 3.7 19.0 % within Time of the day 10.5% 21.1% 31.6% 36.8% 100.0% After noon Count 8 8 5 4 25 Expected Count 8.2 6.1 5.7 4.9 25.0 % within Time of the day 32.0% 32.0% 20.0% 16.0% 100.0% Night Count 11 9 9 5 34 Expected Count 11.1 8.4 7.8 6.7 34.0 % within Time of the day 32.4% 26.5% 26.5% 14.7% 100.0% Total Count 40 30 28 24 122 Expected Count 40.0 30.0 28.0 24.0 122.0 % within Time of the day 32.8% 24.6% 23.0% 19.7% 100.0% Appendix 8 Table 15 Chi-Square Tests Gender of respondent Value df Asymp. Sig. (2-sided) FEMALE Pearson Chi-Square 15.173a 9 .086 Likelihood Ratio 14.750 9 .098 Linear-by-Linear Association .543 1 .461 N of Valid Cases 107 MALE Pearson Chi-Square 10.164b 9 .337 Likelihood Ratio 10.478 9 .313 Linear-by-Linear Association .010 1 .920 N of Valid Cases 122 a. 4 cells (25.0%) have expected count less than 5. The minimum expected count is 3.55. b. 4 cells (25.0%) have expected count less than 5. The minimum expected count is 3.74. Appendix 9 Table 16 Model Summary psychographics Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change New-to-health 1 .748a .560 .529 2.24937 .560 18.438 6 87 .000 social poster 1 .851b .724 .704 1.32343 .724 35.419 6 81 .000 Gyn junky 1 .884a .782 .749 1.88953 .782 23.873 6 40 .000 a. Predictors: (Constant), Perception of instagram, Length of time, social media dependancy3, social media dependancy2, social media dependancy4, social media dependancy1 b. Predictors: (Constant), Perception of instagram, Length of time, social media dependancy3, social media dependancy4, social media dependancy2, social media dependancy1 Table 17 ANOVAa psychographics Model Sum of Squares df Mean Square F Sig. New-to-health 1 Regression 559.725 6 93.288 18.438 .000b Residual 440.190 87 5.060 Total 999.915 93 social poster 1 Regression 372.211 6 62.035 35.419 .000c Residual 141.869 81 1.751 Total 514.080 87 Gyn junky 1 Regression 511.399 6 85.233 23.873 .000b Residual 142.813 40 3.570 Total 654.213 46 a. Dependent Variable: Number of time visited b. Predictors: (Constant), Perception of instagram, Length of time, social media dependancy3, social media dependancy2, social media dependancy4, social media dependancy1 c. Predictors: (Constant), Perception of instagram, Length of time, social media dependancy3, social media dependancy4, social media dependancy2, social media dependancy1 Appendix 10 Table 18 Coefficientsa psychographics Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta New-to-health 1 (Constant) -5.494 1.628 -3.375 .001 Length of time .019 .011 .129 1.716 .090 social media dependancy1 .349 .292 .171 1.192 .236 social media dependancy2 .343 .321 .152 1.067 .289 social media dependancy3 .364 .203 .180 1.793 .077 social media dependancy4 .571 .290 .262 1.973 .052 Perception of instagram .064 .016 .359 4.107 .000 social poster 1 (Constant) -1.271 1.041 -1.221 .226 Length of time .008 .006 .086 1.365 .176 social media dependancy1 .491 .160 .336 3.074 .003 social media dependancy2 .234 .168 .149 1.395 .167 social media dependancy3 -.015 .130 -.010 -.115 .909 social media dependancy4 .048 .158 .028 .305 .761 Perception of instagram .060 .010 .439 6.037 .000 Gyn junky 1 (Constant) -2.820 2.326 -1.212 .232 Length of time .049 .016 .290 2.965 .005 social media dependancy1 -.203 .430 -.077 -.472 .640 social media dependancy2 -.056 .424 -.021 -.133 .895 social media dependancy3 .288 .311 .105 .926 .360 social media dependancy4 .345 .465 .116 .741 .463 Perception of instagram .149 .025 .727 5.889 .000 a. Dependent Variable: Number of time visited Read More