Assessment Of Social Media Marketing For Australian Automobile Industry
Quantitative analysis
To assess if social media marketing is beneficial for increasing the profitability of the Australian automobile industry
Reliability analysis of the data shows that the internal consistency of the data set increases with addition of one item and the values are still significant. It also shows that value of alpha decreases if one of the items are deleted.
|
Reliability Statistics |
|
|
Cronbach’s Alpha |
N of Items |
|
.988 |
12 |
|
Item Statistics |
|||
|
Mean |
Std. Deviation |
N |
|
|
quality advice |
2.38 |
1.413 |
50 |
|
bulk information |
2.44 |
1.232 |
50 |
|
price comparison |
2.40 |
1.355 |
50 |
|
reliable information |
2.42 |
1.247 |
50 |
|
interactive platform |
2.46 |
1.358 |
50 |
|
variety of information |
2.34 |
1.394 |
50 |
|
user reviews |
2.38 |
1.369 |
50 |
|
easy decisions |
2.40 |
1.309 |
50 |
|
waiting time |
2.34 |
1.303 |
50 |
|
2.50 |
1.329 |
50 |
|
|
review and compare |
2.36 |
1.336 |
50 |
|
revenue and profit margin |
2.28 |
1.341 |
50 |
|
Item-Total Statistics |
||||
|
Scale Mean if Item Deleted |
Scale Variance if Item Deleted |
Corrected Item-Total Correlation |
Cronbach’s Alpha if Item Deleted |
|
|
quality advice |
26.32 |
186.793 |
.938 |
.986 |
|
bulk information |
26.26 |
191.992 |
.922 |
.986 |
|
price comparison |
26.30 |
188.663 |
.926 |
.986 |
|
reliable information |
26.28 |
191.512 |
.925 |
.986 |
|
interactive platform |
26.24 |
188.594 |
.926 |
.986 |
|
variety of information |
26.36 |
187.051 |
.944 |
.986 |
|
user reviews |
26.32 |
188.222 |
.929 |
.986 |
|
easy decisions |
26.30 |
189.969 |
.923 |
.986 |
|
waiting time |
26.36 |
189.868 |
.931 |
.986 |
|
quick decision making |
26.20 |
189.020 |
.936 |
.986 |
|
review and compare |
26.34 |
189.168 |
.926 |
.986 |
|
revenue and profit margin |
26.42 |
190.371 |
.887 |
.987 |
|
revenue and profit margin |
|||||
|
|
Frequency |
Percent |
Valid Percent |
Cumulative Percent |
|
|
Valid |
Strongly agree |
18 |
36.0 |
36.0 |
36.0 |
|
Agree |
16 |
32.0 |
32.0 |
68.0 |
|
|
Indifferent |
5 |
10.0 |
10.0 |
78.0 |
|
|
Disagree |
6 |
12.0 |
12.0 |
90.0 |
|
|
Strongly disagree |
5 |
10.0 |
10.0 |
100.0 |
|
|
Total |
50 |
100.0 |
100.0 |
Table 40
The question scrutinises whether social media marketing enhances the profit margin and revenue of the organisations in the automobile industry o not. .As per the result, 36% of the respondent have strongly agreed, 32% have agreed to the question and 10% are indifferent. On the contrary, 12% have disagreed and 10% have strongly disagreed to the question. This shows that majority of the respondents have agreed to the fact social media marketing enhances the profitability of organisations. Social media marketing reduces the overall cost of marketing which results in increase in profit margin and effective use of resources.
The correlation is used to check the relationship between the different elements of social media marketing and profitability of the organisation. The correlation among revenue and profit margin and quality advice, bulk information and price comparison shows strong positive relationship where the values ranges within .814 to .837 which is significant at two tailed and 99% confidence interval. This shows that this relationship is highly significant.
Similarly, while evaluating the relationship among revenue and profit margin and reliable information and interactive platform, it shows strong positive relationship. This clearly shows that there is high positive correlation between the different elements of social media marketing and organisational profitability in automobile industry.
|
Correlations |
||||
|
quality advice |
bulk information |
price comparison |
||
|
quality advice |
Pearson Correlation |
1 |
.876** |
.889** |
|
Sig. (2-tailed) |
.000 |
.000 |
||
|
N |
50 |
50 |
50 |
|
|
bulk information |
Pearson Correlation |
.876** |
1 |
.822** |
|
Sig. (2-tailed) |
.000 |
.000 |
||
|
N |
50 |
50 |
50 |
|
|
price comparison |
Pearson Correlation |
.889** |
.822** |
1 |
|
Sig. (2-tailed) |
.000 |
.000 |
||
|
N |
50 |
50 |
50 |
|
|
reliable information |
Pearson Correlation |
.869** |
.914** |
.841** |
|
Sig. (2-tailed) |
.000 |
.000 |
.000 |
|
|
N |
50 |
50 |
50 |
|
|
interactive platform |
Pearson Correlation |
.885** |
.840** |
.918** |
|
Sig. (2-tailed) |
.000 |
.000 |
.000 |
|
|
N |
50 |
50 |
50 |
|
|
revenue and profit margin |
Pearson Correlation |
.837** |
.814** |
.836** |
|
Sig. (2-tailed) |
.000 |
.000 |
.000 |
|
|
N |
50 |
50 |
50 |
Table 41
|
Correlations |
||||
|
reliable information |
interactive platform |
revenue and profit margin |
||
|
quality advice |
Pearson Correlation |
.869** |
.885** |
.837** |
|
Sig. (2-tailed) |
.000 |
.000 |
.000 |
|
|
N |
50 |
50 |
50 |
|
|
bulk information |
Pearson Correlation |
.914** |
.840** |
.814** |
|
Sig. (2-tailed) |
.000 |
.000 |
.000 |
|
|
N |
50 |
50 |
50 |
|
|
price comparison |
Pearson Correlation |
.841** |
.918** |
.836** |
|
Sig. (2-tailed) |
.000 |
.000 |
.000 |
|
|
N |
50 |
50 |
50 |
|
|
reliable information |
Pearson Correlation |
1 |
.835** |
.819** |
|
Sig. (2-tailed) |
.000 |
.000 |
||
|
N |
50 |
50 |
50 |
|
|
interactive platform |
Pearson Correlation |
.835** |
1 |
.858** |
|
Sig. (2-tailed) |
.000 |
.000 |
||
|
N |
50 |
50 |
50 |
|
|
revenue and profit margin |
Pearson Correlation |
.819** |
.858** |
1 |
|
Sig. (2-tailed) |
.000 |
.000 |
||
|
N |
50 |
50 |
50 |
Table 42
|
Correlations |
||||
|
revenue and profit margin |
variety of information |
user reviews |
||
|
revenue and profit margin |
Pearson Correlation |
1 |
.876** |
.842** |
|
Sig. (2-tailed) |
.000 |
.000 |
||
|
N |
50 |
50 |
50 |
|
|
variety of information |
Pearson Correlation |
.876** |
1 |
.851** |
|
Sig. (2-tailed) |
.000 |
.000 |
||
|
N |
50 |
50 |
50 |
|
|
user reviews |
Pearson Correlation |
.842** |
.851** |
1 |
|
Sig. (2-tailed) |
.000 |
.000 |
||
|
N |
50 |
50 |
50 |
|
|
easy decisions |
Pearson Correlation |
.830** |
.919** |
.859** |
|
Sig. (2-tailed) |
.000 |
.000 |
.000 |
|
|
N |
50 |
50 |
50 |
|
|
waiting time |
Pearson Correlation |
.809** |
.890** |
.899** |
|
Sig. (2-tailed) |
.000 |
.000 |
.000 |
|
|
N |
50 |
50 |
50 |
|
|
quick decision making |
Pearson Correlation |
.859** |
.887** |
.870** |
|
Sig. (2-tailed) |
.000 |
.000 |
.000 |
|
|
N |
50 |
50 |
50 |
|
|
review and compare |
Pearson Correlation |
.808** |
.886** |
.883** |
|
Sig. (2-tailed) |
.000 |
.000 |
.000 |
|
|
N |
50 |
50 |
50 |
Table 43
|
Correlations |
||||
|
easy decisions |
waiting time |
quick decision making |
||
|
revenue and profit margin |
Pearson Correlation |
.830** |
.809** |
.859** |
|
Sig. (2-tailed) |
.000 |
.000 |
.000 |
|
|
N |
50 |
50 |
50 |
|
|
variety of information |
Pearson Correlation |
.919** |
.890** |
.887** |
|
Sig. (2-tailed) |
.000 |
.000 |
.000 |
|
|
N |
50 |
50 |
50 |
|
|
user reviews |
Pearson Correlation |
.859** |
.899** |
.870** |
|
Sig. (2-tailed) |
.000 |
.000 |
.000 |
|
|
N |
50 |
50 |
50 |
|
|
easy decisions |
Pearson Correlation |
1 |
.852** |
.892** |
|
Sig. (2-tailed) |
.000 |
.000 |
||
|
N |
50 |
50 |
50 |
|
|
waiting time |
Pearson Correlation |
.852** |
1 |
.878** |
|
Sig. (2-tailed) |
.000 |
.000 |
||
|
N |
50 |
50 |
50 |
|
|
quick decision making |
Pearson Correlation |
.892** |
.878** |
1 |
|
Sig. (2-tailed) |
.000 |
.000 |
||
|
N |
50 |
50 |
50 |
|
|
review and compare |
Pearson Correlation |
.861** |
.866** |
.873** |
|
Sig. (2-tailed) |
.000 |
.000 |
.000 |
|
|
N |
50 |
50 |
50 |
Table 44
|
Correlations |
||
|
review and compare |
||
|
revenue and profit margin |
Pearson Correlation |
.808** |
|
Sig. (2-tailed) |
.000 |
|
|
N |
50 |
|
|
variety of information |
Pearson Correlation |
.886** |
|
Sig. (2-tailed) |
.000 |
|
|
N |
50 |
|
|
user reviews |
Pearson Correlation |
.883** |
|
Sig. (2-tailed) |
.000 |
|
|
N |
50 |
|
|
easy decisions |
Pearson Correlation |
.861** |
|
Sig. (2-tailed) |
.000 |
|
|
N |
50 |
|
|
waiting time |
Pearson Correlation |
.866** |
|
Sig. (2-tailed) |
.000 |
|
|
N |
50 |
|
|
quick decision making |
Pearson Correlation |
.873** |
|
Sig. (2-tailed) |
.000 |
|
|
N |
50 |
|
|
review and compare |
Pearson Correlation |
1 |
|
Sig. (2-tailed) |
||
|
N |
50 |
|
|
**. Correlation is significant at the 0.01 level (2-tailed). |
Table 45
Regression analysis is the method of developing a linear relationship between two variables to identify the trend in relationship so that further predictions can be made about the relationship between the two. It determines the nature, direction and type of relationship between both the variables. In this current study, a multiple linear regression has been used to measure the linear relationship between the elements of social media marketing and organisational profitability. In order to do so, the different elements are named as predictors and the organisational profitability has been represented as revenue and profit margin. This research has developed a predictive model to evaluate the relationship between the two variables and predict future trend lines based on it.
Reliability analysis
Multiple R defines the correlation between the predictors and the dependent variable. In this research, the value of multiple R is .915 which means that the correlation between the predictors and the dependent variable is high. This also signifies that predictive capability of the model is high. R square is the coefficient of determination which examines the explaining capability of the independent variables and goodness of fit of the model. The value is acceptable at 0.7 but values less than that have also been accepted in practical studies where small margin in increase results in high profitability. The value of R square in the research is .836 which is greater than 0.7 and this implies that goodness of fit of the model is high. Moreover, it also defines that the explaining capability of the independent variables is 83.6%. The Durbin Watson test has been performed to check the first order autocorrelation among the elements, the value of Durbin Watson is 2.283 which lies within 1.7 to 2.5 and this means that there is no first order autocorrelation among the elements.
The analysis of the Anova table shows that F value in the research is .000. This value is less than the p value which is 0.05. This implies that the null hypothesis can be rejected snd the alternative hypothesis can be accepted. This means that there is significant positive relationship between social media marketing and organisational profitability.
|
Model Summaryb |
|||||
|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
Durbin-Watson |
|
1 |
.915a |
.836 |
.789 |
.616 |
2.283 |
|
a. Predictors: (Constant), review and compare, bulk information, price comparison, easy decisions , waiting time, interactive platform , quality advice , reliable information, user reviews , quick decision making, variety of information |
||||||
|
b. Dependent Variable: revenue and profit margin |
||||||
|
ANOVAa |
||||||
|
Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
|
1 |
Regression |
73.671 |
11 |
6.697 |
17.663 |
.000b |
|
Residual |
14.409 |
38 |
.379 |
|||
|
Total |
88.080 |
49 |
||||
|
a. Dependent Variable: revenue and profit margin |
||||||
|
b. Predictors: (Constant), review and compare, bulk information, price comparison, easy decisions , waiting time, interactive platform , quality advice , reliable information, user reviews , quick decision making, variety of information |
|
Coefficientsa |
||||||
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
|
B |
Std. Error |
Beta |
||||
|
1 |
(Constant) |
.104 |
.207 |
.505 |
.617 |
|
|
quality advice |
.032 |
.184 |
.034 |
.174 |
.863 |
|
|
bulk information |
-.119 |
.230 |
-.110 |
-.518 |
.608 |
|
|
price comparison |
.015 |
.201 |
.015 |
.073 |
.942 |
|
|
reliable information |
.053 |
.222 |
.049 |
.238 |
.813 |
|
|
interactive platform |
.283 |
.195 |
.287 |
1.451 |
.155 |
|
|
variety of information |
.502 |
.215 |
.522 |
2.333 |
.025 |
|
|
user reviews |
.349 |
.194 |
.356 |
1.794 |
.081 |
|
|
easy decisions |
-.082 |
.204 |
-.080 |
-.400 |
.692 |
|
|
waiting time |
-.271 |
.200 |
-.264 |
-1.354 |
.184 |
|
|
quick decision making |
.368 |
.207 |
.364 |
1.772 |
.084 |
|
|
review and compare |
-.238 |
.187 |
-.237 |
-1.273 |
.211 |
|
a. Dependent Variable: revenue and profit margin |
|||||
|
Residuals Statisticsa |
|||||
|
Minimum |
Maximum |
Mean |
Std. Deviation |
N |
|
|
Predicted Value |
.69 |
4.77 |
2.28 |
1.226 |
50 |
|
Residual |
-1.776 |
.707 |
.000 |
.542 |
50 |
|
Std. Predicted Value |
-1.297 |
2.030 |
.000 |
1.000 |
50 |
|
Std. Residual |
-2.885 |
1.149 |
.000 |
.881 |
50 |
|
a. Dependent Variable: revenue and profit margin |
|
Question |
Statements |
Codes |
|
1. How does social impact the profitability of the organisations in the automobile industry? |
“Social definitely positively impacts the profit margin and revenue.” “Social media marketing reduces the overall cost of marketing which increases the overall profit margin of organisations in the automobile industry.” “Social media marketing is one of the significant tool for increasing market share and growth by reducing the overall cost of operation. The use of innovation and technology effective can be used to improve the organisational marketing processes and policies.” “Social media marketing facilitates in using different techniques seamlessly without much increase in the budget. Social media marketing facilitate in effective management of capital and it is essential for organisational profitability.” “Well, social media marketing provides better quality services to the consumers and helps in retaining existing consumers. It also facilitates in acquiring new consumers. This has strong positive impact on organisational profitability. |
Positive impact, enhances consumer service quality, reduces cost of marketing |
The research has used mixed method analysis to collect data from surveys and interviews where 50 customers and 5 managers working in the automobile industry in Australia were surveyed and interviewed respectively near the Brisbane area. The quantitative data has been analysed by using SPSS software (statistical tool for social sciences) and qualitative data has been analysed using coding. The survey data has been represented in tables and charts along with descriptive statistics. This data has been analysed based on the response frequency and the pattern in the data. On the other hand qualitative data has been analysed using open coding and axial coding. Open coding has been used to analyse the content and identify the patterns or key words that are common themes to the research. These common themes have been considered and compared with the quantitative analysis. The qualitative analysis aimed to highlight dimensions not highlighted using quantitative analysis.
The results from the research shows that there is positive relationship between social media marketing and organisational profitability. The quantitative analysis of the data clearly showed that the relationship is strong and positive and is significant two tailed. This means that there is bidirectional relationship between the variables. On the contrary, one of the managers stated, “Social media marketing facilitates in using different techniques seamlessly without much increase in the budget. Social media marketing facilitate in effective management of capital and it is essential for organisational profitability” which has also identified the factors essential for the improvement of the organisations profit margin and revenue. This means that managers also consider social media marketing as a method of enhancing the profitability of the organisations.
Conclusion
The results showed that social media marketing has more advantages than disadvantages. As per the findings, social media marketing is cheap, caters to a larger audience, and facilitates word of mouth and two communication. Moreover, it assists in higher market penetration and analysis of the implemented strategies. On the contrary, it can be said that negative feedback, criticism and scrutiny from the customers cause major adverse impact on the brand image and the campaign. The companies have changed their benchmark practices and engaged in digital marketing activities which has resulted in change in focus. The use of digital makes is easier and convenient for the companies to convey their message to different consumer segments at the same time. Social media marketing has changed the industry as majority of the users search for information on online platform and compare car feature, prices and other aspects. The quantitative analysis of the data clearly showed that the relationship is strong and positive and is significant two tailed. This means that there is bidirectional relationship between the variables