Strengths: The major strength enjoyed by Experimental research design is that it allows researchers to easily design and develop casual conclusions, analysis of various factors that can help to create a change in the outcome of the result and also assists in generating innovations and treatments in the final result of the research. Experimental research design also insists users take full control of all the extraneous variables that exist within the research methodology. Compared to other research designs, the process of Experimental research design as compared to other research design is simple, efficient and straightforward in nature which in-turn allows researchers to easily implement in various research disciplines
Weakness: The major weakness faced by researchers while using Experimental research design is that it often develops artificial situations in the business which is totally in contrast with the original situation of the research (Wang, 2013). Moreover, due to the development of artificial situations occurring in the business, it becomes impossible for the researcher to acquire an appropriate reaction of the test subjects of the research.
It can be easily said that the weaknesses of Experimental research design definitely play a significant role in affecting the result of the research as it fails to identify the actual reaction of the research.
The researcher has used the Likert 6-point scale measure to analyse the data collected. The data collected has been used to judge the attitudes and behavioural intentions of the users based on user-generated content on social media. The purchase intention was measured using the Likert 6-point scale and the intention to recommend the product to the friend was assessed through the use of the single item as mentioned in the study by Bergkvist & Rossiter (2007). On the other hand, the measurement of the social identification, interpersonal or collective identification was conducted on the basis of the previous studies and literature that have explored the social influence and social identification in their research. To test the defined four hypotheses, the researcher had used the MANCOVA test. The results of the test are that H2, H4a, and H4b were supported while H1 could not be supported and H3 could not be tested. However, the researcher had appropriately conducted the analysis of the results using the MANCOVA method rather than the simple MANOVA method (Gunderman & Chan, 2013). This is because through the use of the MANCOVA method the noise or error term initiated by the covariant could be eliminated. Another advantage of using the MANCOVA for data interpretation and analysis is that the characterization of the differences in a group would help in controlling the covariates.
The findings of the research match partially with the data. This is because part of the result depicts a significant impact of the collective influence of the online advertising around user generated content. On the other hand, the interpersonal influence does not have any significant impact on the online advertising around user generated content. Considering the hypothesis testing， the data could not be assessed for the testing of H3 while H1 testing could not be supported. The findings could not be in compliance with the data because of the emergence of bias in the sample. Although the researcher, prior to the commencement of the data analysis had eliminated participants who have been engaged in the advertised product before but about one-third of the participants or sample also had prior knowledge of the product. This problem had created a particular attitude towards the product and has significantly affected the findings of the study.
Conclusions that were drawn could partially legitimate the findings as the researcher had faced difficulty in enhancing the attitudes of the participants during the experiment. Consequently, based on the conclusion drawn, the researcher have successfully applied and supported the analysis from the side of the group influence (Statistics, 2017). However, the sample size had been little or moderate for the data analysis using MANCOVA and so the findings were obscure and partially supported the conclusion drawn. The researchers have concluded that the analysis of the online advertising around user-generated content (UGC) and the intention, attitudes, and behaviours of the participants is not limited to the Facebook community and is prevalent in almost all social media sites. Therefore, in order to strengthen the research and draw an effective conclusion, the researcher could have disseminated primary research in other social media platforms like Twitter, YouTube and Instagram etc.
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