Social media marketing factors that will dictate consumer buying behaviour within theclothing sector. To better comprehend and analyze this notion of this thesis, a survey was proposed based on questionnaires that are to be answered by customers of the clothing industry.
The survey’s geographical distribution above was isolated to , as the questionnaires were designed to be close-ended and conveniently provided and collected by the researcher. These account should be linked to be website if different websites not access can use the VPN Service provider for making things run smooth.
Demographic Distribution
This research paper focuses mainly on the clothing sector, specifically women’s clothing, Hence, the sample consists of clothing products and articles available at a leading brand of clothing industry. This thesis’s demographic is aimed at the women customers who purchase various clothing products from brands such as Sapphire. Only females are the subjects of this research. All of the customers who participated in the survey had different household monthly incomes, occupational & educational background.
Population, Sample and Sampling Technique
The main objective of this research paper, as aforementioned, is to understand better the factors of social media marketing that affect the consumer buying behaviour in the clothing industry of The population was chosen for this thesis study only females, as in there is a trend of launching new prints every season and festival for females. Clothing articles, such as lawn suits, are worn by every woman. As such, this thesis study’s research population is significant because, according to Murphy (2016), a research population exceeding.
Learning SPSS Testing Process
One hundred thousand can be considered a large population (Murphy, 2016). The approximate sample size selected from the population were 218 female respondents after setting the C.I. of 95%, which leaves 5% for M.o. E.The responses of 218 female customers were chosen as this thesis’s sample frame. To garner and gather the responses for further analysis, online forms on Google were submitted by the respondents.
Because of constricted resources, the sampling method chosen for this thesis was convenience sampling. Respondents were approached on online forums and various social media platforms such as Facebook and Instagram. The online Google form questionnaire had been posted for the population to respond to. To ensure the reliability and effectiveness of the data, 218 respondents were chosen.
Research Design
The design of this research study entails a systematic strategy to ensure the effective conduct of this research. The research design consists of various research components, including the hypotheses, dimensions, data analysis and collection techniques, and conceptual and theoretical frameworks.
This research study is quantitative research as the concepts of epistemology and positivism. Based on scientific evidence, these concepts will facilitate the testing of hypotheses. Moreover, positivism will reduce in testing the theory by analyzing the dependent variable of the independent variables. Furthermore, epistemology describes this research study’s philosophical face as it entails collecting data on a topic in a procedural sequence. A deductive approach has been adopted to this thesis during the research paper.
Key Concept of Survey
It explains the concept in detail and thus can be considered as explanatory as well. As mentioned before, the survey’s primary data source is the close-ended questionnaires, while the secondary information was gathered scouring over the internet. In addition to the above, convenience sampling dictated the thesis study to account for such a population that would engulf all the industry consumers.
Theoretical Framework
The information adoption model and the Elaboration likelihood model are the two theoretical frameworks underpinning this research study. Susan and Siegal first theorized the Information Adoption Model (IAM) in 2003. IAM is used to facilitate how people or consumers adopt information and change their behaviours within the social media communication platforms (Wang, 2016). The concept that envisions people being impacted by a message in either two ways, being central and peripheral, is the Elaboration likelihood model.
Illustration
The above-illustrated coefficient table represents the dependent variable’s impact because of each independent variable’s presence. The beta value represents the regression coefficient on the table, which indicates the impact intensity on the dependent variable of each independent variable.
The results above give the researcher an insight into online engagement on Consumer buying behaviour. The regression coefficient is 0.224, which indicates that with a single unit increase in online concentration, there will be an increase of 0.224 units of consumer buying behaviour. The t-value of them should be greater than two, and the p-value is 0.000, which is
Less than 0.05, indicating that online engagement has a significant impact on consumer buying behaviour.Moving on, electronic word of mouth is the second variable that needs to be tested for its impact on consumer buying behaviour. The value of 0.94 represents the regression coefficient. It suggests that when a single unit of electronic word of mouth is increased, there will be an increase of 0.94 units in the consumer buying behaviour in the clothing industry. The t-value is 1.847, which is not greater than 2, and the p-value is 0.066, which is not less than 0.05. This result is an indication that electronic word of mouth does not have a significant impact on consumer buying behaviour within the clothing industry.
Conclusion
Entertaining and relevant content is also put under the scrutiny of this test. The regression coefficient is 0.161, which means that for every single unit increase in entertaining and relevant content, there will be an increase of 0.161 units in consumer buying behaviour. The t value is
2.148 while the p-value is 0.033, both of which is an indicator that entertaining and relevant content has a significant impact on consumer buying behaviour within the clothing industry
Lastly, trust in a brand is the fourth variable tested for being an influential factor in consumer buying behaviour. The regression coefficient of trust on brand is 0.201, which means that an increase of 1 unit in the brand’s trust will bring about an increase of 0.201 units of consumer buying behaviour in the clothing industry. The associated t value is 2.755, which is greater than 2, while the p-value is 0.006, which is less than 0.05. These outcomes ensure that trust in a brand has a significant impact on consumer buying behaviour within the clothing industry.
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