Bbs 301 Applying Mixed Methods Research to Business – a Big Data Focus Facebook Data Application
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BBS 301 APPLYING MIXED METHODS RESEARCH TO BUSINESS. A BIG DATA FOCUSFACEBOOK DATA APPLICATION[pic 1] Table of Contents1 INTRODUCTION 12 OBJECTIVE (S) 13 METHODOLOGY 14 RESULTS 34.1 Psychographic segments analysis 44.2 The Rebellious Republics 44.2.1 Product and services recommended The Rebellious Republics 44.3 The Relaxing Rhinos 44.3.1 Products and services recommended for The Relaxing Rhinos 54.4 The Turbullence 54.4.1 Product and services Recommended for Turbullence 64.5 The Polymath 64.5.1 Product and services recommended for The Polymath 65 CONCLUSION 76 LIMITATIONS 77 Appendixes 98 Reference 10INTRODUCTION BBS 301 Applying Mixed Methods Research to Business: A Big Data Approach is all about exploring the fundamental ideologies relating to research design and analytics. In this report, a significant focus is placed on Big Data, through data sourcing, data management and analytics from one of the post popular social media platform (Facebook). Therefore, the ultimate aim of this paper is to investigate people’s opinions on Trade war between US and China and recommend product and services to different psychographics based on results from the data collected of these individuals. The research has been conducted using an unstructured Facebook data application, of about 300 comments (n=299). The paper is organised in a structure way. Section 2 is devoted to the primary objectives of the research. In section 3 focuses on the methodology used to collect the data. Section 4 will go into more details from the results, describing the profile of the four different psychographics segments developed using SPSS, which is used to recommend products and services to each group based on their profile analysis. Section 5 will conclude the paper by summarising the results. Section 6 identifies the limitations in from the research, followed by appendices and references.
OBJECTIVE (S) The prime objective of this report is to provide a comprehensive analysis of an unstructured Facebook data to form different Psychographic segments and recommend product and services to each segment as per their profile analysis results.METHODOLOGYThe data discussed in this paper is drawn from 299 of individuals’ comments from an unstructured Facebook data, based on posts related to Trade war between China and US. The research was undertaken by a team of students from Murdoch University in Western Australia. The data was collected using secondary sources specifically Facebook (FB) page to gather all data information in the form of comments on US and China Trade War. Each comment was randomly selected by the team from three different posts from FB pages, Fox Business and CNN. Information was then transferred to a Microsoft Excel sheet to be analysed based on a nine coding scheme (Classify judgmentally) allocated by the lecturer (Mr Peter Batskos). Then data were analysed using mixed method research of both qualitative and quantitative method. The first step was data analysis after transferring the comments to Microsoft Excel sheet using “Code (Classify judgmentally), which in general term is a Likert-scale attitudinal measure of individual’s comments from the 299 comments ranking from 1 to 9. Task was divided into three different parts and each research team-member has used the same Likert-scale (Coding scheme), which were: 1 = Clarity of ideas , 2 = Level of emotion, 3= Level of objectivity; 4 = Past perspective; 5 = Now perspective; 6 = Future perspective; 7 = Level focus on personalities; 8 = Level of criticism of corporates/business; and 9 = Level of criticism of the government/public service departments (See appendix 1). The first research team member was responsible for comment on the first group of people starting from comment 2 to 102, the second member commented from 103 to 203, and the last member documented from 204 to 301. Additionally, the ranking is considered as subjective due to researcher’s bias during our observations and classification. The second step involved transferring the coded data from Excel into SPSS. From the SPSS cluster outputs, the last two outputs of the “Final Cluster Centres” table analysed and the “Number of Cases in each Cluster” was selected for further analysis and accuracy (refer to TABLE I & II). SPSS technique generated four different clusters, for an appropriate name the will be referred to as psychographic segments, of like-minded people based on similarities in comments. Thirdly, two team-members were assigned with one psychographic segment to analyse, and one member did examine two psychographic segments. Finally, each of the four psychographic segments was again investigated based on consideration of the Likert-scale (Coding scheme). Based on this part each segment was given a specific name and also recommended for products and services to help the group overcome limitations and utilise available skills. These recommendations outlined are purely from the researchers perspective, and ideas were gathered using brainstorming technique.