How Consumer Behavior Influence Big Data Discussion
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How Consumer Behavior Influence Big Data Discussion
The Plan
This research is going to talk about how consumers’ decisions have been influenced by big data. Nowadays, people are living in the big data era. Every people reads a large amount of information every day. The information people read has influenced people’s behaviour and decision. For example, in the middle of 2010 US elections, Facebook allowed its users to publish a sticker called “Voted” once they have voted in this activity(Monnappa, 2015).
The Facebook data science department said the sticker encouraged around 60 thousand votes and the social contagion encouraged 280 thousand votes (Monnappa, 2015).
When people saw the stickers and comments shared by others, they might want to do the same thing and vote. Furthermore, the studies show that Amazon uses big data to speculate consumers’ preferences according to consumers’ searching and shopping history. The accurate recommendations promote consumers’ decision-making (Huang et al., 2019, p. 689) These examples show big data can influence people’s decisions.
Research shows that consumers tend to buy more of the goods that have been recommended to them through big data customisation, where historical preferences of consumers are brought to them (Hofacker et al., 2016). From this realisation, organisations have understood the market better and started to adopt big data analytics. Big data analytics has helped organisations utilise their data to identify new opportunities.
The opportunities come in the form of knowing what the customers like beforehand. When customers enter the organization’s site, they are given various options to choose from. Businesses realised that they could save on costs by up to 15% through big data analytics as they can store large amounts of data.
This is realized when a company compares its contractor’s charges against the average of the other vendors, where the company will identify and eliminate invoice processing errors and automated service schedules. They can make faster and better decisions by analyzing data collected and gauging the opportunity available for new products and services (Hofacker et al., 2016). For consumers, it had shortened the time and hastle that was previously there when they were searching for things (Hofacker et al., 2016).
Ertemel (2015) reveals that vast business data has been created since the Internet’s inception in the last 20 years since the volume of data is so large that standard database management systems are unable to analyse it. Furthermore, the digital revolution provides a competitive edge by allowing businesses to analyse customer behaviour in order to gather insights for their marketing efforts.
In other words, the phenomena known as big data and analytics as tools used by marketers to acquire insights into customer behaviour provide a competitive edge to organisations (Ertemel, 2015). The topic of big data and consumer behaviour has been hot for the past decade. Lots of researchers have been involved in this research area for investigation. Most of the research topics are about studying big data analysis to help improve product promotions and enhance consumer attention or big data mining to help consumers find target products (Zhang & Tan, 2020).
The deeper investigations need to explore how big data records personal information and pushes relevant information to influence consumer behaviour or purchasing decisions. As many users straddle several applications in quick succession (Facebook, Twitter, Instagram), all have now reached an era of social media research characterised by a diversity of platforms (Rhee et al., 2021).
Besides, online social platforms are Internet spaces where individuals with common interests assemble to debate and answer commonly asked issues, as well as give knowledge and assistance on a wide range of topics, and social media increasingly dominate young people’s lives (Vromen et al., 2015; Bronstein et al., 2016). Therefore, the purpose of this research is to examine the impact of big data on consumer behaviour or decision-making by investigating reactions or purchasing decisions of consumers to content pushed on online social platforms.
A semi-structured interview will be used to investigate this phenomenon. A semi-structured interview is a set of questions prepared for the interviewee in advance, and the interviewer can improvise follow-up questions based on the candidates’ responses (Hardon et al., 2004). For example, “Do you have any online shopping experience?”
“Does this affect your consumer behaviour when a product you have searched and purchased is pushed on the website?” These are prepared questions. Compared to other research methods, the semi-structured interview makes it mutually beneficial for both interviewer and interviewee (Galletta, 2013).
Interviewees provide unexpected information by combining their own experiences. This provides interviewees with more spaces for personal expression. Interviewers can compare the interviewees’ answers more objectively. Meanwhile, interviewers can observe candidates’ expressions or tone of voice. This is an opportunity for the interviewer to explore and study this topic more deeply. The target participants of this research mainly focus on young people who use social media, since the largest age group in social media is from 25 to 34 years old in New Zealand (Hinton, 2021).
The expected contributions of the present research project are organised as follows. The goal of this study is to be able to contribute to the theoretical debate on whether consumer behaviour and purchasing decisions are changing in the era of new data technologies, thereby helping to find the most effective ways to influence consumer behaviour and purchasing.
The study also contributes to business practice in the data era by providing information and findings from research in the post-pandemic period. The expected result of our research is that big data has an impact on consumers’ purchasing decisions. To this end, this study analyses consumers’ reactions and purchase decisions based on the pushed content, filling the gap in the investigation of consumer purchasing decisions under the new data technology in the post-covid environment.
RUBRIC
Excellent Quality
95-100%
Introduction 45-41 points
The background and significance of the problem and a clear statement of the research purpose is provided. The search history is mentioned.
Literature Support
91-84 points
The background and significance of the problem and a clear statement of the research purpose is provided. The search history is mentioned.
Methodology
58-53 points
Content is well-organized with headings for each slide and bulleted lists to group related material as needed. Use of font, color, graphics, effects, etc. to enhance readability and presentation content is excellent. Length requirements of 10 slides/pages or less is met.
Average Score
50-85%
40-38 points
More depth/detail for the background and significance is needed, or the research detail is not clear. No search history information is provided.
83-76 points
Review of relevant theoretical literature is evident, but there is little integration of studies into concepts related to problem. Review is partially focused and organized. Supporting and opposing research are included. Summary of information presented is included. Conclusion may not contain a biblical integration.
52-49 points
Content is somewhat organized, but no structure is apparent. The use of font, color, graphics, effects, etc. is occasionally detracting to the presentation content. Length requirements may not be met.
Poor Quality
0-45%
37-1 points
The background and/or significance are missing. No search history information is provided.
75-1 points
Review of relevant theoretical literature is evident, but there is no integration of studies into concepts related to problem. Review is partially focused and organized. Supporting and opposing research are not included in the summary of information presented. Conclusion does not contain a biblical integration.
48-1 points
There is no clear or logical organizational structure. No logical sequence is apparent. The use of font, color, graphics, effects etc. is often detracting to the presentation content. Length requirements may not be met
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