Social Media and Consumer Behaviour – results of last year’s study (qual/quant)
Towards the end of last year I carried out a small piece of research on how social media is utilised and affects consumer behaviour specifically for high-involvement products, in this instance smartphones. I’ve been meaning to post the findings for ages, alas, things got a little busy last term. Without further ado, here’s a summary my study.
Research background and objectives:
the Internet and word of mouth, both offline (friends, family, colleagues) and online (social networks, blogs) can heavily influence the information search and evaluation of alternative stages of the decision-making process. It is estimated that 98% of the population uses Social Media, and at least 30% of consumers looking at brands’ profiles on Facebook, Twitter and LinkedIn (Euromonitor International, September 2012). Information-search represents the primary stage in which marketing can provide information and influence consumers’ decisions (Wilkie & Dickson, 1985). But with consumers adopting different search strategies, identifying the platforms of their information-search behaviour is a challenge for brands. The purpose of this paper is therefore to examine:
- How consumers research a high-involvement purchase, in this instance smartphones
- How is social media used in relation to information-search and purchase decision
- How do consumers determine the reliability of a source/user-generated post
- Who talks to whom about what and the effect this has on purchase behaviour
This empirical research used a two-phase sequential mixed methods approach by triangulating both qualitative (detailed views) and quantitative (statistical numeric trends) methodologies
Summary of results
The purchase journey; three distinct types of consumers identified
Not all consumers are equal, and although they do engage in extensive problem solving to form a new product category concept (Howard, 1994), their decision-making process greatly differs.
Using Malhotra’s data assembling technique (2012, p.392), 3 clusters of consumer behaviour based on information-search activity and personality variables were identified. Secondly, specific survey questions helped us segment and quantify their behaviour further.
Group 1 – Technophobes: Technophobes are a group of consumers who are scared of technology and represent 10% of the consumers surveyed. They are cost-conscious consumers who are satisfied with a feature phone.
The trigger pushing them to search for a new phone tends to be a phone call from their service provider and their information-search process tends to be lengthy as they delay the purchase as much as they can. These consumers do not keep up with the latest technologies and will talk to a number of their friends before making a final purchase.
Group 2 – Tech Fashionistas: Tech Fashionistas represent the largest group of consumers identified (70% of consumers surveyed). They are interested in knowing what’s available on the market and tend to own high-end smartphones, which is used for a multitude of activities on a daily basis, such as making calls, texting, emailing, surfing the Internet, social networking, or listening to music.
Tech Fashionistas tend to own latest and ‘best’ smartphones, and spend considerable amounts of time reading about the latest gadgets, using a number of sources of information. Multiple brands are often considered in the journey, albeit not at once; a bad comment for instance may deter a consumer from purchasing a product and then the process will start all over again. At post-purchase evaluation stage, they will share their thoughts about their phones in social networks, and will actively recommend phones to Technophobes.
Group 3 – Tech Experts: Tech Experts live and breathe technology and represent 20% of consumers surveyed. Because they see themselves as experts, they are often content creators, either reviewing phones on their blog, or creating YouTube review videos, thus influencing Tech Fashionistas.
Tech Experts are active shoppers; even though they feel they are not researching the purchase of a phone they are in fact constantly researching the latest products to create content for their blogs or YouTube. Reviewing smartphones for these consumers is a hobby, and they will only purchase the phone that exceeds their expectations.
Sources of information
Whilst the need recognition occurs offline, the information-search process takes place online. We wanted to identify which online sources are the most commonly used for information search, and the likelihood to trust each of these sources.
Figure 1. Sources looked at when researching smartphones ranked by likelihood to trust each source (on a scale of 1-5 where 1 is not likely to trust, and 5 very likely to trust)
Uncertainty Reduction Theory (URT) suggests that the onset of a relationship is characterised by high levels of various uncertainties, and because uncertainty is difficult to deal with, relationship partners communicate and seek information to reduce ambiguity (Berger, 1987; Weiss et al, 2008).
As highlighted in the figure above, consumer-to-consumer exchanges both offline (friends recommendations) and online (Google Search/blogs) were most commonly used, and trusted sources of information, above manufacturers’ owned channels, such as websites (trust mean score: 3.23) or brochures (trust mean score: 2.88).
The qualitative in-depths interviewed highlighted that whilst brands’ owned properties (websites, brochures) are accessed at information-search stage, they are mostly used for product awareness, whilst blogs in comparison, were used in much more depth and frequency.
‘I think I looked at the website once just to see the tech spec of the phone, then I read a number of blogs for in-depth reviews’ (Male, 25 years-old)
Brands’ social networks such as Facebook, Twitter and Google+ were only used by a small number of respondents for information-search, thus resulting in lower trust scores.
Post-purchase evaluation: Likelihood to recommend online
At post-purchase evaluation stage, the three types of consumer behaviours identified, Technophobes, Tech Fashionistas and Tech Experts, once again, showed very distinct behaviours.
We analysed consumers’ levels of Technographicism (Li & Bernoff, 2008) in other words online participation, to understand their attitudes towards social technologies both for information-search and at post-purchase evaluation stage.
65% of consumers surveyed claimed that they would recommend a smartphone within their social networks. When the performance of the smartphone exceeded expectations (positive disconfirmation of expectations), consumers were likely to share their experiences online. On the other hand of the spectrum, if the performance was below expectations, consumers were again very likely to share their frustration online.
The research has helped us identified three groups of consumers who research very differently a high-involvement purchase but are nevertheless inextricably interlinked. Technophobes are influenced by Tech Fashionistas who are themselves influenced by Tech Experts. Of the three groups, Tech Fashionistas, the largest group of consumers, and the only group going through a typical decision-making process cycle from information search through to post-purchase evaluation.
For Tech Fashionistas and Tech Experts, Social Media plays a significant role at both-information search and post-purchase evaluation. Google Search/Blogs, forums and YouTube videos were the most trusted sources of information. A positive, review could influence the purchase, but a negative comment could detract from the purchase, and the information-search cycle will start all over again until a product exceeds expectations. Consumers will form a hierarchy of attributes they seek in smartphones, and will seek uncertainty reduction through information search for the desired smartphone attributes.
The most trusted sources of information were consumer-to-consumer communications both offline (friend recommendations) and online (blog reviews, YouTube videos, comments), In all cases, apart from the Technophobes who avoid researching themselves, the stimuli responsible for influencing or disrupting the decision-making process was a piece of user-generated content. Reliability of the recommendation was based on the quantity of comments agreeing with the content creator.
As Tech Experts ultimately influence Tech Fashionistas and Technophobes, communication efforts should focus on generating content about the product hierarchy features that matter the most to consumers to ensure discoverability and positive word of mouth through Google search and blogs, the most trusted platforms for information-search.
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