Analysis of the Impact of Social Media Marketing (SSM) on Consumer Behaviour
Table of Contents
1. Chapter 1 – Introduction and Background............................................................................3
1.1. Introduction....................................................................................................................3
1.2. Background................................................................................................................... 3
1.3. Rationale.......................................................................................................................4
1.4. Research Aim................................................................................................................ 4
1.5. Research Objectives.....................................................................................................4
1.6. Research Questions......................................................................................................4
2. Chapter 2: Literature Review................................................................................................5
2.1. Theme 1: The Influence of Social Media Marketing Activities on Consumer Engagement
in the UK..................................................................................................................................5
2.2. Theme 2: How SMM activities develop Social Identities among Consumers.....................5
2.3. Theme 3: How SMM Influences Levels of Consumer Satisfaction.....................................6
2.4. The Influence of SMM on Consumer Intentions.................................................................7
3. Chapter 3: Research Design and Methodology....................................................................7
3.1. Paradigm........................................................................................................................... 8
3.2. Sampling........................................................................................................................... 9
3.3. Tools and Data Requirements...........................................................................................9
3.4. Data Collection Process..................................................................................................11
3.5. Ethics in Business Research...........................................................................................11
4. Chapter 4: Data Analysis Technique..................................................................................14
References................................................................................................................................ 16
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1. Chapter 1 – Introduction and Background
1.1. Introduction
In the recent decade, the usage of social media platforms such as YouTube,
Twitter, Instagram, TikTok, Whatsapp, LinkedIn, Snapchat, and Facebook has recently
increased. Individuals are increasingly using social media platforms to communicate
with each other, and brands are increasingly using them to advertise their products.
According to research conducted by (Kemp, 2023), out of the overall population in the
UK of 67 million (Park, 2022), approximately 57.1 million individuals were using social
media in January 2023. YouTube alone reached 86.4 per cent of the social media user
base, with their ad potential increasing by the end of 2023 (Kemp, 2023).
1.2. Background
A virtual world, courtesy of social media platforms, has been created, and
messages now enable people to share information easily and interact with individuals
worldwide. Understanding the massive shift in information and communication
consumption, different brands have been applying advertising skills such as social
media marketing (SMM) to succeed in online marketplaces such as Wayfair, eBay (UK),
Airbnb, Amazon (UK), Shopee, and Folksy (Pool, 2023). Brands and individuals are
using these marketplaces to initiate trades, communicate with each other, and use the
brands to market products. Social activities have been taken to a virtual world due to
social media marketing. Using these platforms to advertise products and events is
called social media marketing. With the increased rates of community websites and
social media usage, numerous organisations continue finding ways to use online
marketplaces to create communications and enhance relationships with users to create
close and friendly relationships.
Figure 1: Social Media Apps Usage in the UK
Source: Sagar (2022).
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1.3. Rationale
SMM is a critical business strategy that brands can use to create brand
awareness. Since companies spend millions on social media campaigns, understanding
the relationship between consumer behaviour and marketing is critical. Fortunately, a lot
of research on SMM has been conducted on creative strategies that influence
consumers' behaviour. Nevertheless, very few studies have analysed the evolution and
actual content of retail brands' social media strategies concerning metrics of consumer
actions such as online engagement and sales conversions. More research is required to
provide marketers with evidence-based best practices to utilise social media platforms.
1.4. Research Aim
The main aim of the research is to analyse the impact of social media activities on
consumer behaviour in the UK.
1.5. Research Objectives
To assess the influence of social media activities on consumer engagement in
the UK.
To examine how SMM develops social identification among consumers in the
UK.
To investigate consumer satisfaction levels resulting from engagement with SMM
in the UK.
To analyse the influence of SMM on different customer intentions, including
purchasing, recommending, and participating in promotional activities in the UK.
1.6. Research Questions
How do various SMM activities lead to consumer engagement in the UK?
What role does SMM play in forming social identification among UK online
customers?
How does customer satisfaction differ according to interactions with various SMM
aspects in the UK?
To what extent does SMM influence different customer intentions in the UK?
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2. Chapter 2: Literature Review
2.1. Theme 1: The Influence of Social Media Marketing Activities on Consumer
Engagement in the UK
Social media comprises internet-related applications built on ideological and
technological Web 2.0 principles that enable creating and sharing user-generated
content (Kozinets, 2019). Social media's interactive traits allow for knowledge sharing
and participatory and collaborative activities available to larger communities than in
traditional media formats such as print, TV, and radio; social media is a critical
communication channel that spreads brand information (Jamil et al., 2022).
Social media comprises social networking websites (such as Facebook,
Instagram, LinkedIn, YouTube, Twitter and TikTok) used by business firms,
governmental organisations, and social networkers to communicate with consumers
through marketing and advertising (Quesenberry, 2020). Social Media Marketing (SMM)
involves carrying out marketing activities using less effort and cost due to smooth
communication and interactions between retailers, digital services, media, events, and
consumer partners using social media platforms (Jamil, 2022; Dwivedi et al., 2021).
Most online UK businesses utilise SMM (Social Media Marketing) activities that
strongly influence brand performance across metrics such as sales, conversions, and
website traffic (Dwivedi et al., 2021; Jamil, 2022). According to Tamble (2019), creative
visual advertising and marketing social media content stand out as highly engaging
marketing information that multiplies the interaction rate compared to text-only posts.
This likely relates to how visuals facilitate information processing and memory better
than words (Appel et al., 2020).
Additionally, luxury and other major brands use entertainment, interaction,
trendiness, reputation, and customisation that significantly impact brand equity and
consumers' buying intentions (Jamil, 2022; Fetais et al., 2023). Community marketing
activities accumulate from interactions between individuals' mental states and events,
while products are users' external factors (Parsons & Lepkoswska-White, 2018).
2.2. Theme 2: How SMM activities develop Social Identities among Consumers
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SMM activities shape consumer identities linked to peer groups and brand
associations. Brands that cultivate robust and remarkable social media personas build
better engagement from customers who individually identify with projected values
(Cuffie, 2022). According to Li et al. (2022), shaping social identities relates to the self-
congruity theory that people are attracted to brands that match their self-images.
Different studies on online brand communities build social identities for
consumers, appreciating and recognising individuals as parts and parcels of the online
brand communities. According to Hogg (2018), these social identities demystify how
individuals improve self-esteem and self-affirmation using categorisation, identity and
comparison. According to Kumar (2019), no clear definition of the brand owner or
community improves communication and builds rapport between online brand
communities and their members. Thus, online brand community members are grouped
according to living environments, occupations, and educational achievements
(Veloutsou & Black, 2020).
Identifying with brands lets users interact freely, thus creating similar ideologies
about the community (Cuomo et al., 2020; Jamil, 2022). It also strengthens relationships
between members, enabling them to identify with the community (Coelho et al., 2018).
Identifying with online communities can also be seen as the values of user minds and
social communities coming together (Coelho et al., 2018). Based on the consumption
value theory, members of online social brand communities share ideas by participating
in community activities to create solutions (Kaur et al., 2018). When consumers join
brand communities, they participate in discussions or activities and readily help each
other (Cuomo et al., 2020). In turn, this impacts community identity positively and leads
to sharing professional knowledge or understanding with other members to create a
sense of belonging and enhance personal growth (Jamil, 2022).
2.3. Theme 3: How SMM Influences Levels of Consumer Satisfaction
Consumer satisfaction involves comparing expectations and consumer
satisfaction using the standards arising from accrued prior experiences (Ahrholdt et al.,
2019). It is mostly shaped by the degrees to which social media marketing enables two-
way interactive communication versus passive, one-way brand messaging (Sihvola,
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2022). Based on the expectation confirmation theory, consumer satisfaction is the
consumer's expected satisfaction with how the services live up to consumer
expectations (Rahi & Abd, 2019). Consumers can define satisfaction levels by
contrasting current and prior satisfaction levels (Otto et al., 2020).
Response efficiency in social media interactions greatly impacts satisfaction.
Customers expect service parity with other channels in problem resolution and wait
times (Broekhuizen et al., 2018). Those who can directly message brands and receive
helpful and instant responses report higher satisfaction rates than those who cannot
(Schwager & Meyer, 2007). Chatbots also assisted with scaling responses (Tsai et al.,
2021). Nevertheless, conflicting perspectives exist on chatbot restrictions regarding
query appropriateness, empathy, and complexity (Luo et al., 2022; Pompe, 2023).
Research is required to analyse optimal chatbot/human balances.
2.4. The Influence of SMM on Consumer Intentions
SMM can also shape consumer intentions, such as participating in promotions
such as contests and referral programs, product recommendations, and purchase
conversion (Saleem, 2019; Jamil et al., 2022). Different studies on marketing and
information systems have used continuance intention to measure if customers continue
using certain services or products. Applying social network theory, companies need to
appeal to consumers' purchase intent if they want to retain communities and gain more
members (Akar & Dalgic, 2018). Consumers’ purchase intent is connected to
engagement levels and SMM content quality (Bilal et al., 2021). Viral content
significantly drives purchases, interest, and awareness (Tellis et al., 2019).
According to Naeem and Ozuem (2021), the user-generated content (UGC)
social brand engagement model also plays an influential role based on the trust and
validation of fellow consumers. Social media platforms leverage this with review
systems and promote user-generated content, further empowering referrals and
advocacy, symbolising a critical opportunity (Moriuchi, 2019).
3. Chapter 3: Research Design and Methodology
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The research will use secondary quantitative measures best suited to analysing
the fine differences between consumers and providing precise estimates of the
relationship between social media marketing (SMM) and consumer behaviour.
3.1. Paradigm
The research will apply a positivist paradigm to assess the impact of social media
marketing (SMM) on consumer behaviour in the UK. The Week 6 Lecture (2023) states
that a positivist paradigm explains reality through data. The positivism paradigm is
suitable for this quantitative study since it assesses the researchers' perspectives,
allowing them to test hypotheses and theories to establish causal relationships between
consumer responses and SMM activities (Park et al., 2020). In other words, the
positivist approach aligns with deductive logic to test hypotheses and theories to
uncover causal fundamental laws and explanations that govern the world.
The research will apply the different defining features that characterise the
positivist approach. For instance, it will be used to emphasise precise, quantifiable
observations lending themselves to statistical analysis (Week 6 Lecture, 2023).
Positivism will also be used to emphasise objectivity, measurability, predictability,
controllability, and construct validity that aligns with the goal of quantifying impacts and
statistically analysing relationships (Mbanaso et al., 2023). This paradigm will
emphasise precise, quantifiable observations lending themselves to statistical analysis.
Additionally, positivism will allow for theory testing versus theory building using
the available literature on consumer behaviour and SMM (Park et al., 2020). The
expectation confirmation, consumption value, and social identification theories present
testable propositions on the psychological and social drivers of consumer satisfaction,
buying conversion, participation, and sense of belonging (Kaur et al., 2018; Rahi & Abd,
2019). For instance, the expectation confirmation theory holds that consumers judge
satisfaction levels based on pre-purchase expectations fulfilled after purchase (Fu et al.,
2020). This presents clear quantitative relationships between confirmation and
expectations as predictors of satisfaction ratings pliable to positivist measurement. The
selected academic studies will use scales to operationalise and statistically analyse
consumer engagement levels, purchase intent, and brand identification.
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3.2. Sampling
The research will use non-probability sampling rather than probability sampling
methods. Although probability sampling can allow for statistical inference to a broader
population, the goal of the research is analytical depth on the specific relationships
under study (Niewiadomska-Bugaj & Bartoszynski, 2020). So, rather than
randomisation, subjective factors related to informational value based on the literature
foundations will determine the inclusion of particular UK social surveys into the final
secondary dataset.
Specifically, purposive/judgemental sampling will be used to select the most
recent secondary datasets related to social media usage and related online consumer
behaviours of UK audiences, including purchase rates, identification, satisfaction, and
engagement will be utilised. Focusing the research on UK consumer panels will allow
for greater contextual relevance to the findings (Vijayakumar, 2023). The systematic
searches will identify multi-year UK datasets from providers such as annual
reports, .gov, UK Office for National Statistics (ONS), YouGov UK, and Statista, which
conducts systematic data aggregation from verified sources.
These data sources were selected since they use probability sampling from
demographically representative UK panels rather than convenience panels and
standardised measures that leverage validated psychographic scales over multiple
survey waves (Week 5 Lecture, 2023). Additionally, they apply transparent
documentation of origination details for synthesised datasets, large sample sizes that
ensure statistical significance of consumer segments and trends, rigorous quality control
checks and data cleansing procedures by analytics teams, and accuracy evaluations
benchmark survey findings against real-world behaviours (Lakens, 2022; Week 5
Lecture, 2023).
The sample data will cover five years, from 2018 – 2023, during the rapid growth
of online brands. Study relevance will take priority over random sampling or
representativeness in the compiled datasets. Content validity analysis will screen
datasets to retain only applicable measures of SMM exposure, engagement activities,
and self-reported online/offline purchase behaviours. Statistical analysis will determine
correlations, predictive capabilities, and comparisons of variance across the variables.
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3.3. Tools and Data Requirements
The main data requirement for this quantitative secondary analysis is the
compilation of a cross-sectional dataset from existing UK consumer surveys that track
social media usage and associated behaviours, including engagement, social
identification, consumer satisfaction, and purchase rates over five years. Multiple
reputable data sources will be sourced using purposive non-probability sampling based
on inclusion criteria that emphasise coverage across four dimensions: 1) Audience
demographic segments divided by geographic location, income, age, and gender to
enable subgroup analysis; 2) Behavioural metrics that quantify multi-channel shopping
conversion paths, digital participation levels and brand perceptions; 3) Product and
service sectors such as travel, financial services, and retail businesses that actively
advertise online; 4) Major social media platforms that include YouTube, Twitter,
Instagram, and Facebook that frequently carry marketing content.
Candidate data sources being evaluated include government social media
adoption statistics from the UK Office for National Statistics (ONS), industry research
reports published by services such as Mintel, survey panel microdata from
organisations such as YouGov, and proprietary consumer behavioural data tracked by
analytical providers such as Statista and SimilarWeb across leading UK brands.
The final collated dataset constructed within SPSS will consolidate the common
measures most applicable from these sources related to the core research issues in a
consolidated form that can be subjected to ANOVA group comparisons, statistical
analysis using correlation tests, and explanatory regressions to efficiently probe
relations assumed in the hypotheses without undertaking costly primary data collection.
By incorporating ANOVA tests, SPSS will efficiently determine if consumer group
variances based on usage frequency, age, gender, and branded engagement are
statistically significant.
ANOVA (Analysis of Variance) will allow for comparison across various
consumer groups segmented by demographics, usage levels, etc. It will test whether
there are significant differences in the dependent variables, such as satisfaction,
engagement, and purchase rates, based on independent variables. For instance,
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ANOVA will examine if heavy SMM users exhibit greater online community advocacy
and participation compared to light SMM.
3.4. Data Collection Process
Data collection will start by systematically identifying UK survey datasets
between 2018 – 2023, meeting the inclusion criteria covering key platforms where SMM
occurs, industries focused on e-commerce and digital advertising, robust metrics that
capture online/offline consumer behaviours, and representation across different
consumer segments. Rigorous selection on origination and data quality will screen
datasets before pending inclusion into the compiled analytics file.
Once collated within SPSS, multiple preparation techniques will optimise the
varied data for integrated analysis including managing missing values through mean
imputation when reasonable based on overall construct completion rates above 70%
per factor being analysed, confirming or transforming different Likert scale coding
mechanisms into harmonised anchors (1-5), interpreting outlier values based on
collection notes and annotating metadata like category definitions, applying nested
stratification of demographic factors, and tagging related measures to research
constructs through custom string variables.
Derived variables may also prove useful to translate scores from differing scales
into common transformed metrics when the patterns exhibit directional alignment. For
example, two measures that capture social media usage frequency through slightly
different response structures could be bridged into common quartiles (non-user, light
user, moderate user, and heavy user), reflecting comparable intensity segmentation.
Boolean recoding will also dichotomise select indicators as needed into binary
splits such as Purchase Influencer (1=Yes 0=No). Once prepared, the panel-ready
dataset will provide the foundation for descriptive analysis summarising group means on
consumer metrics for ANOVA significance testing on variances according to platform
usage intensity and regression modelling to quantify the explanatory predictive strength
of key SMM exposure factors tied to the hypothesised changes in satisfaction, identity
formation, engagement, and purchase behaviours while controlling for demographic
factors. This strategic secondary dataset compilation centred on the research questions
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will balance collection practicalities with analysis by selecting relevant metrics coupled
with precise data preparation and quality assurance checks.
3.5. Ethics in Business Research
Ethical considerations play a critical role in safeguarding participants' rights and
well-being and ensuring the study's integrity (Week 7 Lecture, 2023). Throughout the
process, significance will be placed on safeguarding the individuals' privacy, rights, and
integrity in the original datasets.
Ethical approval will also be obtained for the research following university
guidelines. The ethical clearance process will be obtained to safeguard participants'
well-being and rights and ensure the research's responsible and ethical conduct (Week
7 Lecture, 2023). A comprehensive request for ethical approval online through the
university's designated system will be submitted to initiate the ethical approval process.
An official online ethics form will also be completed. The ethics form will cover different
sections, which will prompt the provision of detailed information about the research and
re-emphasise ethical considerations comprehensively.
Since the research will not involve direct interactions with participants, a key
modification in the ethical approach is the declaration of non-participant involvement.
This declaration will show that the study relies on existing data from reliable sources
and does not directly engage with individuals.
The ethicality and legality of accessing secondary data sources will also be
confirmed, and evidence of permissions obtained for specific datasets will be provided.
The supervisory confirmation will be sought to affirm the supervisor's approval and
awareness of the ethical considerations in the research. Additionally, documentation of
the secondary data sources used in the research will be included, providing
transparency regarding credibility and reliability.
Precise steps will be taken to anonymise personal identifiers, ensuring that the
privacy of individuals contributing to the original datasets is protected. The security of
hard-copy and electronic data will be of concern, prompting the implementation of
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strong measures such as password protection and digital data protection, and secure
physical storage of hard-copy records.
Honesty and transparency will also be foundational tenets of the research. Clear
reporting of the research process, methodologies, and any identified limitations in the
original studies is critical. Acknowledging and disclosing any biases or conflicts of
interest in the source materials is critical to maintaining the integrity of the secondary
analysis. Additionally, committing to respecting the authors' intellectual property is non-
negotiable and will be maintained through citations and references. Credit will also be
given to entities or organisations that provide the source datasets, underscoring the
contribution to the study in an ethical principle fostering appreciation and transparency
within the academic community.
Compliance with all relevant laws and regulations governing using existing data
is a guiding principle. Accurate reporting of findings is an ethical commitment prohibiting
selective reporting and misrepresentation. This commitment to openness will extend to
the publication process, where any new findings or insights derived from secondary
analysis will be disseminated responsibly, following ethical standards in academic
publishing.
Continuous ethical review will also be woven into the fabric of this research.
Ongoing assessments will be conducted, and changes will be made to ensure that the
research consistently aligns with ethical principles. This iterative process will allow for
identifying and resolving ethical considerations that may arise during the study.
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4. Chapter 4: Data Analysis Technique
The data analysis will rely on SPSS Statistics software to execute the planned
assessment of relationships between SMM factors and associated UK consumer
behaviours. SPSS provides an integrated environment enabling smooth progression
from initial data preparation tasks such as importing, merging and transforming different
external dataset sources into a consolidated analytics-ready data file (Queiroz et al.,
2020). Recoding capabilities within SPSS vastly simplify the process of reworking
existing measures into newly derived variables better suited for analysis based on
composite index techniques that aggregate, collapse, or mathematically adjust indicator
scores to amplify analytical relevance aligned to the research objectives (Week 8-9
Lecture, 2023).
These data-wrangling functionalities ensure analyses accurately reflect
information collected while expanding analytical potential. For example, combining
related behavioural frequency questions across various Likert scales into normative
quartile groups or dichotomising lifetime value metrics using value threshold
benchmarks into descriptive segments. SPSS also contains a comprehensive range of
statistical techniques incorporating univariate descriptive profiles reporting variable
distributions as mean summaries or percentages for initial broad data familiarisation and
visualisation using graphs or frequency tables (Week 8-9 Lecture, 2023).
Univariate analysis will characterise predominant consumer patterns across
associated sentiment metrics, branded engagement activity involvement levels, digital
shopping orientations, and media utilisation habits through summary indexes that profile
the overall sample. Clarifying variable measurement distributions facilitates appropriate
technique selections in later multivariate modelling phases.
The next step will be applying bivariate correlation analysis to examine the
direction and quantitative strength of linear relationships hypothesised between key
independent factors such as social media usage intensity, branded content interaction
levels and consumer behaviour dimensions around digital participation, conversion
actions and shopping attitudes. Correlation matrices will probe proposed lead-lag
relationships.
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Multivariate ANOVA procedures will test for variation in group means on the
outcome metrics according to demographic categories such as platform preferences or
age brackets that provide insight into segmented reactions to diverse social media
brand exposures or messaging styles. For example, finding significant differences would
confirm certain consumer cohorts exhibit divergent behaviours contingent on mobile
versus desktop delivery channels.
Finally, explanatory regressions will assess the predictive capacity attributed to
various SMM exposure elements in driving critical consumer outcomes like membership
signup intentions, purchase decisions, and referral likelihood for exogenous traits such
as geography, income, and age. By quantifying the projected changes in the response
probability variables connected to particular social media stimuli, such as personalised
recommendations or peer content through per cent contribution statistics and probability
thresholds, the predictive analyses will substantiate proposed relationships around
specific practices urging heightened branded engagement, loyalty and advocacy.
By systematically moving through each analysis phase from general data
profiling to targeted hypothesis testing focused on quantifying social media impacts on
engagement, commercial behaviours, identity, and satisfaction, SPSS will deliver the
ideal toolkit for unlocking empirical-based marketing insights on UK consumers from
compiled secondary datasets using established statistical procedures. Interpreted
effectively through this multi-technique quantitative approach, robust datasets offer
expansive potential for informing social media strategy.
Page | 15
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