Do Social Media Platforms Control the Sustainable Purchase Intentions of Younger People?

Abstract

Social media platforms have transformed communication, engagement, and consumer interaction, significantly influencing sustainable consumption behaviour. With the vast array of social networking and media options available, marketing professionals actively leverage these platforms to shape consumer preferences and purchasing decisions. This study investigates the impact of social media platforms on the sustainable consumption decisions of young people. Adopting a quantitative research methodology, the study employed judgemental sampling to select participants. Data were gathered from 450 respondents via an online questionnaire, and the proposed relationships were assessed using structural equation modelling (SEM) with SmartPLS version 4 to provide a thorough statistical evaluation. Research findings indicate that social media marketing has a substantial impact on sustainable purchase intentions, content quality, and behavioural engagement. Furthermore, the quality of content and levels of behavioural engagement significantly affect sustainable purchase intentions. However, the findings suggest that the social media platforms used for information retrieval do not significantly influence intentions to make sustainable purchases. This study contributes to marketing research by developing a model that examines how social media platforms influence young people’s intentions to purchase sustainably, as well as the impact of social media marketing, engagement, and content quality on these intentions.

1. Introduction

In recent times, social media users have become increasingly inclined to engage with companies that exhibit environmental consciousness and adhere to principles of sustainability [1]. Consumers now place importance not only on the quality of products or services but also on the environmental impact and social accountability of the businesses they support [2]. As a result, companies have commenced the integration of sustainability-oriented themes into their social media marketing initiatives to cultivate more profound emotional and ethical connections with customers [3]. Research indicates that content demonstrating environmental sensitivity enhances brand loyalty and influences purchase intentions [4]—social media acts as a powerful medium for effectively conveying value-driven messages. Social media platforms (e.g., Facebook, WhatsApp, Instagram, etc.) offer innovative ways to communicate and engage, significantly influencing sustainable consumption behaviour. Given the multitude of social networking and media options available, it is unsurprising that marketing professionals actively utilise these channels to sway potential consumers [5]. Furthermore, social media serves as an effective platform for users to acquire product information, seek assistance and advice from others, offer support to peers, receive community backing, and share their experiences [6]. Access to social media platforms enables individuals to gather essential information more efficiently and personally than traditional search engine retrieval methods [7]. Social media has evolved into a crucial avenue for distributing product-related information [8]. The utilisation of social media (SM) has morphed into a multifaceted marketing phenomenon. Social media marketing (SMM) leverages social media platforms to promote a business and its offerings [9]. SM, which originated in the early 2000s, has substantially transformed the manner in which consumers, marketers, and organisations communicate with one another [10,11,12] define “social media” as digital platforms designed for interaction with extensive audiences. The primary advantage of this tool lies in its capacity to facilitate user interaction, which proves highly effective in engaging customers and the general public [13,14,15]. Marketers integrate SM into their campaigns and strategies to establish connections with consumers. Consequently, SM can be employed by the marketing subdisciplines of product and customer management, public relations, promotions, and marketing communications [16] and serves as a medium for connecting with customers, gathering information, and nurturing customer relationships [17]. Intense competition exists among marketers, compelling them to pursue innovative strategies to attract new online customers while retaining existing ones [18]. SM promotes economic growth by providing cost-effective marketing strategies and fostering reciprocal engagement. The primary objectives of businesses’ SMM efforts are to enhance customer purchase intentions and expand market share [19]. This is accomplished by integrating traditional internet-based advertising campaigns with email newsletters as a facet of their online marketing endeavours.

Over the past four decades, there has been a substantial transformation in the manner in which individuals communicate, attributable to the influence of social media networks on marketing methodologies and corporate strategies [20]. Nonetheless, the utilisation of social media technology is on the rise, and it is reasonable to anticipate that it will continue to exert a significant impact on business operations in the forthcoming years [21]. For example, there are 5.16 billion active social media users globally, as documented by [22]. The predominant demographic, encompassing 59% of users, falls within the age range of 18 to 34 years. With over 3.15 billion monthly active users, Facebook prevails as the leading platform, while YouTube follows in second place with 2.5 billion active users, followed by Instagram with 2 billion and TikTok with 1 billion users. In light of this, enterprises that adopt emerging technologies can attain considerable competitive advantages through SM. Consequently, entrepreneurs exhibit a strong interest in comprehending the potential of SMM, the latest trend in marketing, to stimulate interest in their businesses [23]. Unquestionably, social media plays a pivotal role in the decision-making processes of consumers. Increasingly, individuals resort to their social media networks for advice prior to making purchases [24]. Social media facilitates engagement with virtual communities, thereby enabling individuals to acquire insights into the perspectives of others and enhance their comprehension of specific products or services [25]. Conversely, SMM provides businesses with a platform to establish direct connections with consumers, fostering relationships through user-centric networking and social interaction [26]. Liu et al. [27] assert that social media is the prevailing platform for the promotion and sale of goods and services.

Previous studies have established a correlation between SMM and purchase intention [28,29,30,31,32,33].

Notably, the role of social media platforms as a controlling variable on sustainable purchase intention (SPI) among younger demographics has garnered insufficient scholarly attention, despite its critical importance. Additionally, the relationship connecting SMM, behavioural engagement (BE), content quality (CQ), and SPI remains insufficiently explored within a cohesive framework. Furthermore, there exists a pressing need for more comprehensive investigations utilising the Theory of Planned Behaviour (TPB) and Uses and Gratification Theory (UGT) to better understand this relationship [34,35]. To remedy this deficiency, the present study seeks to examine the controlling effect of social media platforms on the SPI of younger individuals. Additionally, by leveraging the TPB and UGT, this research aims to provide a more robust understanding of the complexities inherent in the research model, specifically investigating the interrelations between SMM, BE, CQ, and SPI among younger populations. To the best of the authors’ knowledge, this study represents the first empirical investigation aimed at addressing the identified research gap.

Summary of Previous Related Literature

The findings from studies [28,30] suggest that digital marketing has a positive effect on purchase intention, with brand equity serving as a moderating factor. Furthermore, a study [36] reveals that social media advertising—encompassing elements such as credibility, informativeness, entertainment, and access to information—significantly influences behavioural engagement and purchase intention. These relationships were investigated using Structural Equation Modelling (SEM). A study [37] employs the S-O-R model to analyse how brand quality and user-user interaction affect Millennials’ online motivation, underscoring the importance of engaging content for brand awareness. Study [38] indicates that the credibility of sources in social media content significantly impacts the relevance of content shared by non-participants, while the quality of information is crucial in determining the significance of content shared by participants. A Study by [39] discovered that brand awareness, association, perceived quality, and loyalty significantly influence consumers’ purchase intentions for skincare products, highlighting the importance of strategic branding and quality assurance within the industry. Additionally, studies [40,41] found that brand equity significantly affects the relationship between social media marketing, marketing communication, and purchase intention, enhancing consumer trust and engagement and thereby improving the effectiveness of marketing efforts.

The study conducted by [42] utilises the Uses and Gratifications (U&G) framework along with the Technology, Personalisation, and Advertising Model (TPAM) to analyse consumer attitudes. It indicates that factors such as word of mouth, interaction, entertainment, and customisation significantly influence these attitudes, whereas trendiness is found to have an insignificant effect. A study [29] reveals that consumers’ attitudes, subjective norms, perceived behavioural control, green thinking, and social media marketing significantly impact their intentions to purchase green products online. The study referenced as [43] employs service-dominant logic and the information adoption model to examine the influence of social media influencers (SMIs) on consumer behaviour. The findings indicate that SMIs affect perceived source credibility, homophily, content quality, and purchase intention. The study referred to as [44] utilises the Stimulus-Organism-Response model to investigate how content quality and brand interactivity influence purchase intention, concluding that brand awareness significantly enhances these factors, thereby amplifying the effects of engaging content and interactive experiences.

2. Theoretical Framework and Hypothesis Development

2.1. Theory of Planned Behaviour (TPB)

Ajzen (1985) [45] developed the TPB as an extension of Fishbein and Azjen’s (1977) [46]. Theory of Reasoned Action (TRA). The TPB posits that an individual’s intention to engage in a particular behaviour is influenced by three primary factors: their attitude towards the behaviour, subjective norms, and perceived behavioural control [36]. According to [22], people worldwide typically dedicate an average of two hours and twenty-three minutes to SM, highlighting its significance in contemporary marketing communication. In relation to SM, the TPB states that attitude towards behaviour refers to an individual’s perception of how interacting with SMM content affects their SPI [37]. Ref. [38] stated that individuals who have positive attitudes towards a product, such as finding the content informative or entertaining, are more likely to have a greater intention to purchase. Subjective norms refer to the perceived social pressures or expectations that influence individuals to interact with social media marketing content and make a purchase. When people believe that their peers or social networks support the content or product, they are more likely to intend to buy it. Perceived behavioural control refers to an individual’s perception of their capability to engage with social media marketing content and complete a purchase [38]. This concept is influenced by the user’s ability to navigate through the content easily, the clarity of the information provided, and the overall user experience. As the TPB emphasises, well-liked brands tend to attract more interaction, increasing the likelihood of people wanting to buy their products or services. Using the expectations-confirmation theory [39] and social capital theory [40], a study examined the relationship between social media marketing and sustainable purchase intention. Nevertheless, the utilisation of UGT and TPB in SM research, as demonstrated by [41,42], has received limited scrutiny. Therefore, this study will employ the TPB and UGT to investigate the connection between the constructs. Our study utilised the TPB to elucidate the correlation between SMM, BE, and SPI.

2.2. Gratification Theory (UGT)

The UGT was established by Katz, Blumler, and Gurevitch in 1973 to assess the motivations and satisfactions experienced by users of specific media [43]. UGT, an early framework for examining the audience’s role in media selection, underscores individuals’ active pursuit, identification, and utilisation of media to fulfil distinct needs [44]. This study employed the UGT to elucidate the relationship between SMM, CQ, and SPI. The theory asserts that users deliberately select social media content based on the gratifications it provides [47]. According to [22], there are 5.16 billion active social media users, representing 59.3% of the global population. With more than 3.15 billion monthly active users, Facebook is the leading platform, followed by YouTube with 2.5 billion active users, Instagram with 2 billion users, and TikTok with 1 billion users. For instance, users are more inclined to engage when the content delivers value, such as informative or entertaining material. They are attracted to high-quality content, which fosters interactions, including liking, sharing, and commenting. UGT emphasises user motivation by fulfilling specific needs through engagement with content.

2.3. Social Media Marketing, Sustainable Purchase Intentions, Behavioural Engagement and Content Quality

In the context of the internet, SM serves as a mechanism for promoting collaboration and individual development while simultaneously facilitating communication and engagement [48]. As noted in [49], SMM functions as both an indirect and direct marketing strategy aimed at enhancing brand recognition and awareness. Presently, individuals utilise this tool across various social media platforms, including social networking and bookmarking sites, which are evaluated based on three key aspects of SMM: entertainment, interactivity, and trendiness [50]. A significant proportion of users, specifically 59%, fall within the age range of 18–34 years. Engaging in activities such as watching videos (82%), reading news articles (76%), and conversing with friends and family (72%) are among the most prevalent uses of social media.

Sustainable purchase intention refers to a consumer’s preparedness or intention to acquire products that are environmentally friendly, socially responsible, and economically viable [51]. This intention is influenced by various factors, including environmental awareness, ethical considerations, perceived product quality, and societal norms. The significance of this concept lies in its capacity to promote sustainable consumption and encourage businesses to adopt environmentally sound practices. The tendency and preference toward a particular brand or product are intricately linked to purchasing behaviour [52]. SMM exerts a considerable influence on consumers’ purchase intentions, as evidenced by the research conducted by [53,54]. Similarly, the TPB posits that individuals who possess a strong emotional affiliation with a specific brand within a particular product category, owing to effective and consistent marketing initiatives by the company, are more likely to demonstrate an intention to purchase the product [55].

CQ represents a critical determinant affecting the acceptance and utilisation of digital platforms, systems, and resources [56]. CQ refers to the extent of understanding, intrinsic interpretation, precision, and overall significance to the recipient of the outputs generated by a platform, system, or tool. The TPB posits that individuals exhibit a greater propensity to participate in sustainable purchasing behaviours when they perceive that their peers or social networks endorse the quality of the product or content. In light of this evidence, we propose the following hypotheses:

H1. 

Social media marketing significantly affects sustainable purchase intention.

H2. 

Social media marketing significantly influences content quality.

H3. 

Social media marketing has a significant effect on behavioural engagement.

2.4. Content Quality and Sustainable Purchase Intentions

According to studies conducted by [57,58], content marketing represents a strategic approach involving the creation, distribution, and promotion of content aimed at attracting customers. Effective content seldom prioritises the promotion of products; instead, it emphasises assisting clients in addressing their challenges and providing guidance on topics about which they may have uncertainties [59]. In accordance with the UGT, users intentionally select SM content based on their preferences. A previous study has demonstrated that CQ has a substantial impact on SPI, as reported by [60]. Access to accurate and reliable information enables individuals to gain knowledge regarding specific products or services and to receive endorsements pertaining to them. [61] assert that social media provides users with prompt, comprehensive, reliable, and up-to-date information. The quality of information disseminated through social media significantly influences consumers’ perceptions of brands as trustworthy, thereby enhancing their propensity to make purchases [62]. Customers receive relevant information concerning brand promotions, and if this information aligns with their needs, they are more likely to purchase the brand [63]. Consequently, we propose the following hypotheses:

H4. 

Content quality significantly influences sustainable purchase intention.

2.5. Behavioural Engagement and Sustainable Purchase Intentions

The authors [64] assert that digital BE constitutes a dynamic concept that evolves in response to technological advancements and shifts in consumer behaviour. Digital behavioural engagement entails the extent of time, energy, and attention that a customer dedicates to a brand during specific interactions between the consumer and the brand. This may encompass activities such as liking, sharing, and commenting on content posted by users on social media platforms [65]. Perceived behavioural control within the TPB refers to an individual’s perceived ability to interact with social media marketing content and make a purchase. BE, as identified by [41], serves as a catalyst for SPI. Moreover, engaged customers possess the potential to substantially enhance the overall well-being of firms through their patronage behaviours, as emphasised by [66]. Consequently, we propose the following hypotheses:

H5. 

Behavioural engagement significantly affects customer sustainable purchase intention.

3. Research Methodology

This study employed quantitative research methodologies, collecting numerical data to achieve an in-depth understanding of a particular organisation or event [67]. The objective is to systematically interpret patterns, order, and design among participants [68]. The research commences with a deductive theory, which is subsequently tested or validated by the results, thereby establishing a framework for the research questions and hypotheses [68,69]. The proposed model incorporates constructs such as sustainable purchase intention, social media marketing, behavioural engagement, and content quality, all of which have been examined in empirical studies conducted by various scholars. Consequently, Figure 1 illustrates the proposed measurement model, and the existing body of literature guides the suggested measurement of the constructs utilised in this study.

3.1. Sampling

The study’s research population consists of students currently enrolled in higher education institutions in North Cyprus. It employs judgemental sampling, also known as purposive sampling, which is a non-probability technique designed to select participants based on their experience, knowledge, and engagement. Since this study examines the respondents’ engagement with social media platforms, it aligns directly with the study’s objectives. This technique enhances data accuracy by concentrating on individuals who are actively involved in online discourse, ensuring that their experiences and perspectives yield valuable insights for the research. Given the limitations of random sampling, which may produce less relevant responses, judgemental sampling enables researchers to focus on participants with specialised expertise and significant interactions, thereby enhancing the diversity, validity, and contextual depth of the information gathered. This methodologically rigorous selection process ensures that the dataset is robust, meaningful, and directly pertinent to the research framework. Established research guidelines recommend multiplying the number of survey items by 5 or 10, which is generally deemed sufficient for rigorous statistical analysis. The survey consisted of 15 items, resulting in a total of 450 (15 × 10 = 150) responses, which exceeds the necessary number.

Additionally, [70] confirmed that the sample size positively influences reliability and asserted that measurement theory typically does not accommodate significant sampling errors; therefore, a sample size of 300 or more has been recommended. Furthermore, the research by [71,72] suggests that a sample size of 300 or more is sufficient and unbiased for conducting factor analysis. In consideration of the aforementioned studies, it can be assertively confirmed that the reliability of this questionnaire is upheld, given the study’s sample size of 450. This sample size was chosen to enhance the reliability and validity of the findings, allowing for significant insights while minimising potential biases associated with smaller samples. The methodology adheres to established criteria in survey research, ensuring that the dataset is sufficiently large to support robust results and generalisability within the study’s scope.

Ethical considerations were integral to the study. Explicit and informed consent was obtained from all participants prior to their involvement. They were informed about the study’s aims, methodology, and potential effects, allowing them to make an informed decision about participation. Data collected was kept confidential, ensuring individual responses could not be traced back to participants. They were also made aware that participation was voluntary and that they could withdraw at any time without negative consequences. This approach respected their rights and aligned with ethical principles. To improve clarity and data accuracy, the authors included detailed definitions of the constructs used in the study within the questionnaire design. This strategy minimises potential misinterpretations and enhances the validity of the collected responses.

3.2. Data Collection and Procedure

The authors compiled a set of questions for the survey questionnaires after a thorough review of the relevant literature. Furthermore, the authors developed structured questionnaires and sought the evaluation of their validity from three experts in the field. The authors revised particular questions in light of their feedback. They then conducted a pilot study involving thirty participants who had experience with social media platforms via an online questionnaire. The survey was uploaded to Microsoft Forms, and a shareable link was generated. The link was disseminated via social media platforms such as WhatsApp, Instagram, and Facebook, facilitating effective data collection and broad accessibility [73,74]. Utilising the collected data from the thirty participants, reliability and validity measures were evaluated. The results indicated robust validity and reliability metrics, which indicate that the research tool is highly reliable and stable, making it suitable for the study (i.e., Cronbach’s alpha > 0.70; AVE > 0.50) [75].

The inclusion criteria for this study comprised students aged 18 and older who had experience using digital platforms, such as WhatsApp, Instagram, and Facebook. This criterion ensured that participants were familiar with the technological facets essential to the research. The study excluded individuals who were under 18 years of age, non-students, and those without experience using digital platforms to maintain data accuracy and relevance. By applying these inclusion and exclusion criteria, the study focused on a specific population likely to provide valuable insights regarding the research topic.

The survey instrument is composed of two sections. Originally, the authors organised the demographic information, which encompasses age, gender, educational attainment, and the social media platforms utilised. Initially, the aim was to gather data from 600 participants to enhance the generalisability of the findings. A total of 485 responses were received. Following a thorough review, 35 incomplete and inept responses were removed, leaving 450 valid responses for further analysis, reflecting a 75% response rate. This approach ensured higher response accuracy, minimised biases often associated with online surveys, and strengthened the reliability of the dataset for subsequent examinations.

3.3. Data Analysis

Data cleaning was performed to detect missing values, coding errors, or any illogical data values using SPSS (version 25) [76]. This study uses Structural Equation Modelling (SEM), a method that looks at how different measurable factors relate to hidden factors that cannot be measured directly. The structural equation modelling method, specifically partial least squares (PLS), was employed to evaluate structural measures and models [77]. PLS is advantageous because it imposes minimal assumptions on data distribution and accommodates nominal, ordinal, and interval scale variables [78]. It is also good at finding differences between groups when the data is not normally distributed and is best suited for predicting a set of dependent variables based on many independent factors [79,80,81].

Accordingly, 244 respondents (54.2%) identified as female, while 206 (45.8%) identified as male. The majority of respondents, comprising 353 (78.4%), held undergraduate degrees; 69 (15.3%) possessed a master’s degree; and 28 (2.9%) obtained a PhD. Furthermore, 175 respondents (38.9%) were aged between 21 and 25 years, 121 (26.9%) were within the age range of 18 to 20 years, 81 respondents (18.0%) fell within the age group of 26 to 30 years, 40 respondents (8.9%) were aged between 31 and 35 years, 23 (5.1%) were between 36 and 40 years, and 10 (2.2%) were aged 41 years or older. The predominant social media platform utilised by respondents was WhatsApp, with 171 (38.0%) indicating it as their preferred choice, followed closely by Facebook at 167 (37.1%), Instagram at 92 (20.4%), and Vkontakte at 20 (4.4%) (

Table 1. Demographic information.

		Frequency	Percent
Gender	Female	244	54.2
	Male	206	45.8
Level of education	Undergraduate	353	78.4
	Masters	69	15.3
	PhD	28	6.2
Age	18–20	121	26.9
	21–25	175	38.9
	26–30	81	18.0
	31–35	40	8.9
	36–40	23	5.1
	41 and above	10	2.2
Social Media Platform used	Facebook	167	37.1
	WhatsApp	171	38.0
	Instagram	92	20.4
	Vkontakte	20	4.4

).

The second component comprises four variables, each encompassing distinct items. Sustainable purchase intention is represented by five items, including statements such as: “My intention to become a sustainable online shopper is positive and enthusiastic,” and “I have a significant intention to replace the traditional sustainable shopping pattern with sustainable E-shopping.” Furthermore, while browsing a product, I intend to engage in a sustainable purchase process online. The variables of social media marketing, behavioural engagement, and content quality are represented by three items, three items, and four items, respectively. The authors developed the questionnaire utilising a 5-point Likert scale, which ranges from 1 (strongly disagree) to 5 (strongly agree). Additionally, the items within the questionnaire were derived from prior research studies. The authors adopted and modified survey instrument items from the source referenced as [82] to evaluate social media marketing and purchase intention, whereas behavioural engagement and content quality were drawn from source [83]. See Appendix A.

4. Data Analysis and Results

4.1. Measurement Model Assessment

4.1.1. Convergent Validity

The evaluation of the convergent validity measurement model (outer model) comprises the extraction of average variance (AVE), individual indicator reliability, and composite reliability (CR) for internal consistency [79,83]. Indicator reliability explains the variance in the items caused by a variable. A higher value on a variable (an item with a loading of 0.70) indicates that the related measure has a significant degree of reciprocal similarity. Outer loadings evaluate the reliability of indicators [84]. To assess composite reliability, we use internal consistency and reliability. To ensure internal consistency, all the latent variables’ CR values are greater than 0.80 (see

Table 2. Reliability and validity.

	Loading	α	CR	AVE	VIF
BE1	0.884				2.122
BE2	0.875	0.848	0.908	0.767	2.011
BE3	0.867				2.028
CQ1	0.911				3.540
CQ2	0.828				2.229
CQ3	0.849	0.898	0.928	0.765	1.958
CQ4	0.907				3.597
SPI1	0.854				2.531
SPI2	0.901				4.933
SPI3	0.884	0.910	0.933	0.735	2.952
SPI4	0.806				1.969
SPI5	0.838				3.678
SMM1	0.839				1.856
SMM2	0.869				1.884
SMM3	0.891	0.835	0.900	0.751	2.170

Note: α; Cronbach’s alpha, CR: composite reliability, AVE: average variance extracted, VIF: variance inflation factor, BE: behavioural engagement, CQ: content quality, SPI: sustainable purchase intention, SMM: social media marketing.

) [85].

4.1.2. Discriminant Validity

The extent to which a variable is substantially different from other variables is known as discriminant validity. To ensure discriminant validity, the square root of AVE must be greater than the inter-construct correlations [86]. The diagonal values represent the square root of the AVE, while the off-diagonal values indicate the correlations among the variables [87]. The square root of each construct’s AVE (BE = 0.876, CQ = 0.874, SPI = 0.857, SMM = 0.867) is greater than its correlations with other constructs, demonstrating that the model effectively establishes the distinctiveness of the constructs. This data shows that SMM has moderate correlations with SPI at 0.508, BE at 0.399, and CQ at 0.260, while remaining distinct from each. Moreover, SPI has a stronger correlation with BE (0.541) compared to CQ (0.346), suggesting that BE influences SPI more significantly than CQ does. These findings indicate that the constructs are sufficiently distinct, alleviating concerns about overlapping variables in the model. The Heterotrait-Monotrait (HTMT) ratio is an essential measure for assessing discriminant validity in PLS-SEM, confirming that each construct is conceptually unique while preserving significant interrelationships [87,88]. In this model, all HTMT values are below the widely accepted limits of 0.85 or 0.90, signifying that the variables are sufficiently distinct. The correlation between BE and SPI is moderate at 0.614, indicating a significant link. Conversely, CQ and SPI display a correlation of 0.376, indicating robust discriminant validity and affirming their uniqueness. SMM exhibits moderate correlations with SPI (0.578) and BE (0.471), although it notably differs from CQ (0.290), hence reinforcing the model’s validity. Given that all constructs meet the HTMT criteria, the results indicate that the model is robust and does not demonstrate significant overlap among variables. We present the Fornell-Larcker and HTMT findings below (see

Table 3. Discriminant validity—Fornell–Larcker criterion.

	BE	CQ	SPI	SMM
BE	0.876
CQ	0.435	0.874
SPI	0.541	0.346	0.857
SMM	0.399	0.260	0.508	0.867

and

Table 4. Discriminant validity—Heterotrait-monotrait ratio (HTMT).

	BE	CQ	SPI	SMM
BE
CQ	0.497
SPI	0.614	0.376
SMM	0.471	0.290	0.578

).

4.2. Structural Model Assessment

Following a comprehensive assessment of the validity and reliability of the variables, we proceed to analyse the structural model. Path coefficients have been utilised to evaluate the structural model, thereby determining the significance and applicability of the relationships encapsulated within the model. Furthermore, a summary comprising the path coefficients and correlations between latent concepts, along with their corresponding t-test values, has been generated. After implementing bootstrapping techniques, Figure 2 illustrates the correlations among the study variables: SMM, CQ, BE, and SPI. Bootstrapping is employed to systematically estimate the path model using slightly varied data configurations, as delineated by [88,89,90]. The output derived from the bootstrap process offers various methodologies to assess the stability of the model parameters. Confidence intervals are inherently less susceptible to misinterpretation and provide vital information regarding the magnitude of the effect. The bias-corrected bootstrap confidence interval effectively compensates for bias arising from nonsymmetric distributions and their characteristics, particularly skewness.

4.2.1. Assessing R2, Q2 and f2

The coefficient of determination (R²) quantifies the squared correlation between the observed and predicted values of the endogenous variable, indicating the model’s predictive accuracy [91,92,93,94]. The R² values illustrate the degree to which social media marketing influences changes in BE, CQ, and SPI. While there is no universally accepted criterion, existing standards suggest the following classifications: Weak relationship: R² < 0.19; Moderate relationship: R² between 0.20 and 0.49; substantial relationship: R² ≥ 0.50 [95]. According to the results, the R² value of 0.159 for BE indicates a weak association, implying that while social media marketing has some influence, other factors significantly impact engagement. In a similar vein, the R² value of 0.068 for CQ is extremely weak, indicating that social media marketing has minimal explanatory power regarding improvements in content quality. On the other hand, the R² value of 0.403 for SPI shows a moderate relationship, meaning that social media marketing plays a significant role in customers’ choices to buy sustainably, but other factors also matter. These findings demonstrate that, while social media marketing does affect purchase intention, its influence on engagement and content quality is quite limited, which emphasises the requirement for additional strategies to enhance these aspects.

The Q² values represent the model’s predictive relevance [89]. According to the literature, values greater than zero indicate predictive power, while values approaching or exceeding 0.25 suggest stronger predictive strength [90]. The Q² value of 0.152 for BE indicates moderate predictive relevance, suggesting that the model can reasonably predict engagement trends. In contrast, the Q² value of 0.058 for CQ indicates minimal predictive relevance, implying that social media marketing has a limited ability to predict improvements in content quality. Meanwhile, the Q² value of 0.253 for SPI shows strong predictive relevance, indicating that the model effectively forecasts consumer intentions regarding sustainable purchases. Overall, the results suggest that while social media marketing significantly influences engagement and purchasing behaviour, its effectiveness in predicting content quality is rather limited. The f2 demonstrates a substantial impact of the exogenous variable on the endogenous variable [91]. In PLS-SEM, f² values are used to assess the effect size of exogenous factors on endogenous factors. The standard thresholds for interpreting f² values found in the literature are as follows: Small effect: 0.02–0.14, moderate effect: 0.15–0.34, and high effect: ≥0.35 [92,93,94,95]. The relationships BE → SPI (0.160) and SMM → SPI (0.158) show moderate effect sizes, indicating that behavioural engagement and social media marketing significantly influence sustainable purchase intention. The relationship SMM → BE (0.189) also indicates a moderate effect, underscoring the impact of SMM on BE. In contrast, SMM → CQ (0.073) falls within the small effect range, suggesting a minimal influence of social media marketing on content quality. Furthermore, CQ → SPI (0.014) demonstrates a minimal effect of content quality on sustainable purchase intention. These findings imply that, while social media marketing and behavioural engagement notably affect customer purchasing decisions, their influence on content quality remains minimal, indicating a need for further exploration of additional influencing factors (see

Table 5. Assessing R², Q², and f².

	Q2 Predict	R-Square	f-Square
BE	0.152	0.159
CQ	0.058	0.068
SPI	0.253	0.403
BE → SPI			0.160
CQ → SPI			0.014
SMM → BE			0.189
SMM → CQ			0.073
SMM → SPI			0.158

).

4.2.2. Hypotheses Test

The postulated hypotheses, as conceptualised in this study, were tested, and the results are as follows: H1 examines the effect of SMM on SPI (β = 0.337, t = 6.710, p < 0.001). As a result, H1 is supported. H2 assesses if SMM significantly affects BE (β = 0.399, t = 8.316, p < 0.001). Hence, H2 is supported. H3 was tested to determine the effect of SMM on CQ (β = 0.260, t = 4.647, p < 0.001). Therefore, H3 is supported. To assess the effect of BE on SPI, H4 was tested (β = 0.363, t = 6.362, p < 0.001). Thus, H4 is supported. H5 evaluates the effect of CQ on SPI (β = 0.101, t = 2.247, p < 0.05). Hence, H5 is supported.

Table 6. Path coefficient.

Hypothesis		β	Mean	SD	T Statistics	P Values	Bias Corrected Interval
							Bias	2.5%	97.5%	Decision
H1	SMM → SPI	0.337	0.337	0.050	6.710	0.000	0.000	0.235	0.432	Supported
H2	SMM → BE	0.399	0.400	0.048	8.316	0.000	0.002	0.296	0.486	Supported
H3	SMM → CQ	0.260	0.261	0.056	4.647	0.000	0.001	0.145	0.366	Supported
H4	BE → SPI	0.363	0.362	0.057	6.362	0.000	−0.001	0.246	0.471	Supported
H5	CQ → SPI	0.101	0.102	0.045	2.247	0.025	0.002	0.011	0.187	Supported
Control
Social Media Platforms ← SPI		0.071	0.071	0.036	1.950	0.051	0.001	0.001	0.141

presents the results.

Social media platform was used as a control variable on purchase intention. The result shows that (β = 0.071, t = 1.950, p > 0.05) social media platforms have an insignificant controlling effect on purchase intention.

5. Discussion and Conclusions

The study investigated the influence of social media platforms on purchase intention. All hypotheses showed significant effects. The study’s findings are highly valuable for researchers, lawmakers, and private organisations seeking to enhance customer experience.

The present study found that SMM has a significant effect on SPI. This result is consistent with the finding of [29], who argue that SMM is crucial for effectively influencing the buying process across various well-known product categories, including consumer goods, cosmetics, m-banking, electronics, and textiles.

The present study’s findings demonstrate that SMM significantly affects BE. Our finding aligns with [96], which revealed that organisations can boost consumer engagement through social media, leading to higher purchase intentions. Furthermore, SMM encourages consumers to obtain and share information, learn about trends, and stay informed about new products or services. Additionally, the findings suggest that content related to sustainability, such as posts about eco-friendly products or corporate social responsibility, could enhance both engagement and purchase intention. This implies that sustainability-focused campaigns on social media may be especially effective in appealing to environmentally conscious younger consumers.

A significant relationship between SMM and CQ was identified. This finding is comparable to that of [60], which found a direct and significant relationship between SMM and CQ. Moreover, a perception of high quality likely fosters strong brand engagement and increases sustainable purchase intention for the recommended products.

The present study found a significant connection between BE and SPI. This finding aligns with [97], who affirm that a significant correlation exists between BE and SPI. They further state that engaged consumers demonstrate empowerment, connection, and commitment, leading to increased purchase intention. Engaging consumers with sustainability-focused information nurtures trust, understanding, and passion, aligning attitudes with environmentally conscious behaviour and enhancing the chance of selecting sustainable solutions while prioritising sustainable choices.

We found a weak, significant relationship between CQ and SPI. A possible reason CQ has less impact is that purchasing decisions are often influenced more by emotions and interpersonal interactions, such as social connections and personal interests, rather than solely by the factual quality of information. Even high-quality content may lack persuasive power if it does not actively engage consumers, making behavioural engagement a key factor in determining purchase intention [98]. This finding is consistent with [62], which established a significant relationship between CQ and SPI. The study further emphasises that high-quality content effectively communicates the benefits, values, and impacts of sustainable practices, building trust and enhancing brand credibility. It encourages consumers to align their purchasing behaviours with eco-conscious values, highlighting the importance of effective content strategies. The results highlight the importance of interaction-driven marketing strategies, emphasising that while content quality plays a vital role, its effectiveness is limited unless it is combined with strong engagement dynamics to enhance customer purchasing behaviour.

Social media marketing platforms were utilised as a control variable for the sustainable purchase intention of younger individuals. The results indicate that social media platforms do not control the sustainable purchase intention of younger people. Although social media serves as a significant tool for engaging younger demographics, its influence may not be crucial in shaping their sustainable purchase decisions. This conclusion emphasises the need for organisations to prioritise content strategies and ideals that resonate with this group, going beyond mere platform presence.

In this quantitative survey study, we investigated the controlling effect of social media platforms on sustainable purchase intention. The current research presents hypotheses based on UGT and TPB. The combination of these theories highlights the significance of social media marketing, behavioural engagement, and content quality on purchase intention. Contributing to the existing literature based on UGT and TPB, the present study found that social media marketing and behavioural engagement significantly affect sustainable purchase intention. UGT suggests that users seek gratification from social media interactions, which can lead to increased engagement. Social media marketing effectively engages users, fostering active participation (likes, shares, and comments). As a result, brands should focus on user-engaged content to enhance participation. The findings indicate that behavioural engagement has a significant impact on purchase intention. User engagement with social media content serves as a strong indicator of their interest and active involvement.

Furthermore, as established in the TPB, this engagement positively influences the likelihood of subsequent purchase behaviour. Content quality was also found to significantly affect purchase intention. Although content quality is essential, its direct impact on sustainable purchase intention may be less pronounced due to other factors outlined in the TPB, which emphasise subjective norms, attitudes, and perceived behavioural control more. Integrating the sustainability dimension into the analysis provides additional insight into consumer behaviour. Younger individuals not only respond to general product marketing but also exhibit increased purchase intention when brands stress environmental and social responsibility. Social media platforms had an insignificant controlling effect on sustainable purchase intention. According to the Theory of Planned Behaviour, a consumer’s favourable opinion toward a product significantly influences their purchase intention, alongside subjective norms and perceived behavioural control. The social media platforms used for information retrieval do not substantially alter their intention, as their decisions are already rooted in these intrinsic aspects. According to UGT, consumers utilise social media to fulfil specific demands, such as acquiring knowledge or validating their opinions. Given the consumer’s favourable disposition toward the product, they engage in selective exposure, seeking information that supports their choice.

5.1. Theoretical Implications

The findings of this study provide significant contributions to the expanding body of literature regarding social media marketing (SMM) and its impact on consumer behaviour, particularly within the frameworks of the UGT and the TPB. This study substantiates that SMM directly and substantially influences SPI, behavioural engagement, and content quality. These findings reinforce the notion that SMM serves as a pivotal tool for businesses to impact consumer decisions and purchasing behaviours, consistent with UGT by illustrating that consumers interact with social media to satisfy various needs, such as information acquisition, entertainment, and social interaction [99]. Furthermore, the results indicate that behavioural engagement is a crucial element linking SMM to SPI, providing new insights into TPB by emphasising how active consumer participation on social media platforms—such as likes, shares, and comments—positively affects purchasing behaviour. This validates the function of engagement as a catalyst for online consumer actions, underscoring the significance of producing content that cultivates interaction and involvement. The study’s outcomes also endorse the importance of CQ in steering purchase decisions [100]. While content quality notably influences consumers’ perceptions and enhances brand loyalty, it aligns with the assertion that high-quality, relevant, and consistent content is fundamental in establishing brand credibility. Additionally, social media platforms exert control over the effect of social media on sustainable purchase intention among younger consumers [101,102]. This suggests that although social media platforms act as the medium for SMM, the platform itself may not significantly modify consumers’ purchasing decisions to the same extent as the content and engagement strategies employed.

5.2. Practical Implications

From a practical perspective, this study offers many actionable suggestions that assist organisations seeking to enhance their social media marketing activities. It was found that an organisation should assess its goals regarding active user engagement and brand engagement before implementing any tailored social media campaigns. Behavioural engagement has been demonstrated to be a significant determinant of purchase intention, particularly among younger consumers. This indicates that companies must prioritise not only content development but also the cultivation of active involvement from their audience. Approaches that promote user interaction with content, facilitate sharing within social circles, and invite feedback or reviews can considerably enhance the effectiveness of social media marketing initiatives.

Young people are likely to interact fluently with different platforms and place more importance on the content and level of engagement than the platform used [103]. For businesses, this means that the choice of platform is less critical than the quality of content and the type of engagement it encourages. Consequently, brands should focus on cross-platform approaches and ensure that high-quality, compelling content is distributed across various channels to increase reach and effectiveness with younger audiences. Brands that regularly engage with their audience through useful content are more likely to develop a loyal community, which may eventually translate into sustainable purchase intention. Consequently, organisations ought to achieve a balance between content quality and methods that enhance consumer engagement, recognising that both aspects are fundamental in establishing enduring relationships between brands and customers.

Diverse sectors necessitate customised ways for influencing sustainable purchasing intentions, particularly as social media platforms alone cannot substantially dictate purchasing behaviour among younger customers. For instance, in retail and fashion, firms must prioritise transparent sustainability measures, including environmentally friendly supplies and ethical purchasing, enhanced by interactive marketing and influencer partnerships to foster involvement. Technology and electronics firms can prioritise energy efficiency, recyclability, and responsible electronic waste management, promoting trade-in initiatives and repair services to increase consumer engagement. In the food and beverage sector, narrative techniques and certifications for organic and locally produced products can enhance consumer trust, whereas automotive firms can advocate for electric vehicles and carbon offset initiatives through financial incentives and leasing arrangements. Hospitality and tourism enterprises can provide environmentally sustainable accommodations and carbon-neutral trip packages, thereby enhancing the accessibility of sustainable options. By synchronising sustainability initiatives with consumer beliefs and behaviours, brands may transcend mere social media marketing to produce a significant impact and enduring consumer loyalty.

5.3. Policy Implications

The implications for policymakers are twofold. First, it emphasises the development of SMM regarding consumers’ purchase behaviour; consequently, policies that support businesses in navigating digital marketing landscapes are necessary. Policymakers should therefore provide incentives to attract businesses and companies to invest in SMM strategies that focus on creating content and engaging with the relevant behaviours. This may include tax incentives or grants for businesses that invest in digital marketing technologies and social media training related to brand building, thus fostering a more competitive and innovative digital marketplace.

Second, governments and relevant stakeholders need to promote the establishment of industrial standards and best practices for digital marketing, especially in sectors where consumer confidence and engagement are vital, such as the hospitality and service industries. Encouraging the adoption of transparent and ethical SMM practices can help protect consumers while enabling businesses to develop stronger, more authentic connections with their audience.

Policymakers could also collaborate with educational institutions to create training programmes that enhance businesses’ capabilities in SMM. By investing in education and training that positions small and medium-sized enterprises (SMES) on equal footing with larger companies in terms of social media interaction and consumer outreach, policymakers can boost the competitive power of SMES in the digital market. Emphasising these elements, however, allows businesses to create more effective and sustainable social media strategies that can lead to long-term success.

5.4. Limitations and Future Research

This study has several limitations that should be acknowledged. First, the generalisability of results might be limited due to the small sample size, primarily consisting of young students. Future research can employ a more diverse sample, including different age groups, geographical regions, and professions, allowing for a better and deeper understanding of the investigated phenomena. Additionally, the cross-sectional study design limits the ability to trace changes in consumer behavioural patterns over time, providing only a snapshot of the relationships among SMM, SPI, and other relevant factors at a single point in time. In contrast, a longitudinal design would have facilitated observing the relationships under scrutiny as they change over time. A notable constraint of this study is the possibility of common method bias (CMB), as all variables were assessed by a single questionnaire at a single time point. CMB arises when systematic measurement errors erroneously enhance or distort the relationships within constructs, leading to biased results [83,104,105,106,107,108,109,110,111,112]. This bias may arise from variables such as social desirability, consistency themes, or common rater effects, wherein respondents unintentionally conform their answers to prior responses instead of offering independent assessments. Thus, the identified connections may exaggerate or distort the true associations between variables. To mitigate this constraint, forthcoming research may employ diverse data sources, implement temporal separation of measurements, or leverage statistical methodologies, such as Harman’s single-factor test or marker variables to evaluate and manage CMB.

Furthermore, the focus on students introduces a sampling bias, as students likely have specific social media usage patterns and purchasing behaviours that differ from those of the general population. This issue limits the research; therefore, future studies are encouraged to adopt a mixed-methods approach that can provide both qualitative and quantitative analyses. Qualitative analyses, such as interviews or focus groups, would offer more in-depth insights into the motivations and attitudes of consumers toward SMM. At the same time, experimental designs would create opportunities for establishing more robust causal links between the variables. The study’s longitudinal design could also yield further insights into how SMM impacts SPI over time and whether these effects evolve as consumer engagement with social media platforms changes. This approach would also facilitate capturing changes in long-term brand loyalty and purchasing habits and advancing an analytical perspective on the dynamics of consumer-brand interactions. Another consideration is the differential effects of various social networking sites on SPI. Each platform, such as Instagram, TikTok, and Facebook, has its unique user dynamics, audience profiles, and content-sharing mechanisms, which may influence customer behaviour differently. Understanding these platform-specific effects can help clarify how companies should tailor their SMM strategies based on the strengths and weaknesses of each particular platform. Future research should further explore different dimensions of the proposed research model with respect to various demographic groups. Thus, future studies should determine whether gender influences online shopping behaviour.

Regarding social media marketing, female buyers, who are generally more enthusiastic about e-commerce than their male counterparts, may respond differently, particularly in the contexts of fashion, beauty, and home care. It has also been observed that men tend to shop online for specific product categories like technology and accessories, which may uncover unique behaviours meriting further investigation. An analysis of gender-specific usage patterns can yield more detailed insights for future research, enabling marketers to tailor their social media marketing strategies to specific target demographics. Although PLS-SEM is an effective tool for examining complex interactions, it possesses significant limitations that render it inadequate as a fully autonomous tool [113,114,115]. A significant limitation is the absence of robustness checks, which could trigger concerns regarding the reliability and validity of the results. Furthermore, PLS-SEM exhibits considerable sensitivity to sample size, indicating that outcomes may fluctuate markedly based on the dataset employed [116]. It also employs bootstrapping for significance testing, which may not consistently produce the most accurate estimates in comparison to conventional parametric methods [117]. To strengthen the trustworthiness and broader application of the results [118,119,120,121,122], researchers might consider using other methods, like CB-SEM or different evaluations, to confirm their findings and improve analysis accuracy. This study is limited by potential cultural bias stemming from a single-region sample. The results may not be entirely applicable to various geographic locations because of cultural norms, economic conditions, and regional trends. Subsequent studies may address this constraint by carrying out cross-regional comparisons or broadening the sample to encompass an extensive range of subjects.