Theoretical Framework: The S-O-R Model

To provide a robust theoretical underpinning for the relationships proposed in this study, the Stimulus-Organism-Response (S-O-R) framework (Mehrabian & Russell, 1974) is integrated. The S-O-R model posits that external stimuli (S) influence internal organismic states (O), which subsequently lead to behavioural responses (R). In the context of digital fashion branding, elements like brand storytelling, UGC and collaborations act as stimuli (S). These elements are perceived and processed by consumers through cognitive and emotional evaluations (O), which then influence outcomes such as brand image and engagement (R). This framework supports previous empirical assertions (Hollebeek et al., 2014) that digital content influences internal psychological states before triggering brand-related behaviours, thereby justifying the chosen relationships within the proposed model.

Brand Storytelling, Social Media and Websites

For a brand to create and foster a healthy customer base has to emotionally connect with the target audience by fabricating a brand image for their audiences. To sustain a stable customer base, marketers should aim to develop deeper, long-lasting and strong brand attachments for consumers (Iveson et al., 2026). For this, marketers take the assistance of brand storytelling. Brand storytelling is to share any story or belief with a target group to arouse bonds in people’s minds or emotions so that such a brand becomes more attractive, more recognised and provokes relationships among people in the organisation and with other stakeholders outside the organisation (Kosakarika, 2023). Storytelling has emerged as an effective content marketing strategy that helps brands build meaningful relationships with consumers by creating emotionally appealing narratives. Such narratives strengthen consumer-brand engagement and encourage stronger brand loyalty by making audiences feel more connected to the brand (Appel et al., 2020). Research also suggests that emotional engagement developed through storytelling reflects the extent to which consumers form deeper psychological connections with brands (Dessart & Pitardi, 2019). In addition, immersive storytelling experiences supported through interactive and vivid content can further enhance audience involvement and engagement. When consumers feel absorbed in a brand narrative, their emotional attachment and sense of presence increase, making storytelling an important mechanism for fostering authentic consumer-brand relationships (Ahmed et al., 2024). A powerful brand story imprints a positive brand idea towards the target audience. The online industry is fuelled by content, and therefore, content marketing appears to be the best for approaching and creating brand image and personality. With a major involvement of influencers, brands can introduce themselves in a very clean and crafted manner. Brand storytelling is useful for building strong brands and brand loyalty because it has the power to persuade consumers, entertain them and imprint a brand in their minds (JungHwa Hong, 2022). Thus, the study hypothesises:

H₁: Brand storytelling has a positive relationship with social media usage.

H₂: Brand storytelling has a positive relationship with website usage.

Collaboration, Social Media and Websites

In the digital marketing age, where people have a very media-driven lifestyle, marketers have evolved their marketing approach towards the marketplace. Now, the content is generated by popular and influential people posting content on the online space, known as influencers. An influencer is an individual with a large following and has a powerful influence on their followers, their opinions and sometimes their decisions. Celebrities, social media stars, industrial experts, brand ambassadors, content creators—bloggers, videographers/vloggers, artists, podcasters and photographers—are the influencers of the modern marketplace. Influencers, due to their large follower base and niche content creation across social media platforms, foster higher audience interaction and engagement as followers often relate to or aspire to the lifestyles and values reflected in their content (Levin et al., 2025). Previous studies have suggested that collaborations with influencers who create relatable and informative content, particularly when aligned with expert perspectives and consistent with their existing values, enhance authenticity and strengthen audience engagement (Paraskeva et al., 2024). The online industry was initially governed by very niche fashion setters, but now it has opened up. The influencers are the driving force of various categories, such as fashion, tech, beauty, lifestyle and travel, and play a very crucial role in trend-setting. Due to consistent content posting about trends and recommendations, they exert an influential force on their followers to engage with their suggestions (Nurfadila, 2020). Social media and the employment of influencers by brands are a driving force for the purchasing decision of a consumer. Thus, the study hypothesises:

H₃: Collaborations have a positive relationship with social media usage.

H₄: Collaborations have a positive relationship with website usage.

UGC, Social Media and Websites

Brands may now effectively engage with their audience and create a feeling of community by using UGC (Ploscă et al., 2024). It has the potential to significantly impact brand perception, consumer loyalty, brand image and purchasing decisions in the online space. Gharib et al. (2019) contend that better product development and consumer comprehension can be achieved by studying UGC. Businesses can get insights that guide their product design and development process and result in more effective and relevant offers by analysing the feedback, preferences and experiences that consumers share (Fu et al., 2024). Similarly, Djafarova and Bowes (2021) contend that because of its easily navigable design and freely available UGC, social media, especially Instagram, has grown in popularity as a platform for online purchasing. Because they believe UGC, often referred to as electronic word-of-mouth (eWOM), to be truthful and grounded in real-world experiences, consumers are more likely to trust it than traditional marketing communications. UGC is important to drive customer interaction, shape brand perception and influence purchase decisions (Osorio-Andrade et al., 2025). Through its insights into consumer preferences, UGC helps firms generate more relevant products and improve their product development (Gharib et al., 2019). Furthermore, UGC is beneficial in establishing trust and encouraging customer loyalty because of its authenticity and legitimacy (Djafarova & Bowes, 2021). Even though previous studies have recognised the significance of UGC, there is still a significant knowledge vacuum in understanding how UGC, along with brand storytelling and collaboration, can help generate positive brand engagement and develop brand image on websites and social media. Thus, the study hypothesises:

H₇: UGC leads to more social media usage.

H₈: UGC strengthens the website’s credibility.

Social Media

Social media has completely changed how companies communicate with their customers. Social media channels present a distinctive chance for brands to interact with clients, increase brand recognition and cultivate customer loyalty. Kim and Ko (2012) state that to sustain an exclusive and appealing image, luxury brands can use social media platforms as a potent tool for storytelling, interaction and communication with their target audience, while Lu et al. (2022) mention that social media has developed into an essential tool for B2C companies to communicate with clients. Crafting compelling communications is still difficult, though. This review of the literature looks at how customer engagement on social media is affected by the message source and message content (emojis vs. objective information). According to available data, the message’s source and content affect consumers’ perceptions and levels of trust, which in turn affects engagement. However, knowledge of how these components work together to promote engagement in a business-to-business setting is lacking. Liu et al. (2020) suggest a technique that combines human judgement with big data from social media networks to enhance demand forecasts for new items. They illustrate the difficulties in accurately classifying social media’s influence factors on demand forecasting, converting insights into judgements that can be implemented and maintaining data dependability. They also show how effective this method is in a real-world scenario. The literature review investigates how social media influences consumer experiences and propels brand success. It emphasises how crucial social media is to the storytelling and engagement of luxury brands, as well as the efficient communication of B2C businesses. However, the study finds a knowledge vacuum on how message source and content interact to shape customer engagement, especially in business-to-business settings. Subsequent investigations may probe this field more thoroughly and examine the enduring effects of social media involvement on customer conduct and brand allegiance.

Websites

In the past 10 years, websites have transformed, becoming a significant element of digital brand strategy, serving as a dynamic platform that shapes consumer perceptions and nurtures brand engagement (Shehu et al., 2021). In the fashion industry, a brand’s website functions as a primary digital touchpoint that has the ability to influence consumer behaviour through visual aesthetics, interactive interface and content framework. A coherent, streamlined website with clear navigations and personalised elements can significantly boost the user experience and nurture consumer retention (Natasha et al., 2021).

As per recent studies, incorporation of brand storytelling, collaborations and UGC into a website design can increase emotional resonance and engagement (Krishen et al., 2021). Similarly, when storytelling is embedded into a brand’s digital interface, it creates a profound psychological connection with the users. Likewise, collaborations on websites provide authenticity and cultural relevance, strengthening user trust and involvement (Yin et al., 2026). In the same way, UGC, on a brand’s website, builds social proof and improves interactivity, demonstrating brand loyalty and authenticity (Jiang et al., 2025).

Thoughtful website functionalities further support brand image formation and sustained engagement (Xu & Schrier, 2019). Through these dynamics, which demonstrate websites as aid to strategic conduit, brands can operationalise their digital narratives and co-create value with consumers.

Social Media, Websites and Brand Image

Brand image is a consumer’s perception of a particular brand, which can be related to the product quality, services or experiences. The online industry is a rapidly growing industry where marketers have to leave a lasting impression to build a good brand image, and this is where engagement and personalisation play their role in building a brand image. Keller (1993) mentioned that the reflection that the consumer holds about a particular brand depends on how they perceive it and what they think about it. Kotler and Keller (2016) believe a brand image to be the way that customers view a company, shaped by things like experiences, services and quality of products. They underline that brand image is vital in creating consumer satisfaction and loyalty. Besides the significant impact of brand image in building loyalty, it holds a major role in creating engagement, which also positively impacts brand loyalty (Kim & Ko, 2012). Prior studies have also validated that the emotional association with a brand strengthens brand loyalty, which the brands leverage for premium prices (Diniso, 2025). Similarly, in the fashion sector, customer engagement and loyalty are greatly influenced by brand image. Customers’ perceptions of a brand are greatly influenced by their experiences, services and product quality. Enhanced consumer satisfaction and loyalty can result from better brand interaction achieved through these components (Orús et al., 2026). Furthermore, brand image enhancement can be achieved through experiences that incorporate components such as UGC (Lanz et al., 2024), brand storytelling (Ahmed et al., 2024) and collaboration (Ika Sari et al., 2024). These tactics in influencing consumer perceptions and emotional ties to the brand have been widely examined in the literature. Through the cultivation of genuine relationships, brands may bolster their trustworthiness and hence cultivate a more positive brand perception. Thus, the study hypothesises:

H₅: Social media usage strengthens the brand image.

H₉: Websites positively impact the brand image.

Social Media, Websites and Brand Engagement

With the rapid changes in online consumption behaviour, brands are under pressure to provide customers with distinctive and customised experiences. To develop strong brand ties and encourage consumer loyalty, engagement is essential (Aldhamiri et al., 2024). Hollebeek and Macky (2019) argue that relationships, trust and brand engagement can only be developed through digital content marketing. It promotes behavioural, emotional and cognitive engagement through the provision of quality content, which in turn increases favourable attitudes, brand equity and brand trust. Pizzi et al. (2022) suggest that engagement is a multidimensional construct of immersion, passion and activation and that interaction, brand attachment, brand trust and brand knowledge all contribute to brand engagement in the metaverse. They contend that these elements eventually influence consumers’ inclinations to make in-person purchases and are favourably influenced by the immersive experiences provided by the metaverse. Previous studies have observed that if engagement with the content satisfies the motivation, then the content marketing can strongly influence the advertising value (Mirzaei et al., 2024). By examining the distinct engagement drivers, this research aims to close the knowledge gap regarding how these two segments of the market drive engagement through different factors. Thus, the study hypothesises:

H₆: Social media usage strengthens brand engagement.

H₁₀: Websites positively impact brand engagement.

Data Collection and Sample Characteristics

To address our research questions, we gathered data through a survey from individuals in India who are active social media users. We used a convenience sampling approach to reach out to the target population through social media platforms such as WhatsApp, LinkedIn, Instagram and e-mails, asking them to share their perspectives. The survey was done from June to October 2024 to collect the data with the help of a structured self-administered questionnaire (Malhotra, 2004). The questionnaire was divided into two parts: the first being the data about the demographics of the respondents, while the second part addressed the variables of the research model (Table 1), which were measured through a 5-point Likert scale, where 1 represented strongly disagree and 5 represented strongly agree. The power estimation technique was employed to determine the sample size. G* Power software was used for calculating the required sample size. With an effect size of 0.15, a power value of 0.95 and a maximum of 8 predictors, the required sample size was calculated as 74. A total of 154 responses were collected, of which only 137 were selected for final analysis after eliminating incomplete responses, missing values and outliers, and the given sample size is more than the required sample size. These 137 responses were used for final analysis through partial least squares-structural modelling (PLS-SEM) in SmartPLS. Table 1 presents the descriptive characteristics of the sample.

Measures

The questionnaire’s elements were modified from previously validated scales and adapted for the context of content marketing on online platforms (websites & social media). Brand storytelling has been measured with 5 items adapted from Yu Xie (2021); collaboration with four items adapted from Nataly Levesque and Frank Pons (2020); UGC with three items adapted from Owusu et al. (2016), Nawras et al. (2021) and Dianat et al. (2019); websites with three items adapted from Wang and Tang (2003), Abelse (1998) and Zviran et al. (2006)); social media with two items adapted from Chatterjee and Kar (2020); brand image with three items adapted from Aaker (1996) and Martinez and Chernatony (2004); and engagement with two items adapted from Hollebeek et al. (2014).

Figure 1. Conceptual Research Model.

Measurement Model Assessment

Measurement model assessment measures the reliability, convergent validity and discriminant validity of the model’s constructs. Therefore, the first step evaluated the outer loadings of each item in the reflective measurement model, as can be seen in Figure 2. After removing UGC1, BS 6, SM 3 and ENG 3 one by one, respectively, whose outer loadings were less than 0.708, all the other items were retained, except BS2, BS3, C1 and ENG 2, but as can be seen in Table 3, all the constructs had AVE more than 0.50; therefore, all the indicators were retained (Hair et al., 2020). Thus, indicator reliability was established.

Figure 2. Outer Loading.

The next step assessed the internal consistency reliability of each construct through composite reliability values. Table 2 reflects that all the constructs have composite reliability values between 0.70 and 0.90, which is evaluated as ‘satisfactory to good’ as per (Hair et al., 2019) guidelines, except for engagement; however, since its respective average variance extracted (AVE) value is more than 0.50, internal consistency reliability has been established. The third step measures each construct’s convergent validity through AVE, which must be at least 0.50 and above for acceptance (Hair et al., 2019). As can be seen from Table 2, all the AVE values are greater than 0.50, which explain that at least 50% or more of the variation of the items constitutes their respective construct. In the fourth step, HTMT values were calculated to assess the discriminant validity in the outer reflective model. According to Henseler et al. (2015), with a conservative threshold value of 0.85, discriminant validity is established. As can be seen in Table 3, all values lie within the threshold except for cross-loadings of the following constructs: ‘engagement and brand image’, ‘social media and brand image’, ‘social media and collaboration’, ‘social media and engagement’, ‘user-generated content and social media’, ‘website and engagement’ and ‘website and social media’, which are greater than 0.90; hence 10,000 samples bootstrapping was performed to check if the sample mean lies within the 97.5% confidence interval and zero does not lie in between the confidence interval values of 2.5% and 97.5%. As can be seen from Table 4, the sample mean of all the above-mentioned constructs lies between the values of the confidence intervals with no zero in between, as all the values are positive, thus satisfying the requirements for the discriminant validity for the model. Hence, after conducting a confirmatory factor analysis, the results established that the model is fit for conducting structural model assessments.

Structural Equation Model Assessment

Through the establishment of validity and reliability of elements of the measurement model, the model becomes fit for structural model assessment for establishing relationships amongst the variables. Therefore, to proceed further, the issue of multicollinearity was addressed, where VIF values of the inner model have to be less than 3.33, and Table 5 reflects that all the values are less than 3, thus establishing that there is no issue of multicollinearity (Henseler et al., 2014). Next, to assess the inter-construct relationships, bootstrapping with 10,000 resamples was performed (Hair et al., 2019). Table 5 explains the significance of the path coefficient and effect size (F2), while Table 6 explains the coefficient of determination (R2). The results indicate acceptance of all hypotheses as they were found to be positively significant, except for H₃, H₈ and H₁₀, as the value 0 appears between the confidence intervals. Additionally, Table 6 explains the coefficient of correlation of all the dependent variables, that is, R2 reflects on the predictive power of the four endogenous variables, that is, brand image, engagement, social media and websites, where brand image explains 30% of the variance, engagement only 23.6%, dominant 42.8% by social media and 27.39% by websites is explained. As per Cohen (1988), these values are substantial. Also, since the SRMR value is less than 0.08, the fitness of the model has been established.