Measuring consumer perceptions towards S-commerce: scale development and validation Open Access

The development of Web 3.0 has enabled retailers to leverage social media platforms to promote their business to consumers (Esmaeili and Hashemi, 2019; Zaidan et al., 2022; Thaichon, 2017; Chakrabortty, 2018). Social media is an emerging trend used for socialization and information transmission (Stephen and Toubia, 2010; Lim et al., 2020). By adopting the features of Web 3.0, e-commerce is located under a new evolution called social commerce (S-commerce) (Esmaeili and Hashemi, 2019). S-commerce was first introduced in 2005 by Yahoo (Wang et al., 2022). It became an effective tool for engaging people in the marketing and selling of products and services in online marketplaces and communities (Stephen and Toubia, 2010). S-commerce is a subset of e-commerce that carries all the features imbibed in electronic commerce (e-commerce) along with consumers' social interactions.

Unlike e-commerce, where consumers interact with online shopping sites for browsing and purchasing, S-commerce involves online communities that initiate user interactions and user-generated content (Zhang and Benyoucef, 2016). This infers that the consumer’s purchase behavior generally relies on the information shared publicly through social media platforms (Pacheco and Jaipaul-O’Garro, 2020). The rapid development of social media has provided an enormous potential to transform e-commerce from a product-oriented environment to a social and customer-centered one (Wigand et al., 2008). The research also indicates that customers’ social interaction in online brand communities about their favorite brands boosts relationship quality and helps increase customer brand loyalty (Esmaeili and Hashemi, 2019). Most young consumers rely on friends and close acquaintances' comments and feedback on social networking sites before choosing any product or service (Thaichon, 2017).

Though S-commerce websites are designed to work collaboratively and participatively in promoting new business models, there is a lack of a comprehensive framework to shape S-commerce from both business and information technology (IT) perspectives (Baghdadi, 2016). In addition, the need to understand consumer behavior in S-commerce has become critical for companies (Zhang and Benyoucef, 2016). Ultimately, the success of S-commerce platforms depends on the readiness of the consumers to invest their time, attention and money (Wang and Huang, 2023) and their trust in S-commerce (Wang et al., 2022).

Therefore, various studies focused on the factors affecting consumer behavior, but the studies have mainly been done from the perspectives of IT and social ties only. However, various factors remain untouched and need to be explored. Therefore, the present study attempted to fill this gap by designing a validated scale that measures the perceptions of consumers towards S-commerce platforms.

S-commerce has emerged as a dynamic and influential platform, blending e-commerce with social media interactions. As consumers increasingly turn to S-commerce sites for their shopping needs, it is essential to understand the factors that shape their attitudes and intentions. This paper reviews various research studies that explore the multifaceted influences on consumer behavior in S-commerce.

Um (2019) delved into the technology acceptance perspective and identified key determinants affecting consumer attitudes towards S-commerce sites. The study found that perceived usefulness, perceived ease of use, perceived shopping enjoyment and perceived shopping risk significantly impact consumers' attitudes. These factors underscore the importance of user experience and risk perception in shaping consumer perceptions of S-commerce.

Maia et al. (2018) and Bianchi et al. (2017) emphasized trust, information quality and perceived usefulness of the websites as pivotal factors for consumer participation in S-commerce. These studies underlined the significance of trust and information quality in driving consumer engagement.

Abed et al. (2015) explored the Unified Theory of Acceptance and Use of Technology 2 model and identified trust, consumer innovativeness and information quality as important factors influencing S-commerce adoption. This model underscores the relevance of consumer trust and innovation in S-commerce.

The technology acceptance model proposed by Davis is widely used in adapting S-commerce platforms. Abed (2020) extended this perspective, highlighting the perceived usefulness of the site as a significant factor influencing small and medium-sized enterprises' intention to use S-commerce. The technology-organization-environment (TOE) framework also highlights the perceived usefulness’s role in shaping SMEs' behavioral intentions.

Studies by Kim and Park (2013) and Meilatinova (2021) emphasized that consumers' trust in S-commerce is mainly influenced by reputation, size, information quality, transaction safety, communication, economic feasibility and word-of-mouth referrals. Dabbous et al. (2020) underlined the mediating role of trust in the relationships between consumer engagement, brand awareness and intention to purchase through S-commerce. Trust towards members of the S-commerce community also played a significant role, highlighting the concept of trust transfer theory. This theory suggests that trust towards the website/server can be developed through interpersonal trust.

In addition to technical factors, studies by Pacheco and Jaipaul-O’Garro (2020) and Bianchi et al. (2017) accentuated the significance of factors such as the source and type of content, message credibility, posting frequency and online social interaction propensity in shaping perceived social presence. Perceived social presence, in turn, influences consumer attitudes towards S-commerce sites.

Chen and Shen (2015) found that emotional and informational social support significantly influences consumers' trust and community commitment, impacting their social shopping and sharing intentions.

Alotaibi and Aloud (2023) emphasized perceived usefulness, perceived behavioral control and subjective norms as significant psychological factors influencing consumer behavior. Psychological determinants, price evaluation decision mechanisms and perceived risk types also shape user intentions to adopt S-commerce (Lăzăroiu et al., 2020).

Socialization motives utilitarian and hedonic shopping motives, as noted by Aydın (2019), influence S-commerce adoption intentions. Riaz et al. (2021) found that S-commerce constructs, such as learning from forums, ratings, reviews and social advertisements, significantly predict social support constructs, ultimately contributing to consumers' purchase intentions on social networking sites.

In summary, research in S-commerce highlights the interplay of social theories and technological factors in shaping consumer attitudes and intentions. Existing literature has primarily focused on the effectiveness of S-commerce sites concerning technology factors. However, few studies have explored consumer behavior in utilizing S-commerce. This study addresses this gap by developing a comprehensive scale to measure consumer perceptions of S-commerce sites, contributing to a deeper understanding of the factors driving consumer perceptions in this evolving digital landscape.

In a 2022 survey, it was found that around 58% of online shoppers worldwide, aged between 26 and 41 years, had already engaged in shopping directly through social networks. This indicates that millennials exhibit the highest level of adoption when it comes to social shopping. Following closely behind, users aged 18–25 also displayed significant interest, with more than half stating that they have purchased through these platforms (Statista, 2022). Based on these reports, the study sample includes respondents who mainly fall in the age bracket of 18–40, i.e. GenZ and millennials.

Further, Tandon (2021) reported that the number of native S-commerce consumers in India may reach 228 million by the end of 2022. This represented a significant 45% increase from the existing user base. Shoppers in India are exploring alternative online purchasing methods, such as utilizing platforms like YouTube, WhatsApp, Facebook and Instagram, to buy goods, contributing to this growth. Thus, the data were collected from 214 respondents across India who are using S-Commerce platforms.

Further, the scale was developed by following the steps recommended by Churchill (1979). These steps include (a) generation of items and pilot testing, (b) collection of data, (c) scale refinement using exploratory factor analysis (EFA), (d) scale refinement using confirmatory factor analysis and (e) testing of scale’s reliability and validity.

Based on the literature review, an initial pool of 29 items that may relate to consumers' perceptions and behavior towards S-commerce was first developed. A set of items adopted from past studies and self-generated by the researchers is shown in Table 1.

The snowball sampling method was used to collect responses from people with experience on S-commerce platforms, including WhatsApp, Facebook and Instagram. The online questionnaire was administered to collect data from respondents to study their perceptions towards S-commerce platforms.

Consumers were requested to share their agreement or disagreement with 24 statements about their S-commerce experience. To identify the dimensions, the responses of 214 respondents to these 24 statements were subjected to EFA. While cleaning the data, three duplicate cases were deleted. Therefore, EFA was run on 211 respondents.

Overall, the Kaiser–Meyer–Olkin measure of sampling adequacy was found to be 0.760, and Bartlett’s test of sphericity was also significant (Chi-square = 1540.297, df = 276 and significance = 0.000), indicating the suitability of data for factor analysis (Table 2). All of these examinations revealed that the data were fit for analysis. Principal component analysis was employed to extract factors. The number of factors to be extracted was finalized based on factors with an Eigen value greater than 1 and were selected. Eight factors have been extracted, and together, they account for 62.347% of the variance.

All the eight factors extracted through EFA have been given names based on the variables included in each factor (Table 3).

The success of S-commerce is hinged on the satisfaction and advocacy of the customers. The result shows that satisfied customers are more likely to be advocates. Positive user experiences, product quality and adequate customer support lead to satisfaction. Word-of-mouth recommendations, user-generated content and social media engagement are some forms of advocacy. Satisfied customers tend to leave good feedback and make follow-up purchases, while influencers can enhance their advocacy. Satisfaction works hand-in-hand with advocacy; thus, pleased clients will promote brands in their networks through social media platforms, thus strengthening the prospects’ business in S-commerce.

The result indicates that the S-commerce platforms are highly usable and enjoyable by users. They regarded incorporating social media platforms into the shopping experience as smooth and relaxed, making it easy to discover new products and buy them. These interactive features include user-generated reviews and recommendations, which inform their judgment and make their shopping more sociable. In summary, the study concludes that S-commerce strikes a balance between its use and entertainment, thereby becoming an attractive option among modern buyers.

The findings indicate that trust and familiarity are crucial in determining user engagement and satisfaction. The respondents had greater confidence in purchasing from S-commerce platforms they were already familiar with, like popular social networking sites. Their willingness to make purchases was positively affected by feeling secure about transacting within a known online environment. Similarly, having positive encounters with user-generated content, such as reviews or recommendations from friends/followers, built the trustworthiness of the S-commerce platform. Thus, these findings show how much trust combined with familiarity determines people’s attitudes towards things they rely on when making decisions about buying new stuff online.

The findings show the importance of trust in S-commerce policies. The respondents were more likely to engage in transactions when S-commerce platforms had transparent and comprehensive policies (especially concerning data privacy, payment security or return procedures). A transparent and trustworthy privacy policy gives users confidence in handling their personal information. Moreover, visible and fair return and refund policies played a crucial role in enhancing trust, as users trusted that their money was safe during transactions. These results emphasize the critical role of well-defined and transparent policies for building and maintaining trust within the S-commerce domain.

The result indicates that those users who had already used popular social networking platforms were more comfortable and willing to use S-commerce functionalities. These participants, already familiar with platforms like Facebook or Instagram, found navigating through and accessing shopping features directly embedded on these sites easily. This kind of knowledge in advance shortened the learning curve and built clients’ trust towards purchasing through S-commerce. These findings highlight the importance of exploiting people’s comfort levels while using social media platforms and how this can be leveraged to boost adoption and engagement with S-commerce features.

The findings of the study indicate that S-commerce relies heavily on financial trust. The respondents highly preferred S-commerce platforms with robust payment security features such as encrypted transactions and secure payment gateways. The perception of financial safety significantly affected users’ intention to share payment information and make purchases, although sometimes it also indicated a greater willingness to take risks. Additionally, the platforms that give transparent and simple details regarding their security protocols attract more trust from users. Finally, these results emphasize the need to prioritize trust in finance to ensure security procedures within S-commerce platforms to create user confidence and sustain growth in this area of e-commerce.

It was found that the behavior of S-commerce users is highly influenced by price and ratings. The respondents consistently acknowledged that cost was a vital factor where better pricing often translated to higher conversion rates. Moreover, user-generated ratings are central to decision-making. Higher-rated products and those with positive reviews were preferred because they seemed trustworthy and reliable. These results demonstrate the importance of being competitive in pricing and the power of social proof when considering options for S-commerce shoppers; hence, businesses should maintain competitive prices and advocate for positive customer feedback to succeed in this field.

The findings imply that subjective value and social signals shape users’ behavior on e-commerce platforms. To them, price, quality and brand reputation indicate a product’s perceived value. Besides, their purchase decisions were influenced by peer pressure, both directly from their friends or indirectly, through social media contacts. When people claim to have seen their friends endorsing a particular brand or product, they buy it due to the perceptional aspects associated with it being socially valuable.

After exploring the factors, the next step is to confirm the factors and validate the scale. All the fit indices are calculated to analyze the model’s fitness to data. The indices' values have been found in the acceptable range depicted in Table 4.

Table 5 presents the results of various reliability and validity measures for the constructs under study. The composite reliability (CR) values for each construct exceeded the recommended threshold of 0.7 as Malhotra and Dash (2010) suggested, indicating that the scale is reliable.

To assess the convergent validity of the scale, which evaluates how well the observed variables within a given construct align with each other (as proposed by Casalo et al., 2008; Malhotra and Dash, 2010), we used two key indicators: average variance extracted (AVE) and Composite Reliability (CR). In Table 5, it can be observed that all AVE values exceed the acceptable threshold of 0.5. Furthermore, the CR values for each construct are higher than their respective AVE values, which is also a positive outcome, as demonstrated in the above table.

Moreover, establishing discriminant validity is crucial to ensure that each construct is distinct from the others (as emphasized by Malhotra and Dash, 2010; Casalo et al., 2008). This is evaluated based on two conditions: maximum shared variance over AVE (MSV/AVE) and average shared variance AVE (ASV/AVE). These conditions have been met satisfactorily, indicating that each construct is distinct from the others.

In summary, the results presented in Table 5 confirm the reliability of the scale, its convergent validity and its discriminant validity, thereby demonstrating the effectiveness of the measurement instrument in capturing the intended constructs.

The world of social media has rendered an incredible revolution to foster commercial activities in the modern age. Web 3.0 has increased communication between consumers with new channels such as blogs, social networks, social media and communities and new channels for firms to contact customers. S-commerce is also an emerging and fast-growing trend.

In this study, an attempt has been made to explore and confirm the factors affecting the perceptions of consumers towards S-commerce platforms. Eight factors were explored using EFA, namely, “satisfaction and advocacy of S-commerce,” “usability and enjoyment of S-commerce,” “trust and familiarity in S-commerce,” “trust in S-commerce policies,” “familiarity with S-commerce platforms,” “financial trust and security in S-commerce,” “price and rating influence on S-commerce shopping” and “perceived value and social expectations in S-commerce.” The study’s primary purpose is to develop a scale measuring consumers' perceptions to ensure the validity of the scale confirmatory factor analysis. Then, the reliability and validity of the developed scale were ensured.

The research offers an in-depth understanding of how consumers perceive S-Commerce platforms. It also helps in the identification of factors that affect consumer perceptions, as well as explains some dimensions that define those perceptions. These results will enable marketers, companies and researchers to develop winning strategies and enhance customer experiences within S-commerce.

This current study primarily builds and validates a scale to examine consumer perceptions of S-commerce. This can be a critical tool for future studies in this industry, enabling researchers to assess consumer viewpoints quantitatively and consistently across different studies. Moreover, businesses and marketers can use this confirmed scale to gauge customer attitudes towards the S-commerce context.

Additionally, the findings could help organizations fine-tune their marketing approach for S-commerce sites. By comprehending what determines consumer perception or behavior, companies might tailor their communications messages, product offerings and user experiences to meet customers’ needs. Consequently, clients may be more satisfied, leading to increased trust, which ultimately results in higher conversion rates and increased revenues for companies hiring S-commerce platforms such as Amazon.com.

This study can help S-commerce platform creators design user-friendly interfaces and seamless experiences. Designers will need to prioritize those elements influencing consumers’ perceptions favorably. This may enhance customer engagement and trust, enhancing the chances of repeat purchases.

In addition, the validated scale can be used to facilitate relative analysis between different S-commerce platforms. Researchers or practitioners may use this scale to evaluate and compare consumers’ perceptions of various platforms, thereby identifying each platform’s strong and weak points. Such data are helpful for companies looking forward to benchmarking performance against competitors and finding improvement opportunities. Policymakers and regulatory bodies can also benefit from these results because they highlight consumer perceptions regarding S-commerce. Policymakers who understand what affects consumer perception will develop appropriate regulations that protect customer interests and guarantee fair practices.

The implications of this study on understanding consumer behavior towards S-commerce include developing a validated scale for measuring its customers' perceived values, which could act as an important benchmark in academia and the industry at large. This helps in understanding how S-commerce affects consumer behavior. It provides inputs valuable for business houses and market.