Impact of social support on social media purchase behavior: the mediating role of customer rating and review quality Open Access

Social commerce, rooted in social media, empowers consumers to interact, share information and make informed purchasing decisions. Businesses utilize social media tools to engage with customers, enhance relationships and drive sales (Hajli, 2015). Research on social commerce dates back to the late 1990s but experienced significant growth from 2004 onward, coinciding with the emergence of platforms like Facebook (Liang & Turban, 2011). Consumers’ shared information, including ratings, reviews, recommendations and referrals, significantly influences their engagement in social commerce (Lin, Li, & Wang, 2017).

Over the past decade, online social media usage has experienced substantial growth, with platforms such as Facebook, Twitter, Instagram and TikTok collectively engaging billions of active users worldwide (Van Bavel, Robertson, Del Rosario, Rasmussen, & Rathje, 2023; Statista, 2021). Online social media empowers consumers to interact, share information and make informed purchasing decisions (Lin et al., 2017; Kaplan & Haenlein, 2010). Social media influencers are becoming more popular and are influencing how people think, feel and make decisions (Joshi, Lim, Jagani, & Kumar, 2023; Lou & Yuan, 2018). This integration can impact their followers’ behavior, including their attitudes, perceptions, preferences, choices and decisions (Joshi et al., 2023; Cheung, Sia, & Kuan, 2012).

Social support, which includes both informational support (IS) and emotional support (ES), plays a significant role in influencing purchase behavior in social commerce. IS refers to the provision of advice, suggestions and information that can help consumers make informed purchasing decisions (Hu, Chen, & Davison, 2019). ES, on the other hand, involves expressions of empathy, care and concern, which can enhance consumers’ confidence and reduce their perceived risk in online transactions (Chen & Shen, 2015). Additionally, social support theory (SST) posits that social support can buffer the negative effects of stress and uncertainty in online shopping environments (Cobb, Ross, & Ross, 1976). Empirical research indicates that both informational and emotional sentiments, present in social circles, significantly enhance consumers’ engagement and satisfaction in social commerce (Zhou, 2011). Furthermore, the credibility and trustworthiness of the source providing social support are crucial in shaping consumers’ attitudes and behaviors (McKnight, Choudhury, & Kacmar, 2002). Overall, the integration of social support mechanisms in social commerce platforms can lead to higher levels of consumer trust, satisfaction and purchase behavior (Hajli, 2014).

IS plays a crucial role in shaping consumer purchase behavior (CPB), particularly in the context of social commerce. The availability and quality of information can significantly influence a consumer’s decision-making process, from initial interest to final purchase (Smith, 2020). According to the five-stage model of the buying decision process, consumers typically go through problem recognition, information search, evaluation of alternatives, purchase decision and post-purchase behavior (Kotler, Keller, Brady, Goodman, & Hansen, 2016). IS finds its criticality particularly during the information search and evaluation stages, where consumers seek reliable data to make informed choices (Brown, Broderick, & Lee, 2007). Recent research has also explored the impact of cognitive biases on information processing and purchase behavior. For instance, the availability heuristic suggests that consumers are more likely to rely on readily available information, which underscores the importance of prominent and easily accessible IS (Tversky & Kahneman, 1973). Additionally, the social proof phenomenon indicates that consumers tend to follow the behavior of others, making user-generated content and reviews critical components of IS in social commerce (Cialdini, 2007).

ES means receiving care, understanding and empathy from a social group. When someone faces challenges, they need both practical help and emotional encouragement that shows they are cared for, which can help solve the problem (Liang & Turban, 2011). By providing ES to another person, you offer them encouragement and caring, making them feel valued and important (Burleson, 2003). Research has shown that ES can significantly enhance customer loyalty (Yu & Dean, 2001). In the context of online shopping, ES from peers can play a crucial role in influencing consumer behavior. For instance, ES can help reduce the stress and uncertainty associated with online purchases (Makmor, Alam, & Aziz, 2018). Additionally, the quality of customer ratings and reviews can mediate the relationship between ES and consumer behavior, as positive reviews can reinforce the ES received from peers (Brown et al., 2007).

Customer reviews, recommendations and experiences shared by current consumers provide authentic insights into products or services, significantly influencing how others perceive them (Tuncer & Kartal, 2024). Authentic customer reviews are crucial, as they offer genuine feedback, free from marketing bias and help potential buyers make informed decisions (Ghelber, 2021). Research indicates that the volume of online reviews has surged, reflecting the growing comfort of consumers with sharing their experiences online (Gartner, 2020). This increase in user-generated content, such as reviews and social media posts, provides valuable insights into customer satisfaction and product performance (Staircase AI, 2024). Moreover, the credibility of these reviews plays a pivotal role in shaping consumer trust and purchase behavior. Negative reviews, although potentially damaging, can also offer opportunities for businesses to address and resolve customer issues, thereby enhancing overall customer satisfaction (Trustpilot, 2023). The impact of online reviews on purchasing decisions is profound, with studies showing that authentic reviews can significantly influence consumer perceptions and buying choices (Ghelber, 2021).

Despite the growing recognition of the importance of social support, online reviews and social influence in shaping consumer purchasing behavior, several notable research gaps persist (Hu et al., 2019). Most existing studies, including Taylor, Fradgley, Clinton-McHarg, Hall, and Paul (2023), Islam, Mäntymäki, Laato, and Turel (2021) and Shensa et al. (2019), have primarily focused on the individual effects of informational or emotional support separately, without thoroughly examining how these two forms of social support interact or synergize within social media environments to influence consumer decisions (Fang, Wang, Wen, & Zhou, 2020). Furthermore, while the significance of customer ratings and review quality (CRRQ) has been acknowledged, there is limited understanding of how these factors mediate the relationship between social support and purchase behavior, especially in the context of social commerce platforms characterized by high user engagement and interactivity.

Studies have shown that social influence significantly affects consumer decisions in physical retail environments (Makmor et al., 2018). Additionally, the role of peer recommendations and reviews has been well-documented in influencing offline purchasing behavior (Brown et al., 2007). Though previous studies have also suggested the influence of CRRQ in shaping online purchase decisions, yet the specific mechanisms remain unclear (Wang, Guo, Wu, & Liu, 2019; Chen et al., 2017). Hence, the current study postulates mediation of CRRQ while investigating the role of social support in online CPB. Research has also indicated that ES from peers can enhance loyalty in traditional shopping settings (Purani, Kumar, & Sahadev, 2019). IS, such as product recommendations and advice, has been found to be crucial in offline purchase decisions (Goodrich & De Mooij, 2013). Even with these findings, there is a notable gap in understanding how these supports translate to online environments (Lăzăroiu, Neguriţă, Grecu, Grecu, & Mitran, 2020). Furthermore, the interplay between IS and ES in online purchases needs further investigation (Popescu & Ciurlău, 2019; Margalit, 2015). Keeping the above discussion in mind, the current study formulates the following objectives:

1. To examine the impact of social support (i.e. informational and emotional) on customer rating and review quality

2. To analyze the impact of customer rating and review quality on social media online customer purchase behavior

3. To investigate the mediating role of customer rating and review quality between social support and social media online customer purchase behavior

This research takes the initial goal beyond its foundation through the integration of a bi-theoretical model using elaboration likelihood model (ELM) and social support theory (SST). As according to Friedman (1983), “In an uncertain situation, where there is no single overreaching theory to explore the phenomena inclusively, the gleaning insights can be drawn from range of multiple theoretical lenses.” Therefore, we assume that the primary theoretical contribution of this research lies in demonstrating how social support mechanisms, rooted in SST, interact with cognitive processing routes outlined by ELM to shape consumer decision-making. Specifically, by incorporating constructs such as IS, ES and CRRQ, this study offers a comprehensive framework that bridges psychological theories with digital marketing practices, enriching the academic discourse on online consumer behavior. These theoretical contributions are valuable because they not only deepen conceptual clarity around the roles of IS and ES in digital environments but also pave the way for developing more effective social commerce strategies. Overall, this research fills existing gaps by empirically validating theoretical models within the context of social media-based commerce, thereby expanding scholarly understanding of the dynamic interplay between social influence, cognitive processing and consumer purchase behavior in the digital era.

Basically social support is a construct that has been derived from social support theory (SST) (Crocker & Canevello, 2008; Lakey & Cohen, 2000) and refers to a psychological state that derives physical assistance from online groups, individuals or communities (Hu et al., 2019). SST illustrates the impact of supportive interactions on consumer behavior, emphasizing that such interactions can reduce stress and enhance well-being, which are crucial in shaping consumer attitudes and behaviors (Cobb et al., 1976). Such social supports are centered to be the main hallmarks that influence consumer online purchases (Riaz et al., 2020). That is why recent global reports and surveys have also highlighted the importance of social support in influencing online purchasing behavior. For instance, a Nielsen report (2023) indicates that consumers increasingly rely on social media for product recommendations and ES, which significantly impact their purchasing decisions. Similarly, a study by Ilieva et al. (2024) found that social media influencers, who provide both informational and ES, greatly affect customer attitudes and purchase intentions.

However, in the current study social support has been conceptualized as a multifaceted construct that includes two type of support, i.e. IS and ES. According to Chen et al. (2017), supports that can be either emotional or informational or both influence individuals to reuse the system or to try a newer one. On one side, IS has been likely found to enhance the perceived value of customer reviews and ratings (Riaz et al., 2020). It includes provisions of advice, guidance and informational that help people to cope with their challenges by making more informed decisions (Hajli, 2015). A meta-analysis by Ao et al. (2023) stated that more and more detailed and informative reviews are crucial for customers making informed purchasing decisions. While on the other end, ES fosters a sense of community and trust among consumers. Autio (2020) emphasizes that positive emotional interactions, such as supportive comments and endorsements, increase the credibility of reviews and ratings, thereby influencing purchasing behavior. Further, a study by Sin, Nor, and Al-Agaga (2012) found that consumers heavily rely on social media for both IS and ES when making online purchases.

In addition to SST, this study also explores the role of ELM while explaining CPB. Coined by Petty and Cacioppo (1986) ELM has been widely accepted as an inclusive theoretical foundation to explain socio-psychological and marketing behaviors (Li, 2012). According to them, the primary goal of this model is to account for various elements of a message, considering that individuals have different moods, abilities and motivations. Often, people may not fully perceive or thoroughly evaluate all the contents of a message before making a decision (Shahab, Ghazali, & Mohtar, 2021).

By concentrating on the influence process and categorizing influence mechanisms into central and peripheral routes, ELM can explain individuals’ responses to online content (Fan, Peng, Chen, & Cong, 2024). In the perspective of online purchase behavior, ELM establishes distinguishing between these two routes of persuasion, i.e. central and peripheral (Teng, Khong, & Goh, 2014). The central route processing highlights that the consumers who are highly motivated and capable of analyzing information are deeply engaged with product details, reviews and comparisons, leading to informed decisions based on quality and credibility (Shi, Hu, Lai, & Chen, 2018). Conversely, peripheral route processing involves consumers who rely on superficial cues like attractive visuals, popular endorsements and emotional appeals due to lower motivation or capacity for detailed processing (Teng et al., 2014). These routes highlight the importance of tailoring marketing strategies to either provide rich, informative content or leverage engaging, visually appealing messages to influence consumer purchase decisions on social media platforms.

Customer ratings and review quality play a crucial role in shaping consumer purchasing decisions on online social media platforms. In the digital marketplace, where direct product interaction is limited, potential buyers rely heavily on the experiences and opinions of others to assess the credibility and quality of a product or service (Zhang, Zhao, Cheung, & Lee, 2014; Park, Lee, & Han, 2007). High-quality reviews provide detailed insights into product performance, usability and reliability, helping consumers make informed choices (Wang et al., 2019). Additionally, ratings serve as quick indicators of overall customer satisfaction, influencing trust and purchase intent. Studies suggest that consumers are more likely to trust peer-generated content over traditional advertising, making reviews a powerful tool for businesses to build credibility and attract new customers. Furthermore, well-articulated and authentic reviews contribute to transparency, reducing uncertainty and perceived risk associated with online shopping (Katyal & Sehgal, 2024). As social media platforms continue to integrate e-commerce features, the significance of customer ratings and reviews in shaping brand reputation and consumer behavior is further expected to grow.

Building upon the theoretical foundations and existing empirical evidence, this research posits several hypotheses to elucidate the pathways through which social support influences online social media purchase behavior, mediated by CRRQ. Social support, encompassing both informational and emotional dimensions, plays a crucial role in shaping the quality of user-generated content such as ratings and reviews. IS provides detailed, relevant information that enhances the perceived credibility and usefulness of reviews, while emotional support fosters trust and positive affect, encouraging users to share higher-quality reviews. Empirical evidences (Filieri, McLeay, Tsui, & Lin, 2018; Liu & Zhang, 2010) suggests that when consumers receive relevant product information from social connections, they are more likely to contribute detailed and high-quality reviews due to increased understanding and confidence. Hence, we proposed the following hypothesis:

ES enhances trust and emotional engagement (Rathore & Ilavarasan, 2020), creating a sense of belonging and psychological safety that encourages consumers to share more genuine, heartfelt reviews. This emotional authenticity not only strengthens the credibility of the reviews but also fosters a deeper emotional connection between consumers and the brand or community. As a result, consumers are motivated to provide detailed, emotionally rich reviews that resonate with other potential buyers, thereby amplifying the influence of reviews on purchase decisions (Wang et al., 2019; Guo, Wang, & Wu, 2019). Such authentic, emotionally expressive feedback can evoke empathy and trust among prospective customers, making them more confident and motivated to convert into buyers. Therefore we propose:

In this study CRRQ serves as a mediator while investigating the relationship between social support and online purchase behavior. High-quality reviews and positive ratings can significantly amplify the effects of social support by providing detailed information (Wang et al., 2019; Liang & Turban, 2011). This is supported by research indicating that authentic and high-quality reviews positively affect trust in rating systems, which in turn impacts customer experience and purchase behavior (Karunasingha & Abeysekera, 2022; Peña-García, Losada-Otálora, Auza, & Cruz, 2024). Contrariwise, poor-quality reviews can emasculate the positive impression of social support by familiarizing doubts and uncertainties (Boon-Long & Wongsurawat, 2015). The observation of false or low-quality reviews disturbs customer trust and experience in rating systems, foremost leading to decreased purchase intentions and overall satisfaction (Kutabish, Soares, & Casais, 2023). Therefore, the above literature unfolds the significance of review quality in mediating the relationship between social support and purchase behavior, on the basis of which we propose that:

On the basis of formulated hypotheses, the current study suggests the PLS-SEM-based theoretical framework as shown in Figure 1.

This study adopts a positivist philosophy and a deductive approach to investigate the impact of IS and ES on online social media purchasing behavior, with a focus on the mediating role of customer rating and review quality. Data were collected using a survey questionnaire with closed-ended questions rated on a five-point Likert scale (From 1 = Strongly Disagree to 5 = Strongly Agree). A purposive sampling method was employed, targeting respondents with relevant characteristics to ensure homogeneity (Andrade, 2020). The unit of analysis includes individuals with prior e-commerce experience in Pakistan, selected through non-probability sampling. The survey forms were distributed via Google Forms between March 5 and April 15, 2024, targeting individuals in Pakistan with prior e-commerce experience, specifically in major cities of South Punjab. To exceed the minimum requirement of 200 samples, over 250 survey questionnaires were distributed through online social media. The study received more than 200 valid responses, achieving an 84% response rate. The validity of responses was judged on the basis of appropriateness and correct filling of the questionnaire.

The survey instrument was designed to measure the impact of IS and ES on online social media CPB, with a focus on the mediating role of CRRQ. The questionnaire is divided into two sections: demographics and study variables. The first section collects basic demographic data to understand the background of the respondents. While, in the second section, all the constructs were accessed through multi-item scales, either adopted or adapted from previous studies (see Table 1). IS (three items) and ES (three items) and customer rating and review quality (nine items) were adapted from Lin, Hirschfeld, and Margraf (2019). Online social media purchasing behavior (five items) was measured by using a combination of items from the existence research: the first four items from Erkan and Evans (2016) and the last from Brady and Cronin (2001).

The data analysis of research on the impact of IS and ES on online social media purchase behavior, focusing on the mediating role of customer rating and review quality, the process begins with data preparation, including data cleaning and coding. Descriptive statistics are calculated to understand sample characteristics. The measurement model is assessed for reliability and validity using Cronbach’s alpha, composite reliability (CR) and average variance extracted (AVE). Discriminant validity is confirmed using the Fornell-Larcker criterion. The structural model is evaluated using partial least squares structural equation modeling (PLS-SEM) and bootstrapping to determine the significance of path coefficients (Hair, Risher, Sarstedt, & Ringle, 2019). Hypothesis testing evaluates the direct and indirect impacts of IS and ES on online social media purchase behavior. Finally, results are reported, including descriptive statistics, reliability and validity results, path coefficients, significance levels, indirect effects and hypothesis testing outcomes. This comprehensive analysis ensures a thorough understanding of the research topic.

Demographic analysis results reveal that the respondent pool is predominantly male (59.4%) and female (40.06%), with 61.3% of participants aged 18–25, while 31.1% fall within the 26–35 age range, indicating a significant presence of relatively young adults; only 5.7% are aged 36–45, and a mere 1.9% are 46 or above (see Table 2). Additionally, a substantial majority (71.2%) of respondents are single, with almost 28% married, while only 0.9% are divorced, and there are no widows in the sample. Linguistically, 57.7% of respondents prefer Urdu, establishing it as the dominant language, followed by 13.7% for English, 13.2% for Saraiki and 11.8% for Punjabi, together representing about 25% of language preferences; a small fraction (3.8%) prefer other languages.

In modern research methodologies, PLS-SEM is widely used for analyzing complex relationships between variables. PLS-SEM is particularly effective in predictive modeling and theory building when data are non-normally distributed (Sarstedt et al., 2022). In this analysis, the PLS-SEM approach was used to assess the relationships between various constructs, such as customer support and online purchase behavior (Henseler et al., 2015). The software generated key outputs, including reliability, validity tests and hypothesis testing, all of which are crucial for validating the theoretical framework of this study (Chin, 1998).

The constructs show good reliability and validity across various measures. Variance inflation factor (VIF) values are all below 3, indicating the absence of multicollinearity. Cronbach's alpha values range from 0.771 to 0.890, rho_A values from 0.775 to 0.894 and CR values from 0.868 to 0.910, all indicating high reliability. AVE values range from 0.531 to 0.708, showing good convergent validity. Most factor loadings are above 0.7, indicating good indicator reliability. Specifically, customer rating and review quality has a CR of 0.910, AVE of 0.531, and mostly high factor loadings, while ES has a CR of 0.868, AVE of 0.687 and all factor loadings above 0.7 (see Table 3).

The Fornell–Larcker criterion is used to assess discriminant validity by comparing the square root of the AVE of each construct with the correlations between constructs. In Table 4, the diagonal values (0.729, 0.829, 0.842 and 0.782) represent the square roots of the AVE for each construct, i.e. CRRQ, ES, IS and CPB, respectively. Hence, the results in Table 4 show that all values are below 0.90, indicating good discriminant validity among the constructs CRRQ, ES, IS and CPB. This means each construct is distinct and measures different concepts effectively.

Further, Table 5 highlights the heterotrait-monotrait ratio (HTMT) between four main constructs used in this research, coefficients show the degree and direction of these variable’s correlation. According to the results, CRRQ and ES correlate at 0.833. This substantial positive association suggests that emotional assistance is linked to better ratings and review. Secondly, the correlation between CRRQ and IS sustains at 0.687, emphasizing that the accurate, complete and relevant IS helps to make confident purchases and leave insightful evaluations. Thirdly, CRRQ and CBP are 0.655 correlated, suggesting how important customer feedback is in buying decisions. Further, the existence of a strong correlation between ES and IS, i.e. 0.760, show that businesses that provide ES also deliver high-quality IS. The correlation between ES and CPB is also significant at 0.689, showing that customers feel respected and understood when given ES. Lastly, IS and CBP also correlate at 0.583.

Given in Table 6, the R² model explains 53.7% of the variance in customer rating and review CRRQ and 33.6% in CPB. Adjusted values are slightly lower, indicating a good fit with appropriate predictors. CRRQ is better explained by the model than CPB. Furthermore, Table 6 also indicates effect sizes (F²), according to which the effect of ES on CRRQ was 0.408, IS on CRRQ was 0.100, and CRRQ on CPB was 0.506. Lastly, Q² values for CRRQ (0.269) and CBP (0.198) confirm in-sample predictive relevance (Hair et al., 2019).

Furthermore, Table 7 also indicates that ES has a substantial positive effect on CRRQ, with a path coefficient of 0.541 and a T-statistic of 7.900, indicating that providing ES greatly improves the quality of CRRQ. IS also positively impacts CRRQ, but with a path coefficient of 0.267 and a T-statistic of 4.107. Hence, both emotional and IS are crucial, as they enhance the quality of customer feedback, which in turn encourages more purchase behavior. Further, the data show that better CRRQ significantly boost CPB, with a strong path coefficient of 0.580 and a T-statistic of 11.883. Table

Shown in Table 8, the impact of IS and ES on online social media purchase behavior, focusing on the mediating role of customer rating and review quality, the results indicate significant relationships. The path from IS to CPB, mediated by CRRQ, shows a beta value of 0.155, with a standard deviation of 0.042, a T statistic of 3.670 and a p-value of 0.000, confirming its statistical significance. Similarly, the path from ES to CPB, mediated by CRRQ, has a beta value of 0.314, a standard deviation of 0.046, a T-statistic of 6.801 and a p-value of 0.000, also indicating a significant relationship. Further, the structural equation modeling results are also shown in Figure 2. These findings suggest that both IS and ES positively influence purchase behavior through the mediating role of customer ratings and reviews quality, with ES having a stronger impact.

The study examined the impact of IS and ES on online social media purchase behavior, with CRRQ serving as a mediating factor. The reliability and validity of the scales used in the study are confirmed through various metrics, including Cronbach’s alpha (α), rho_A, CR and AVE, all of which demonstrate strong internal consistency and convergent validity.

The bootstrap results conducted at 10,000 subsamples displayed the outcomes of hypotheses testing, confirming significant associations among various constructs. Hypothesis H1 regarding the association between IS and CRRQ (IS → CRRQ) has a beta value of 0.267, a sample mean of 0.274 and a t-statistic of 4.107, reflecting a significant relationship. Likewise, H2 (ES → CRRQ) regarding the association between ES and CRRQ shows a beta value of 0.541 with a t-statistic of 7.900; H3 (CRRQ → CPB) regarding the association between CRRQ and CPB has a beta value of 0.580 and a t-statistic of 11.883; H4 (IS → CPB) regarding the association between IS and CPB shows a beta value of 0.155 and a t-statistic of 3.670; and H5 (ES → CPB) regarding the association between ES and CPB presents a beta value of 0.314 with a t-statistic of 6.801. All hypotheses demonstrated significant relationships, as evidenced by their p-values of 0.000, and were accepted, respectively.

Henceforth, building upon the above discussion, this study establishes that social support (IS and ES) in social commerce offers significant character in determining consumer online purchase behavior. The study also declares that CRRQ are some of the most important aspects of the online shopping concept. They serve as reference points, influencing prospective buyers. The study documents that both pools, as IS and ES, play a role in determining the quality of these ratings and reviews from the customers. This combination brings out the best of CRRQ, which in one way or the other help to direct other consumers toward making the purchase behavior. Now one can say that, apart from the provision of IS, affirmations, emotions and fellowship from friends or social icons make the reviews more valuable.

The relevance of this study takes importance from the findings of the Global Risks Report 2024 that reveals that with the advancement of technology, specifically the social media interface, it is gaining importance in determining consumer behavioral changes (World Economic Forum, 2024). While digital platforms are constantly changing, the capacity to offer useful information and emotional communication is going to be instrumental in helping organizations that are seeking to strengthen their customer interactions and advance e-commerce operations. Businesses should focus on fostering a supportive community where users can share their experiences and provide feedback. Encouraging detailed and honest reviews can amplify the positive effects of social support, making potential buyers feel more confident in their purchasing decisions. Additionally, leveraging advanced algorithms to highlight the most helpful reviews can further enhance user trust. By prioritizing both IS and ES, companies can create a more engaging and trustworthy social commerce environment, ultimately driving higher sales and customer satisfaction.

Our research uses SST and ELM to study how customers behave on digital platforms. The research demonstrates how IS and ES drive consumers to make decisions using SST. This theory implies that many people need social connections to lower their stress and strengthen their well-being, which then affects how they think and behave as consumers. When they receive sufficient detailed product IS and user feedback, they enhance their ability to make purchase decisions. When peers show emotional understanding and offer motivation to customers, this builds trust and creates a united sense of community that helps customers stay loyal to the brand. The ELM strengthens our theoretical foundation by describing how consumers think about IS on online platforms. ELM identifies two distinct routes of influence: Online consumers engage in both central processing, which analyzes IS and peripheral processing, which responds to emotional or social cues. When motivated to ship smart customers seek through product IS in ratings and reviews so they can make better choices. Consumers who avoid detailed content analysis make decisions based on their instinct about whether reviewers seem trustworthy and how reviews make them feel.

The research shows how CRRQ connects key customer behaviors elements while advancing established buying models. The study proves ratings and reviews connect social backing with customer buying behavior. Buyer purchase decisions depend heavily on both the number of reviews and how well users evaluate the product. Organizations need to invest in making good user feedback because it helps in developing better consumer choices.

Furthermore, our research advises other theoretical approaches such as the theory of planned behavior (Ajzen, 1991) and the technology acceptance model (Davis, 1985) for forthcoming scholarships, as they could benefit from better explaining online buying behavior. The research directed to incorporate attitude and social media models will further help to understand why customers make online purchases together with their experiences about choices and perceived limits. Hence, by widening this theoretical foundation, researchers can create a complete framework for analyzing modern consumer digital behavior, leading to improved understanding of e-commerce and social media interactions.

The goal of this study was to build a welcoming space to exchange scholarship that might elaborate consumer feedback for improved understanding of shopping needs. The study acknowledges that detailed and truthful customer feedback helps both support systems and helps buyers feel better about their choices. Therefore, the practical implications can be analyzed manifolds. For instance, when users receive reliable IS and ES, they are more likely to perceive the platform as trustworthy, leading to increased purchase intentions. Businesses should focus on fostering a supportive community where users can share their experiences and provide feedback. Encouraging detailed and honest reviews can amplify the positive effects of social support, making potential buyers feel more confident in their purchasing decisions. Additionally, leveraging advanced algorithms to highlight the most helpful reviews can further enhance user trust. By prioritizing both IS and ES, companies can create a more engaging and trustworthy social commerce environment, ultimately driving higher sales and customer satisfaction.

This is the explanation that people’s emotions make the influence of reviews on purchase behavior in online social media even stronger than the IS. By responding to the emotions of users, social identification supports promote the value perception of customer reviews and ratings for improving the inclination to purchase decisions more prominently (Kim & Ko, 2012). Further, high CRRQ serve as a crucial mediator, enhancing trust and credibility (Huang & Benyoucef, 2014). Businesses should focus on providing reliable IS and ES to make users perceive the platform as trustworthy, leading to increased purchase intentions (Liang, Hu, Islam, & Mubarik, 2021).

While the practical implications outlined in the study offer valuable guidance for enterprises seeking to enhance their social commerce strategies, the discussion would be significantly strengthened by providing more detailed insights into how these measures can be practically implemented. For example, enterprises could develop specific actions such as establishing dedicated community management teams (Marti, Liu, Kour, Bilgihan, & Xu, 2024; Dowsley, 2009) to foster supportive environments or leveraging advanced analytic tools to identify and highlight the most helpful reviews. Additionally, companies might implement targeted training for staff on delivering empathetic and personalized responses to consumer feedback or employ AI-driven algorithms to curate and showcase high-quality reviews effectively (Stray, 2020). Clearer guidance on integrating these tactics into existing operational workflows, resource allocation and technology systems would help organizations translate the proposed strategies into actionable steps. Supplementing the current recommendations with concrete examples, step-by-step approaches or case studies demonstrating successful implementation would further enrich the practical value of the study, enabling practitioners to better understand, adopt and adapt these measures within their unique contexts.

The results showed that continued research is necessary to adjust our models to match the speed with which social media platforms grow. Social media and e-commerce need ongoing studies helping researchers update and improve existing behavior models for better digital marketing results. Researchers and marketers will stay effective at their jobs because they can change their approach to suit fresh patterns in consumer online habits.

While this study provides valuable insights into the impact of IS and ES on online social media purchase behavior, several limitations should be acknowledged to guide future research. Firstly, the study's reliance on self-reported data may introduce biases such as social desirability bias, where respondents might overstate positive behaviors or underreport negative ones (Podsakoff et al., 2003). Future research could employ a mixed-methods approach, combining quantitative surveys with qualitative interviews to mitigate these biases and gain a deeper understanding of consumer behavior (Creswell, 2021).

Secondly, the sample used in this study was limited to a specific demographic group, which may not be representative of the broader population. Future studies should aim to include a more diverse sample to enhance the generalizability of the findings (Bryman, 2016). Additionally, longitudinal studies could provide more robust insights into how IS and ES influence purchase behavior over time (Ployhart & Vandenberg, 2009).

Thirdly, the study focused primarily on the mediating role of CRRQ. Future research could explore other potential mediators and moderators, such as trust, brand loyalty and perceived value, to provide a more comprehensive understanding of the underlying mechanisms (Baron & Kenny, 1986). Lastly, the rapidly evolving nature of social media platforms presents a challenge for maintaining the relevance of the findings. Future studies should continuously update their frameworks to account for new features and trends in social media usage (Kaplan & Haenlein, 2010).

The rapid evolution of social media platforms has significantly transformed consumer behavior, especially in online purchasing. This study explores how IS and ES influence online CPB, focusing on the mediating role of CRRQ. In the digital age, consumers are flooded with vast amounts of IS. IS, which includes the availability and accessibility of relevant product IS, plays a crucial role in shaping consumer purchase decisions. When consumers have access to comprehensive, accurate and timely IS, their confidence in making informed purchase decisions increases. The study highlights that IS indirectly influences CPB but also enhances the perceived CRRQ. High-quality IS leads to more detailed and reliable reviews, which, in turn, positively impacts other consumers’ CPB.

ES in online communities, characterized by empathy, encouragement and positive reinforcement, significantly affects consumer behavior. The sense of belonging and emotional connection fostered within social media platforms can enhance consumer trust and loyalty. The study reveals that ES indirectly influences CPB by improving the perceived CRRQ. When consumers feel emotionally supported, they are more likely to share their positive experiences and provide high-quality reviews, which serve as valuable social proof for potential buyers.

Customer ratings and reviews are critical components of the online shopping experience. They act as social proof, influencing potential buyers’ decisions. The study emphasizes that both IS and ES contribute to the quality of these customer ratings and reviews. IS ensures that reviews are detailed and reliable, while ES encourages consumers to share their positive experiences. This combination enhances the overall CRRQ, which, in turn, positively influences other consumers’ purchase intentions.

In summary, the study underscores the importance of both IS and ES in shaping online social media CPB. IS provides consumers with the necessary details to make informed decisions, while ES fosters a sense of community and trust. Together, these factors improve the CRRQ, which play a mediating role in influencing other consumers’ CPB. By understanding these dynamics, businesses can better influence social media platforms to enhance consumer engagement.

The implications of IS and ES on online social media CPB are significant. High-quality customer ratings and reviews serve as a crucial mediator, enhancing trust and credibility. When users receive reliable IS and ES, they are more likely to perceive the platform as trustworthy, leading to increased purchase intentions. Businesses should focus on fostering a supportive community where users can share their experiences and provide feedback. Encouraging detailed and honest reviews can amplify the positive effects of social support, making potential buyers feel more confident in their purchasing decisions. Additionally, leveraging advanced algorithms to highlight the most helpful reviews can further enhance user trust. By prioritizing both IS and ES, companies can produce a more engaging and trustworthy social commerce environment, ultimately driving higher sales and customer satisfaction.

We pay thanks to the editorial team of the journal who will receive this manuscript. The major credit also goes to the family who sacrifice their time while we perform the research work. Our research team also pays tribute to the scholarly atmosphere of the Department of Commerce, Bahauddin Zakariya University Multan, for providing research-oriented seminars and trainings to equip future scholars with modern-day research techniques and tools.