Examining the Predictors of Consumer Trust and Social Commerce Engagement: A Systematic Literature Review

Abstract

Trust and engagement in social commerce are increasingly recognized as critical drivers of consumers’ purchase intentions. This study aims to review the existing literature on consumer trust and engagement in social commerce by identifying the main predictors, examining research trends and gaps, and suggesting clear directions for future studies to better understand and support consumer behavior in this context. The study conducted a systematic literature review and a thematic analysis. The findings showed that four main themes were identified, including technological, organizational, social, and motivational. Under these four themes, eighteen sub-themes were identified. This study is innovative in systematically integrating predictors of consumer trust and engagement into a unified framework that positions trust as a mediator, and in developing a thematically grounded synthesis of four themes and eighteen sub-themes, highlighting underexplored areas such as entertainment and emerging technologies for future research. The studies of consumer trust and engagement are increasing in emerging economies. The study highlights the gaps in the current literature, including inadequate integration across theme categories, insufficient focus on emerging technologies such as blockchain and artificial intelligence, and a lack of examination of cultural and emotional aspects. Future research should deploy longitudinal designs, cross-cultural comparisons, and mixed-methods techniques to address these gaps. This study enhances the comprehension of trust and engagement in social commerce, offering significant insights for platform developers, marketers, and policymakers.

1. Introduction

Social commerce (s-commerce), which integrates social media with e-commerce, has revolutionized the digital economy. S-commerce promotes user-centered exchanges that foster collaboration [1]. Facebook Marketplace and Instagram Shopping, as well as other social media websites and applications, enable consumers to purchase products and services while connecting, offering feedback, and co-creating value [2]. The assimilation of social media into commerce has transformed consumer behavior, providing companies unparalleled opportunity to create trust, loyalty, and engagement [3,4,5]. S-commerce research is becoming important for digital consumer behaviors. Over the last decade, researchers have researched s-commerce adoption causes and effects. Trust, source credibility, social contact, and consumer engagement have historically influenced behavior, purchases, and loyalty [2,4,6]. Social presence and social support theories demonstrate that perceived social presence fosters trust and engagement [7,8]. The Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) demonstrate that the simplicity of use and usability of technology influence customer trust and involvement in social commerce [9,10].

Despite these gains, research on trust mechanisms [11], buying intentions [12], and consumer engagement [13] seldom synthesizes. Literature studies such as [14] summarize research trends, but do not address developing challenges like privacy, cross-cultural inequities, and the influence of new technology in the way of blockchain and artificial intelligence (AI). In the current time, where rapid changes in technology and consumer expectations are high, understanding trust, consumer engagement, and behavioral intentions is critical for policymakers and enterprises, to attract consumers and ensure their sustainability.

A lack of information in theoretical and methodological frameworks requires thorough assessment. Some research employs theories like social support and social presence [7,8], while other studies use emerging frameworks like the content dimensions framework [15] or service-dominant logic. This variety highlights the need to incorporate multiple perspectives in order to identify current trends in the variables impacting consumer trust and engagement in s-commerce, as well as to pinpoint deficiencies in the existing literature. Despite the widespread use of quantitative methodologies like Structural Equation Modelling (SEM) and Artificial Neural Networks (ANNs), sample sizes, analytical methods, and study designs limit generalizability, which implies the need for a literature review study to understand the most frequent factors that determine consumer trust and engagement in s-commerce.

This study fulfills the requirement for a comprehensive systematic literature review by synthesizing the current literature between 2019 and 2024, recognizing new trends, and offering practical insights for scholars and practitioners. This review aims to assess the prevailing theories, critically in s-commerce research and their contextual applications, identify and analyze significant independent, dependent, mediating, and moderating variables affecting s-commerce usage, and evaluate the methodologies utilized in the field to underscore their strengths, limitations, and potential for improvement. The study examines developing trends, including the impact of new technologies, privacy issues, and cross-cultural impacts, while suggesting a complete research agenda to tackle unsolved matters and adapt to changing market and technical environments.

This review is essential for several reasons. Initially, it provides an extensive understanding of the determinants influencing consumer trust and engagement in s-commerce, addressing gaps in the existing literature and generalizing the findings by incorporating different studies from different geographies and industries. Secondly, by integrating theoretical and methodological approaches, the study provides a framework for future research, allowing academics to expand upon the established literature. The assessment examines developing phenomena, including the integration of technology in s-commerce, offering insightful advice to organizations aiming to improve consumer engagement and trust. This study enhances the wider discussion on digital ecosystems by establishing s-commerce as a disruptive element in the contemporary economy. This review addresses these aims, bridging gaps in the current literature and establishing a basis for new and effective research in the rapidly developing field of s-commerce. To direct this systematic review, the following research questions are employed:

(1)

What are the primary predictors of consumer trust and engagement in social commerce?

(2)

What are the trends and gaps in the literature?

(3)

What are the directions for future work?

The following sections present a summary of the literature review. They also present the research methodology along with the findings, gaps, limitations, and future work.

2. Summary of Studies

The reviewed articles highlight the rapid growth and complexity of s-commerce research, highlighting trust, consumer engagement, technical advancements, and behavioral motivators. Over time, s-commerce has evolved, as noted by [14] and [16]. Based on the reviewed studies, s-commerce research has integrated social, technological, motivational, and behavioral components since 2007. The literature emphasizes the impact of consumer trust on s-commerce outcomesHajli, Sarker et al., and Alharbi and Alkhalifah highlight trust as a key mediator between antecedents like social presence, source credibility, and ease of use and dependent variables like purchase inten-tion and consumer loyalty [7,12,17]. References [8,13] found that trust improves consumer interactions and engagement, and reference [9] found that interpersonal trust affects s-commerce behavior more than organizational trust. Theoretical diversity and combination of theories improves s-commerce research. The TAM, theory of reasoned action (TRA), UTAUT, and Social Exchange Theory explore various variables that can strengthen consumer trust and engagement in s-commerce. Reference [18] stresses the need to incorporate impending technology paradigms into theoretical frameworks and suggests academics study emergent phenomena like e-commerce to share commerce and value and sustainability co-creation, using a combination of theories.

Consumer engagement holds numerous causes and effects together; references [13,19] found that social support, brand affiliation, and community identification increase engagement and loyalty behaviors like repurchase intention, co-creation willingness, and electronic word of mouth (eWOM). The research by [20] in tourism showed that engagement links authenticity, interaction, and engagement. These findings emphasize the relevance of engagement in shaping consumer trust and loyalty in s-commerce. Technological and behavioral elements improve s-commerce. Technological qualities like source authenticity and influencer endorsements enhance trust and behavioral intention, but privacy concerns hinder consumer engagement in s-commerce adoption [17]. References [15,21] emphasized how technological integration and content affect consumer experiences and results. These findings support the growing interest in hybrid analytical methods like SEM-ANN, which give deep s-commerce behavior insights [17,22].

S-commerce research is context-dependent, adding complexity. Reference [20] in tourism, reference [21] in fashion, and reference [18] in food and beverage, illustrate regional and sectoral s-commerce adoption inequalities, and references [5,23] explain how gender and local trust dynamics affect s-commerce behavior across settings. Many social, technical, motivational, and organizational factors impact consumer trust and participation in s-commerce. To build trust, engagement, and community, s-commerce needs social elements. Trust is a significant consumer behavior component in Social Presence Theory and Social Support Theory.

Reference [24] indicated that platform design and system quality, fundamental parts of the Information Systems (IS) Success Model, improve consumer engagement, whereas [18] stressed source credibility as a major factor in e-commerce satisfaction and buying intents. Hedonic and utilitarian motivations, perceived value, and consumer contact affect s-commerce consumer behavior. Both Self-Determination Theory and Uses and Gratification Theory describe these processes. Reference [25] employs the multi-attribute utility theory to study how value co-creation enhances consumer experiences, whereas [24] emphasizes hedonic and utilitarian incentives on participation. Consumer engagement relates interaction quality and perceived benefits to purchasing chasing intentions and loyalty. The Social Exchange Theory shows that perceived economic advantages and brand knowledge improve buying intentions through trust and involvement, motivating s-commerce consumers [26].

Organizational factors include how organizations and platform’s structure enable s-commerce activities to ensure success. Reference [23] emphasizes the importance of structural assurance in fostering trust in s-commerce platforms, especially in payment reliability and community engagement. Organizational initiatives like omnichannel integration are vital. Using the stimulus–organism–response (SOR) model, reference [21] show that flexible and integrated omnichannel commerce methods increase consumer participation, while connection and personalization have mixed results, while reference [18] emphasize user-centric, collaborative business models that focus on co-creation and sustainability, especially in technology-enabled s-commerce ecosystems.

Together, the studies reveal s-commerce’s complexity. Consumer relationships are based on trust and social presence, according to Social Support Theory and Social Presence Theory. Technological advances, supported by TAM and UTAUT, increase usability and interactivity. Uses and Gratifications Theory and Self-Determination Theory emphasize engagement and perceived worth as motivational variables. Organizational factors emphasize structural assurance and inventive business models based on socio-technical theory and the SOR model.

The selected studies span multiple regional and economic contexts, including both developed and developing countries such as the United States, China, Malaysia, Saudi Arabia, and Egypt. While the majority of studies are concentrated in Asia and the Middle East, this geographic distribution reflects regions with high rates of social commerce adoption and digital engagement. The inclusion of diverse contexts enhances the transferability of findings and highlights the influence of cultural and technological ecosystems on trust and engagement dynamics [9].

3. Research Methodology

This study employs a systematic literature review (SLR) technique, utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to guarantee a thorough and transparent review procedure. The PRISMA guidelines facilitate a structured methodology for identifying, screening, and synthesizing existing research, enabling the inclusion of relevant studies while systematically addressing potential gaps in the literature [27,28,29]. Through this process, the review ensures replicability and comprehensiveness in its examination of the research landscape.

To further enhance the analysis, the study employs thematic analysis as a qualitative method to identify, analyze, and interpret recurring patterns and themes within the selected studies. Thematic analysis provides a robust approach to categorizing key findings, allowing the synthesis of diverse perspectives on critical aspects such as trust, consumer engagement, and technological integration in s-commerce [30,31,32]. By integrating PRISMA with thematic analysis, the study not only adheres to methodological rigor, but also delivers a nuanced understanding of the literature, offering valuable insights into emerging trends and underlying relationships within the field. The process of searching for articles is given in Figure 1.

3.1. Search Method

We searched Scopus and Web of Science for peer-reviewed, high-impact journal articles. Specific key terms were used to identify the related articles. To enhance transparency and replicability, following PRISMA 2020 guidelines [28], additional details of the search process are provided. The selection of keywords followed an iterative refinement procedure informed by scoping searches, reference list checks, and keyword analysis from seminal s-commerce studies. Synonyms and alternative terms were mapped into conceptual clusters covering (1) the phenomenon (“social commerce”, “s-commerce”, “social shopping”, “social buying”), (2) focal constructs (“trust”, “consumer trust”, “user trust”, “buyer trust”), (3) engagement-related terms (“consumer engagement”, “customer engagement”, “user engagement”, “participation”), and (4) relationship descriptors (“predictors”, “determinants”, “antecedents”, “factors”). Boolean operators (OR/AND) were used within and between clusters to ensure comprehensive coverage. In Scopus, searches were restricted to the Title, Abstract, and Keywords fields, while in Web of Science, the Topic field (Title, Abstract, Author Keywords, and Keywords Plus) was used. Filters excluded conference proceedings, book chapters, and non-English publications. All database settings, search strings, and retrieval dates were documented in a search protocol log to ensure exact reproducibility. The process and documentation approach followed best-practice examples from systematic reviews [9].

For Scopus, the search string was TITLE-ABS-KEY((“social commerce” OR “s-commerce”) AND (“trust” OR “consumer trust”) AND (“consumer engagement” OR “customer engagement”) AND (“predictors” OR “determinants”)) AND PUBYEAR > 2018 AND PUBYEAR < 2025 AND (LIMIT-TO(DOCTYPE, “ar”)) AND (LIMIT-TO(LANGUAGE, “English”)).

For Web of Science, the search string was TS = ((“social commerce” OR “s-commerce”) AND (“trust” OR “consumer trust”) AND (“consumer engagement” OR “customer engagement”) AND (“predictors” OR “determinants”)) with the same publication year and language filters applied.

Scopus and Web of Science were selected because they are the two largest multidisciplinary and peer-reviewed indexing services that comprehensively cover high-impact journals in information systems, management, and social commerce. This choice aligns with database selection standards applied in recent systematic reviews [33,34]. While databases such as IEEE Xplore, ScienceDirect, or ProQuest also contain relevant materials, these often focus on conference proceedings, practitioner reports, or gray literature, which fell outside the empirical and journal-based scope of this review. Nevertheless, their exclusion is acknowledged as a limitation that may restrict comprehensiveness.

3.2. Criteria for Inclusion and Exclusion

A set of inclusion and exclusion criteria was created to enhance the selection of research. Articles were included if they explicitly investigated predictors or antecedents of trust and consumer engagement in s-commerce, were published in reputable journals (indexed in Scopus or Web of Science), and employed empirical methodologies such as surveys or meta-analysis systematic reviews. Studies were eliminated if they addressed e-commerce in general rather than specifically social commerce, were published in languages other than English, or lacked adequate methodological information or a theoretical foundation. Two separate reviewers carried out the screening and selection procedure, separately evaluating the entire texts, abstracts, and titles, according to the established criteria. There was no usage of automation tools. A comprehensive screening process resulted in identifying 1029 articles. This has also resulted in removing 178 articles indexed in Scopus and Web of Science. Based on the screening of publication year, a total of 511 articles were removed. In addition, 69 articles were not suitable, due to being published in a conference or a book chapter. A total of 101 articles were removed based on languages being non-English. Lastly, a total of 118 articles were removed because they focus on areas not related to s-commerce.

3.3. Data Extraction

A standardized data extraction sheet was created to document essential information from each article, ensuring consistency and accuracy. The extracted data encompassed the study’s title, authors, publication year, nation of emphasis, theoretical framework, independent and dependent variables, mediating and moderating factors, research methods, sample size, data analysis methodologies, and results. A full reading of articles was conducted. This has resulted in removing 22 articles because they did not focus on predictors of consumer trust or engagement in s-commerce. There are 30 articles included in this study. As a result, 30 empirical studies were included in the final synthesis. This selective approach aligns with prior systematic literature reviews that emphasize quality over quantity, to preserve thematic clarity and analytical depth [35]. A more extensive inclusion would have risked heterogeneity and reduced the analytical coherence of the review.

3.4. Method of Thematic Analysis

Using a thematic analysis technique, we categorized the retrieved variables into four main themes: technological, organizational, social, and motivational. Themes were developed following a comprehensive analysis of each article, with studies categorized under a theme when their identified predictors or antecedents distinctly matched the conceptual description of that category. Prior to summarizing the findings, we combined variables with similar meanings to improve conceptual clarity and decrease repetition. For instance, perceived usefulness was equated with relative advantage and performance expectancy; social influence was associated with subjective norms; and effort expectancy was regarded as similar to perceived ease of use (PEOU) or complexity. Likewise, perceived advantages, perceived costs, and perceived value were amalgamated into a singular construct, while the utilization of technology was categorized under the general concept of technical development.

Although conceptual consolidation was applied to reduce redundancy, it is important to note that the same construct was not always operationalized uniformly across the included studies. For instance, perceived risk was measured in some studies primarily through privacy and security concerns [5,11], while others broadened it to encompass financial risk, transaction reliability, and potential product quality issues [22,23]. Similarly, perceived value was variously defined as a trade-off between benefits and costs [24], as hedonic enjoyment [13], or as an amalgamation of both utilitarian and experiential benefits [25]. Even widely adopted constructs such as social presence showed divergence, with certain studies operationalizing it as the perception of human warmth and responsiveness [8], whereas others measured it through interactivity and platform responsiveness indicators [21]. These definitional and measurement variations can influence how predictors are classified within thematic categories and may partly explain differences in their reported significance across contexts [35]. Future research would benefit from greater standardization of construct operationalization, potentially through consensus-based measurement frameworks, to strengthen comparability and cumulative knowledge development in s-commerce trust and engagement research [36,37,38]. The four main themes were further refined into 18 sub-themes. Results from separate research, accompanied by the thematic synthesis, were displayed in comprehensive summary tables that list each study’s characteristics, the variables analyzed, and the principal conclusions. This review utilized a qualitative theme synthesis instead of a statistical meta-analysis; hence, no effect measurements, heterogeneity analyses, or sensitivity analyses were performed. This absence of effect-size estimation and sensitivity testing represents a methodological limitation; future reviews could integrate statistical aggregation techniques where datasets allow comparability, following recommendations from recent PRISMA-based systematic and bibliometric reviews [34].

To ensure methodological rigor, the classification of variables into the four themes and eighteen sub-themes was not directly adopted from the original articles, but was developed through an author-led coding process. This systematic protocol strengthens the transparency and validity of the thematic synthesis, thereby enhancing readers’ confidence in the robustness of the categorization process.

3.5. Assessment of Risk of Bias and Certainty of Evidence

No formal statistical methods were utilized to assess the possibility of bias due to missing results, as the synthesis was performed subjectively, without statistical aggregation. To mitigate any reporting bias, the evaluation employed a thorough search method with clearly defined inclusion and exclusion criteria. Similarly, no formal certainty assessment instrument (e.g., GRADE) was utilized. Confidence in the conclusions was based on the consistent patterns observed across numerous high-quality empirical studies.

4. Results

4.1. Profile of Articles

In this study, a total of 30 articles were reviewed. The article can be divided based on their countries, industry, sample size, and approach. The number of articles increased during the COVID-19 pandemic in 2020. This can reflect the trend in the year 2020, where online transactions have increased massively. One of the sectors that benefits from COVID-19 is online buying, particularly via s-commerce. Figure 2 shows the distribution of articles based on the year.

The emerging countries benefit more from the growth of s-commerce in terms of the number of articles. This could be due to the notion that these countries found themselves to be forced to use the online businesses, while these are well-established in developed countries. As shown in Figure 3, the growing number of articles are in emerging economies such as Saudi Arabia (28%), China (14%), and Malaysia (9%). In Saudi Arabia, this growth could be related to the government initiative such as Vision 2030, which aims to digitalize the country and enhance the contribution of the non-oil sector, while it could be related, in China and Malaysia, to the consumer preference and changing behavior toward s-commerce. While this distribution reflects regions with advanced s-commerce adoption and supportive policy environments, it also indicates a geographic concentration that may constrain the generalizability of the findings. Cultural orientations in these contexts—predominantly collectivist—tend to reinforce relational trust mechanisms such as reciprocity, community belonging, and interpersonal credibility, as emphasized in Social Exchange Theory. These drivers may be less dominant in more individualistic societies, where cognitive and utilitarian considerations aligned with TAM and UTAUT, such as perceived usefulness and ease of use, could play a stronger role in shaping engagement. Moreover, industry-specific conditions can moderate these relationships: sectors with strong experiential and hedonic appeal, such as fashion or tourism, may rely more heavily on social presence and community interaction, whereas highly regulated industries like finance or healthcare place greater emphasis on system quality, security, and compliance assurances. The current concentration of studies in a limited set of cultural and industrial contexts highlights the need for cross-cultural, multi-sectoral research designs to examine whether and how these mechanisms operate differently across diverse environments.

Beyond geographic concentration, industry-level variations also warrant deeper attention. For instance, in finance and healthcare sectors, where consumer risk perception is particularly high, predictors such as system quality, privacy, and security assurances may carry more weight in shaping trust and engagement compared to relational or hedonic factors. In contrast, tourism and hospitality industries rely more heavily on authenticity, social presence, and interactivity, which foster emotional engagement and co-creation. Similarly, fashion and retail industries emphasize social influence, community identity, and entertainment-driven features (e.g., live-stream shopping, gamification) to sustain consumer participation. These contrasts highlight the fact that predictors of trust and engagement are not uniform across industries; instead, they are moderated by sector-specific characteristics such as regulatory requirements, consumer expectations, and the hedonic–utilitarian balance of transactions.

The reviewed studies can be divided into two approaches. The first is the empirical studies that examined the predictors of consumer trust and engagement in s-commerce, using quantitative methods. The second is the meta-analysis and SLR studies that identified the predictors. A total of 66% of articles were empirical, using a quantitative approach, while 31% were SLR and used meta-analysis, and 3% were mixed methods, using quantitative and qualitative approaches. Among the 66% of the empirical studies using quantitative methods, the sample size ranged between 189 and 2058, with a mean of 401 respondents. This reflects the trend toward using structural equation modeling, which requires a high sample size compared with SPSS (version 29).

The reviewed studies were analyzed using several analytical techniques and software. Mainly, the analysis was conducted by SEM or thematic analysis. A combination of two techniques was also observed in the literature by using a combination of meta-analysis and SEM or SEM and Artificial Neural Network (ANNs). Figure 4 shows data analytical techniques. The CASP-based assessment indicated that the majority of included studies demonstrated clear research aims, appropriate methodology, and valid results. However, some studies (n = 5) had limitations in reporting sampling strategies, and a few (n = 3) did not adequately address potential confounding factors. These limitations should be considered when interpreting synthesis results.

4.2. Thematic Analysis

The thematic analysis indicated that there are four main themes that are divided into eighteen sub-themes. The main and sub-themes are shown in

Table 1. Thematic Analysis.

Ref	Technological Factors						Organizational Factors			Social Factors					Motivational Factors
	PR	SQ	IQ	SSQ	TA	FC	CO	SR	PEOU	SOI	SS	SV	SI	SP	PU	PV	IV	ENT
[11]	X							X
[39]			X				X			X		X					X
[8]														X
[22]														X			X
[12]									X	X			X	X	X		X
[10]										X					X
[13]									X					X
[26]										X		X			X
[18]							X					x		X	X
[7]											X			X
[9]			X						X						X
[40]	x						X					X
[24]		X								X		X				X
[41]	x								X						X	X
[23]	X			X			X			X
[25]	X						X			X
[20]	X						X			X
[19]	x						X			X					X			X
[3]				X	X											X
[42]					X			X
[43]						X			X				X		X
[5]	x					X								X			X
[44]							X	X			X
[45]							X			X					X	X
[21]	X						X	X								X	X
[46]			X					X			X	X
[17]	X		X					X			X
[16]											X			X	X	X
[15]	X							X									X
Total	11	1	4	2	2	2	10	7	5	10	5	6	2	8	10	6	6	1

Note. PR: Perceived risk, SQ: Service quality, IQ: Information quality, SSQ: System quality, TA: Technological advancement, FC: Facilitating conditions, CO: Convenience, SR: Source credibility, PEOU: Perceived ease of use, SOI: Social interaction, SS: Social support, SV: Social value, SI: Social influence, SP: Social presence, PU: Perceived usefulness, PV: Perceived value, IV: Information value, ENT: Entertainment. (X indicates that the factor was examined in the respective study)

.

The most frequent variables are related to perceived risk, and this reflects the concern of consumers, since all the transactions are conducted online and using social media applications. Convenience is also a notable variable, because the use of s-commerce allows users to browse and choose anytime from anywhere and order the product to their doorstep. Social interaction is also important and highly frequent. This is because s-commerce enables users to socialize and view comments and ratings of other users. Perceived usefulness also is essential. The usage of s-commerce saves time and cost. The least frequent variables are entertainment, service quality, technological advancement, and system quality, as well as social influence. Based on the thematic analysis, this study proposes that the four themes can impact consumer trust and engagement, with consumer trust acting as a mediating variable.

The conceptual framework presented in Figure 5 offers a comprehensive synthesis of predictors influencing consumer trust and engagement in s-commerce. While grounded in established theoretical models such as the TAM [47], the UTAUT [48], and the Technology–Organization–Environment (TOE) framework [49], this framework diverges in critical ways to better capture the multidimensionality of consumer behavior in the context of s-commerce. First, TAM emphasizes perceived usefulness and perceived ease of use as primary determinants of technology acceptance. However, it ignores social, emotional, and contextual factors that are crucial in s-commerce contexts, and is restricted to cognitive assessments of technology. By incorporating social presence, social interaction, support, and source credibility, all of which are critical in peer-influenced contexts such as s-commerce, the proposed framework in Figure 5 goes beyond these constructs [5,7,8]. TAM does not adequately account for the relational and participatory aspects of s-commerce, which are reflected in this wider coverage.

Second, UTAUT retains a largely instrumentalist perspective on technology use that is centered on efficiency and intention, even though it introduces variables like social influence, facilitating conditions, and performance expectancy to address some of TAM’s shortcomings. Drawing from the Self-Determination Theory [25] and the Uses and Gratifications Theory [13,26], the proposed framework in Figure 5 goes one step further by integrating motivational drivers like entertainment, perceived value, and information value. These elements provide a more comprehensive understanding of engagement by taking into consideration both extrinsic and intrinsic user motivations that go beyond utilitarian purposes.

Third, the TOE framework emphasizes contextual dimensions such as technological readiness, organizational support, and environmental factors at the firm level, even though it is widely used to research innovation adoption in organizational settings. Importantly, TOE leaves out psychological factors like trust and engagement, which are crucial to consumer decision-making in s-commerce. In contrast, the proposed framework in Figure 5 applies these dimensions at the individual consumer level, incorporating elements like organizational design (e.g., convenience, source credibility) and technological affordances (e.g., system quality, risk, information accuracy). Thus, by modifying the logic of TOE to micro-level behavioral processes, the current framework fills a gap [17,21].

Furthermore, a significant difference between Figure 5 and TAM, UTAUT, and TOE is the explicit inclusion of consumer trust as a mediating variable. The model acknowledges trust as a mechanism through which organizational, social, technological, and motivational factors impact consumer engagement, drawing on Social Exchange Theory [50]. This mediating function is consistent with recent empirical research showing the importance of trust in transforming antecedents into behavioral intentions and loyalty [7,24,26]. Last, but not least, the framework shown in Figure 5 stands out for its capacity to take into account new themes and digital risks that are frequently overlooked in legacy models. In the changing s-commerce landscape, for example, factors like perceived risk, privacy concerns, and the quality of AI-enabled services are becoming increasingly important [5,22,43]. By incorporating these elements, the framework becomes more adaptable to current issues and advancements in digital consumer environments. For greater clarity, the main framework in Figure 5 has been simplified to highlight the four major themes—technological, organizational, social, and motivational—and their links to consumer trust and engagement. The comprehensive mapping of the eighteen sub-themes is provided in Appendix A (Figure A1), ensuring full transparency while keeping the main text concise and free from unnecessary complexity.

To complement the thematic synthesis and enhance the analytical depth of this review, a quantitative synthesis in the form of vote counting was undertaken. This approach, widely employed in systematic literature reviews where statistical meta-analysis is not feasible [35], involves enumerating the number of studies in which each construct was identified as a significant predictor of either trust or engagement. While vote counting does not provide effect-size estimations, it offers a comparative indication of the relative prominence of variables within the existing literature, thereby enabling a more nuanced prioritization of factors for future research and theoretical development.

The frequency analysis revealed that certain constructs have received substantially greater empirical attention and support than others. As summarized in

Table 2. Frequency of Predictors by Theme (Vote Counting Results).

Theme	Sub-Theme	Frequency (n Studies)	% of 30 Studies
Technological	Perceived Risk (PR)	11	36.7%
	Service Quality (SQ)	1	3.3%
	Information Quality (IQ)	4	13.3%
	System Quality (SSQ)	2	6.7%
	Technological Advancement (TA)	2	6.7%
	Facilitating Conditions (FC)	2	6.7%
Organizational	Convenience (CO)	10	33.3%
	Source Credibility (SR)	7	23.3%
	Perceived Ease of Use (PEOU)	5	16.7%
Social	Social Interaction (SOI)	10	33.3%
	Social Support (SS)	5	16.7%
	Social Value (SV)	6	20.0%
	Social Influence (SI)	2	6.7%
	Social Presence (SP)	8	26.7%
Motivational	Perceived Usefulness (PU)	10	33.3%
	Perceived Value (PV)	6	20.0%
	Information Value (IV)	6	20.0%
	Entertainment (ENT)	1	3.3%

, perceived risk, convenience, social interaction, perceived usefulness, and source credibility were the most frequently supported predictors across the reviewed studies, each appearing in approximately one-third or more of the sample. In contrast, variables such as entertainment, technological advancement, system quality, and social influence were rarely reported as significant predictors, appearing in less than 10% of the included studies. These differences may reflect both theoretical prioritization in the field and gaps in empirical exploration.

The results suggest that while the most frequently studied variables may represent established predictors with robust empirical support, less frequently examined constructs could hold untapped theoretical significance, particularly in emerging contexts and with evolving technologies.

4.2.1. Technological Factors

Important sub-themes of technological factors include the perceived risk (PR), service quality (SQ), information quality (IQ), system quality (SSQ), technological advancement (TA), and facilitating conditions (FCs). These sub-themes were identified as critical for consumer trust and engagement. These sub-themes demonstrate how platform functionality and user-friendly features facilitate smooth interactions. Reference [24] demonstrates that system quality enhances perceived value and engagement, while [17] found that information quality influences trust in social commerce platforms, but privacy issues impede trust and engagement. In this study, perceived risk was combined with additional factors like consumer concern, privacy, security, and risk.

Since privacy, security, and data exploitation concerns lower consumer trust and discourage platform engagement, perceived risk is a major barrier [5,11]. Creating a secure and dependable workplace requires reducing these hazards. However, service and information quality boost trust and engagement. Service quality, including timeliness and customization, boosts dependability and consumer satisfaction, boosting trust and platform engagement [24]. Information quality increases trust by providing accurate and relevant data, increasing user interaction and engagement by meeting their informational needs. System quality ensures a safe and smooth user experience. Users are more confident and engaged on platforms with intuitive interfaces, reliable performance, and solid security frameworks [24]. This is especially beneficial when integrated with AI and blockchain, which address transparency and security while adding innovative features that boost user interactions [42]. Aligning user expectations with platform capabilities requires facilitation. Technical assistance and resource availability reduce adoption barriers and improve the user–platform relationship, boosting trust and continued engagement [43].

4.2.2. Organizational Factors

Organizational factors had three sub-themes in this study: convenience (CO), source credibility (SR), and perceived ease of use (PEOU), and these sub-themes affect trust and consumer engagement in s-commerce by addressing specific user experience and decision-making aspects. CO makes s-commerce systems more trustworthy and engaging, by reducing user effort and optimizing interactions. Easy navigation, fast checkout, and multi-channel accessibility make shopping easy, boosting trust in the platform’s dependability and user-centric design [39]. Convenience boosts engagement because consumers are more likely to use platforms that meet their product delivery needs [21].

By ensuring that platform or third-party information is accurate, reliable, and genuine, source credibility (SR) builds trust. Verified reviews and clear communication reduce s-commerce risks and boost consumer trust [11,17]. Trust gives consumers a sense of security, enabling them to connect more with platform content and social features. By reducing cognitive effort to use s-commerce platforms, perceived simplicity of use increases trust and engagement. User-friendly platforms reduce irritation, giving consumers confidence and control, and boosting trust [43]. Increased PEOU reduces barriers to engagement, including complicated interfaces or confusing processes, making the user experience more enjoyable and interactive [12].

4.2.3. Social Factors

Social variables include SOI, SS, SV, SI, and SP. SOI allows transparent and meaningful relationships between consumers, suppliers, and peers, fostering trust. Effective involvement builds platform community trust and connection, reducing uncertainty and increasing reliability [39]. Active and participatory social interactions encourage frequent encounters and community participation, which boosts user engagement. Social support (SS) boosts trust and engagement by providing emotional and informational assistance on the platform. Peer or platform support shows a cooperative and user-centric environment, which builds trust [7]. Social support fosters user engagement, prompting individuals to seek guidance and exchange experiences; therefore, it enriches their involvement in the platform ecosystem. In addition to practical advantages, social value (SV) enhances consumers’ social identity and sense of belonging; hence, it cultivates trust. Consumers have confidence in platforms that facilitate relationships and provide social validation [39].

Social influence (SI) derived from the thoughts and behaviors of others fosters consumer trust and engagement. Favorable platform interactions from peers, influencers, and reputable individuals enhance consumer confidence [5]. Social influence motivates individuals to adopt and adhere to communal concepts by promoting social norms or trends. Social presence (SP) enhances confidence in social commerce by fostering authenticity and connection. Consumers rely on platforms that replicate social cues such as responsiveness, personalization, and human-like engagement [11]. Social presence fosters a feeling of “presence” on the platform, promoting more frequent and meaningful user interactions [7].

4.2.4. Motivational Factors

PU, PV, IV, and ENT are essential for consumer trust and s-commerce engagement. Practical advantages and experiential satisfaction foster consumer trust and engagement with s-commerce platforms. PU enhances trust by demonstrating the platform’s capability to fulfill client requirements. When a platform improves efficiency, aids decision-making, or offers substantial assistance throughout the purchasing process, consumer trust increases [43]. Consumers are more predisposed to interact with platforms that assist them in locating pertinent products or finalizing transactions [12]. Perceived value (PV) enhances trust and participation by establishing a favorable equilibrium between benefits and costs. High perceived value, including competitive price, superior product quality, or exclusive offers, enhances product confidence, equity, and reliability [24]. This encourages consumer participation, since consumers are more likely to spend time on valuable sites.

Information value (IV) improves decision-making and trust. For consumer trust, precise, relevant, and implementable information reduces uncertainty and increases transparency [39]. Valuable information platforms encourage users to explore, share, and argue, creating a dynamic and interactive environment. Entertainment (ENT) represents a relatively underexplored but increasingly influential factor in driving trust and engagement within s-commerce platforms. Beyond enhancing the hedonic appeal of platforms, entertainment functions as a psychological motivator that sustains user retention, fosters immersion, and encourages repeat interaction. Emerging research highlights the role of gamification features, interactive interfaces, and visually appealing designs in cultivating positive emotional states that indirectly strengthen trust by signaling platform credibility and user-centricity [19]. From a behavioral perspective, entertainment deepens engagement by transforming transactional interactions into enjoyable experiences, thereby increasing willingness to participate in co-creation and electronic word-of-mouth. In practice, platforms that integrate entertainment—through live-stream shopping, virtual events, or gamified reward systems—create a sense of playfulness and immersion that complements utilitarian drivers such as convenience and perceived usefulness. Despite its low frequency in the reviewed studies (3.3% of included articles), its growing adoption in digital commerce contexts suggests that entertainment could emerge as a critical predictor of sustained engagement. Accordingly, future research should explore how entertainment interacts with other constructs (e.g., social presence, perceived value) and test its long-term effects on consumer trust and loyalty across diverse cultural and technological contexts.

4.2.5. Trust as Mediator

Trust serves as a crucial mediating function in social commerce (s-commerce) by converting the influences of many predictors into consumer results, including engagement, loyalty, and purchase intentions. It alleviates the intrinsic uncertainty of online transactions, rendering it an essential tool for cultivating trust in s-commerce platforms. Trust mitigates the adverse effects of perceived risk by assuring consumers of platform reliability, therefore promoting involvement, despite initial reservations [11]. Social elements, including social presence and social support, significantly depend on trust to convert relational encounters into behavioral outcomes. Effective communication and collaborative settings foster trust, therefore promoting loyalty and active engagement [7]. Likewise, motivating elements like perceived utility and perceived value are rendered useless without trust, which guarantees that these perceptions result in concrete actions, including purchase intentions [43]. Technological qualities, such as system and information quality, rely on trust to connect technical performance with user happiness and engagement. In the absence of trust, even superior systems struggle to maintain user retention, since doubts regarding security or transparency diminish their efficiency [24]. Theoretical frameworks such as Social Exchange Theory highlight trust as crucial for connecting predictors and outcomes, emphasizing its importance in mitigating uncertainty and fostering favorable consumer behaviors.

4.2.6. A Critical Review of Consumer Trust and Engagement in S-Commerce

Consumer trust and engagement are central constructs in s-commerce literature. Yet, despite their prominence, the definitions, antecedents, and outcomes of these variables differ considerably across studies. This review synthesizes how the 30 selected articles conceptualize and operationalize these constructs, offering a clearer understanding of their theoretical foundations, empirical contexts, and interconnections. Consumer trust in s-commerce refers to the belief that platforms, vendors, or peer users will act reliably, securely, and honestly within digital transactional environments. Trust has been widely acknowledged as a key mediating variable that links technological, organizational, and social antecedents to consumer behavior [7,17,26]. Several predictors consistently emerge across the literature. Technological attributes such as system quality, service quality, and information quality were positively associated with trust [11,24,39]. These findings are grounded in Information Systems Success Models [51] and extended by TAM and UTAUT, where ease of use and performance expectancy serve as antecedents of trust [9,10].

Social factors also play a critical role. Social presence, social support, and interpersonal interactions significantly contribute to trust formation [5,7,8]. In this context, Social Presence Theory and Social Support Theory are commonly applied. For example, [5] highlighted how gendered privacy concerns influence trust when consumers interact with user-generated content in social commerce environments. Organizational characteristics such as source credibility and convenience also emerged as significant antecedents. Reference [43] found that reliable, transparent sellers enhance platform trust, particularly when combined with user-friendly interfaces. Likewise, [12] emphasized trust’s role in transforming source credibility into behavioral outcomes such as engagement and loyalty. Despite a rich body of evidence, most studies applied cross-sectional, quantitative approaches, limiting understanding of how trust evolves over time. Few studies (e.g., [22]) attempted to differentiate between interpersonal trust (in other users) and institutional trust (in the platform itself), a gap that future research should address.

Consumer engagement in s-commerce is broadly defined as users’ voluntary, repeated interaction with the platform, community, or content. It is commonly framed as an outcome variable (e.g., co-creation, eWOM, loyalty) or as a mediator between antecedents (e.g., perceived value) and behavioral outcomes [13,25]. Engagement has emotional, cognitive, and behavioral dimensions and is influenced by motivational, social, and technological drivers. Motivational factors are particularly prominent. Theories such as Uses and Gratifications Theory and Self-Determination Theory underpin findings that perceived usefulness, perceived value, and entertainment increase engagement [19,24,26]; [25] showed that co-creation value and utility perceptions encourage user retention and loyalty.

Social predictors, including social identity, community belonging, and peer interaction, are central to engagement dynamics, and [20] demonstrated that authenticity and interactivity foster engagement in peer-based tourism platforms. Reference [19] explored how brand communities strengthen engagement via national origin cues, while [45] examined how interactive technologies enhance behavioral engagement. Technological factors, including platform responsiveness, interactivity, and personalization, also influence engagement; [21], applying the stimulus–organism–response (SOR) model, found that omnichannel retail design leads to emotional activation and behavioral engagement. Similarly, [22] employed hybrid SEM-ANN models to assess how technological affordances predict both engagement and loyalty.

Despite these findings, engagement is rarely treated as a multi-stage or evolving construct. The majority of studies treat it statically, ignoring how engagement may fluctuate based on trust breaches, user experience, or external influences (e.g., policy shifts or technological innovation). Longitudinal studies are needed to understand retention and disengagement, especially in rapidly evolving digital ecosystems.

Table 3. Comparison Among Studies.

Ref	Focus	Key Predictors	Theory Used	Country/Context
[7]	Trust	Social presence, eWOM	Social Presence Theory	UK
[17]	Trust	Privacy, information quality	TAM, IS Success	Saudi Arabia
[24]	Trust/Engagement	System quality, hedonic value	IS Success, SOR	Malaysia
[25]	Engagement	Co-creation, value	Self-Determination Theory	Pakistan
[13]	Engagement	Social support, community factors	Social Support Theory	Spain
[8]	Trust	Social support, commitment	Social Commerce Trust Framework	UAE
[10]	Trust	Ease of use, credibility	UTAUT	Iran
[21]	Engagement	Omnichannel design, content quality	SOR	Saudi Arabia
[26]	Trust/Engagement	Perceived risk, trust, intention	Social Exchange Theory	Lebanon
[45]	Engagement	Interactive platforms, user control	Digital Experience Theory	Global
[19]	Engagement	Community identity, brand country	Brand Community, UGT	China
[39]	Trust	Service quality, interaction	TAM	China
[5]	Trust	Gender, privacy concerns	Social Presence, Privacy Concerns	Saudi Arabia
[16]	Trust/Engagement	Institutional trust, system quality	SEM-ANN Hybrid	Malaysia
[43]	Trust	Source credibility, convenience	TAM, Trust Transfer	Saudi Arabia

synthesizes key studies that have examined consumer trust and engagement in s-commerce:

The literature reveals that consumer trust and consumer engagement are deeply intertwined, yet conceptually distinct constructs. Trust operates primarily as a mediator, translating platform quality, user interaction, and system security into behavioral outcomes. Engagement, in contrast, reflects a broader constellation of user attitudes and actions, often shaped by motivational and social cues. While theoretical diversity has enriched the field, more integrative models are needed that consider both constructs together, especially as s-commerce continues to evolve with emerging technologies like blockchain, AI, and VR.

4.2.7. Towards an Integrated Theoretical Synthesis

The extant body of research on social commerce adoption and engagement demonstrates extensive reliance on established theoretical frameworks, most notably the TAM, the UTAUT, the TOE framework, and SET. However, the prevailing applications of these models tend to adopt a parallel and compartmentalized orientation, resulting in fragmented theoretical insights and attenuated explanatory coherence. A critical synthesis reveals that while each framework encapsulates valuable explanatory mechanisms, their isolated deployment limits the capacity to capture the complex, multi-layered interplay between technological affordances, socio-relational processes, and contextual contingencies that underpin consumer trust and engagement in s-commerce environments.

TAM, underpinned by [47], postulates perceived usefulness and perceived ease of use, and offers a parsimonious yet predominantly cognitive view of technology adoption. While this model has demonstrated predictive efficacy in assessing adoption intention, it neglects the affective, socio-cultural, and trust-based dimensions that are foundational to relationship-centric environments such as s-commerce. SET, by contrast, is rooted in the norm of reciprocity and relational exchange [50], positioning trust, commitment, and mutual obligation as central mediators of sustained engagement. Nevertheless, its socio-relational lens underplays the role of system performance, interface quality, and technological readiness—dimensions that are particularly salient in digital commerce contexts. UTAUT advances beyond TAM, through its inclusion of social influence, facilitating conditions, and performance expectancy [48], yet its utilitarian emphasis risks overlooking intrinsic motivations, hedonic gratifications, and symbolic consumption drivers that have emerged as significant in s-commerce ecosystems. TOE, although highly relevant for innovation adoption at the organizational level, was conceived to examine macro-contextual influences—technological readiness, organizational capabilities, and environmental pressures—thereby necessitating theoretical recalibration when applied to micro-level, consumer-facing phenomena.

The juxtaposition of these frameworks reveals distinct complementarities. TAM’s cognitive determinants can be theoretically deepened through UTAUT’s incorporation of social norms and enabling conditions, while SET embeds the relational mechanisms that translate platform interactions into trust and commitment. TOE, when adapted to the individual level, introduces structural and institutional contingencies, such as regulatory environments, market maturity, and infrastructural robustness, that moderate the relationship between antecedents and engagement outcomes. A theoretically integrated model thus offers a more holistic explanatory architecture, accounting for both functional and relational antecedents while situating them within broader cultural–institutional ecosystems.

The cultural specificity of these theoretical logics is non-trivial. In collectivist economies such as Saudi Arabia, China, and Malaysia, contexts that dominate the current empirical corpus, SET’s relational reciprocity and communal embeddedness may exhibit greater salience than TAM’s efficiency-oriented predictors. Conversely, in individualistic markets, cognitive appraisals of utility and usability, as articulated in TAM and UTAUT, may hold primacy. TOE’s environmental dimension provides theoretical agility to reconcile these divergences by embedding adoption and engagement decisions within context-specific socio-technical and regulatory configurations.

The conceptual schema advanced in this study (Figure 5) moves beyond the juxtaposition of these models, to propose a theoretically unified account in which consumer trust is positioned as the central mediating mechanism through which technological, organizational, social, and motivational factors exert their influence on engagement. This synthesis not only bridges the cognitive–relational divide, but also incorporates environmental contingencies, yielding a multi-dimensional framework with enhanced explanatory depth and cross-contextual generalizability. Such integration advances theoretical discourse in the field by reconciling previously siloed perspectives and providing a coherent analytical lens for examining s-commerce behavior in heterogeneous cultural and industrial settings.

5. Gaps in the Literature

The extensive literature review illuminated s-commerce consumer trust and engagement factors. However, a few gaps remain that require additional research to improve this subject and solve literature gaps. A framework that integrates motivational, social, organizational, and technological components is lacking. Few studies have synthesized perceived usefulness, social interaction, and system quality to understand their effects on trust and engagement. This fragmented approach makes it harder to uncover synergies or trade-offs among predictors, which is essential for s-commerce platform strategy.

Geographical research concentration limits the generalizability of the findings of previous studies. A large number of studies concentrated on Saudi Arabia, China, and developed Western nations, overlooking other cultures and socioeconomic circumstances. The limited geographic breadth restricts conclusions and ignores cultural, legal, and technical factors that impact consumer behaviors and preferences. Digital literacy, privacy issues, and infrastructure availability might affect s-commerce trust-building methods in developing nations and established markets. The data also shows a lack of longitudinal trust and engagement studies. Many studies employ cross-sectional data, which gives a snapshot of consumer behaviors but reflects limitedly the changing nature of trust and engagement in s-commerce ecosystems. Understanding trust over time and how platform enhancements, technical advances, and consumer expectations affect participation requires longitudinal study. Moreover, the industry-level scope of current studies is narrow, with limited coverage beyond e-commerce, fashion, and tourism. Future research should extend into diverse industries such as finance, healthcare, and education, where trust and engagement mechanisms may be shaped by stricter regulations, ethical considerations, and distinct consumer motivations. This broader coverage would significantly enhance the external validity and cross-sectoral applicability of social commerce research.

Moreover, there is little investigation of future technologies, such as AI, blockchain, and virtual reality, regarding trust and engagement. Although several studies examine technological elements, such as system quality and technological progress, the capacity of these sophisticated technologies to enhance consumer experiences and cultivate trust is still inadequately investigated. This gap is essential, as these technologies progressively influence the digital economy and change the standards of trust and engagement. Few of the available research addresses the role of demographic and psychographic characteristics, including age, gender, and personality traits. Although several research studies examine moderating variables such as wealth or social influence, a more thorough investigation of the interaction between individual variations and s-commerce predictors is essential. This would facilitate a deeper comprehension of consumer behavior and enable platforms to tailor their strategies successfully. Future studies should not only theorize the role of AI, blockchain, and VR, but also adopt empirical strategies such as scale development, controlled experiments, and multi-method approaches to generate validated indicators of their impact on trust and engagement.

This study was conducted by reviewing 30 articles that are related to the predictors of consumer trust and engagement in s-commerce. The study is limited to these articles. The findings of the study are limited to articles from Scopus and Web of Science. Other databases were not searched. In addition, although vote counting was used to identify the relative prominence of predictors, the lack of effect-size analysis or sensitivity testing means that the findings remain primarily descriptive; future meta-analytical work is recommended to provide stronger statistical generalizability.

As a way forward, the findings of this study indicated that there are still limited empirical studies on consumer trust and engagement. More studies are suggested for future work to enhance the understanding of consumer trust and engagement in s-commerce. Further, future studies are suggested, to be conducted in other emerging and developing countries. S-commerce has become widespread, and understanding the determinants of using s-commerce in developing countries is important for consumers and companies. Future studies are also suggested, to examine the proposed factors in this study. Lastly, the findings showed that only a limited percentage of studies have conducted mixed methods. Therefore, to enrich the understanding of trust and engagement in s-commerce, future studies are suggested, to conduct a mixed method by combining questionnaires with interviews with selected consumers to understand the trust and engagement process, as well as their predictors.

6. Contribution and Novelty of the Study

This study offers a significant contribution to the growing body of literature on s-commerce by presenting an integrated and thematically grounded framework that synthesizes the antecedents of consumer trust and engagement. Instead of just looking at separate factors or limited theories like previous reviews, this study uses a systematic literature review (SLR) and thematic analysis to group the factors into four main categories: technological, organizational, social, and motivational. A total of 18 sub-themes were extracted from 30 empirical studies published between 2019 and 2024, providing a comprehensive and up-to-date representation of the field.

The novelty of the study lies in its multi-theoretical integration and the development of a conceptual framework (Figure 5) that explicitly positions consumer trust as a mediating variable between antecedents and consumer engagement. This framework draws on several theoretical foundations, such as the TAM, UTAUT, Social Exchange Theory, Social Presence Theory, and Uses and Gratifications Theory, but departs from them by offering a more context-specific and behaviorally comprehensive model tailored for s-commerce environments. In addition to advancing theoretical understanding, the study provides important practical implications. The proposed framework enables platform developers, marketers, and policymakers to identify which factors are most influential in fostering consumer trust and engagement, allowing for more targeted strategies in platform design, content personalization, and community management. For example, enhancing system quality, ensuring information accuracy, and fostering social interaction can collectively strengthen user trust, which in turn promotes greater engagement, loyalty, and co-creation behaviors.

Furthermore, the study addresses key methodological gaps in the literature by highlighting the over-reliance on cross-sectional, quantitative methods and calling for more longitudinal, cross-cultural, and mixed-method research designs. This opens new avenues for future studies to empirically validate the proposed model and test the dynamic interplay between trust, engagement, and contextual factors across different consumer segments and technological environments. Overall, this study contributes to a conceptually integrative, empirically grounded, and theoretically distinct framework that advances understanding of consumer behavior in social commerce. It enriches academic discourse by bridging previously fragmented insights and provides a solid foundation for future empirical testing and policy development in digital commerce ecosystems.

7. Conclusions

This paper presents a thorough synthesis of the aspects affecting trust and consumer engagement in s-commerce, categorizing them into technological, organizational, social, and motivational factors. The investigation of critical sub-factors, including system quality, service quality, social impact, and perceived value, highlights the pivotal role of trust as a mediator in influencing user behavior. The study underscores several gaps in the literature, notably the absence of a cohesive framework that synthesizes these elements, inadequate investigation of nascent notions such as advanced technology, and a lack of focus on cultural and emotional aspects. The prevalence of cross-sectional and quantitative research in the area constrains the comprehension of the dynamic and intricate nature of trust and engagement.

While the review captured a wide range of predictors across four key themes (technological, organizational, social, and motivational), it remained focused on studies that met strict empirical and thematic relevance. To deepen the qualitative richness of the review, future work should consider integrating gray literature, practitioner insights, or longitudinal case studies that may offer nuanced understandings of trust-building mechanisms. Additionally, mixed-method synthesis techniques, such as qualitative comparative analysis (QCA), may help uncover causal patterns not evident in quantitative studies alone. This approach would enhance the comprehensiveness and interpretive power of future reviews.

While emerging technologies such as artificial intelligence (AI), blockchain, and virtual reality (VR) have been acknowledged as potential game-changers in the social commerce landscape, current research rarely moves beyond conceptual discussion. To enhance feasibility and methodological clarity, future studies should articulate measurable constructs and develop testable propositions for each technology. For AI, trust-related impacts could be operationalized through constructs such as perceived personalization accuracy, algorithmic transparency, and explainability [52], measured via Likert-scale items adapted from technology trust literature to assess perceived fairness, clarity of system recommendations, and user control. Blockchain in s-commerce can be operationalized through dimensions such as perceived transaction transparency, immutability, and security assurance [53], using indicators related to user confidence in fraud prevention, auditability, and decentralization benefits. For VR, immersive experience may be captured through constructs such as perceived spatial presence, sensory richness, and interactivity [54], integrated into engagement models informed by Social Presence Theory and flow theory.

Based on these operational definitions, future research could address the following targeted questions:

(1)

How does AI-driven personalization transparency influence trust formation and sustained engagement in s-commerce?

(2)

To what extent do blockchain-enabled transaction features moderate the relationship between perceived risk and consumer trust in s-commerce?

(3)

How does VR-based immersive shopping affect the pathways between social presence, engagement, and purchase intentions?

To move beyond a conceptual agenda, future studies should operationalize emerging technologies with measurable constructs and empirical designs. For AI, indicators such as algorithmic transparency, personalization accuracy, and explainability can be adapted from technology trust literature. For blockchain, constructs like transaction transparency, immutability, and security assurance can capture user confidence. For VR, spatial presence, sensory richness, and interactivity can be measured using validated immersive technology scales. Methodologically, survey-based SEM, field experiments, and longitudinal studies can transform these constructs into empirically testable pathways, strengthening the applicability of social commerce research.

Embedding these variables into existing theoretical frameworks—such as extending TAM with perceived transparency for AI, incorporating blockchain attributes into TOE’s technological dimension, or adapting Social Presence Theory to account for VR immersion—would strengthen both the conceptual and empirical rigor of future research. This would ensure that emerging technologies are not only conceptually relevant, but also methodologically viable for empirical testing.

Future studies should employ an interdisciplinary and integrative approach to develop the area, examining the interactions among the listed themes and including longitudinal, mixed-methods, and cross-cultural studies. The impact of new technologies, like blockchain and artificial intelligence, on improving trust and engagement requires more examination. Furthermore, tackling ethical and privacy issues while examining multi-platform dynamics might yield a more comprehensive insight into user behavior in s-commerce. Addressing these gaps will enable future research to enhance the creation of adaptable, inclusive, and user-centric s-commerce platforms that promote trust and sustained engagement. This research provides a crucial basis for scholars, professionals, and policymakers aiming to understand the intricacies of the changing digital business environment.

In light of the need for greater theoretical integration, methodological transparency, analytical depth, and forward-looking contributions, the present study has taken deliberate steps to strengthen these dimensions. The theoretical critique has been deepened by critically comparing the complementarities and tensions among TAM, UTAUT, TOE, and Social Exchange Theory, clarifying their contextual applicability in explaining trust and engagement in social commerce. Methodological transparency has been enhanced through the explicit inclusion of full search strings, database configurations, synonym mappings, and reproducibility protocols, ensuring that the systematic review process adheres to PRISMA 2020 guidelines. Analytical rigor has been improved through the application of quantitative synthesis techniques, including vote counting and frequency analysis, to assess the relative importance of variables across studies. Furthermore, the cross-cultural and industry-level comparative analysis has been expanded, providing nuanced insights into how cultural and sectoral contexts shape trust and engagement mechanisms. The future research agenda has also been refined by operationalizing constructs for emerging technologies such as AI, blockchain, and VR, and by proposing specific, testable research questions to facilitate empirical validation. Collectively, these refinements not only address the identified limitations, but also enhance the robustness, applicability, and impact of the study, making it a stronger contribution to both academic scholarship and practical advancement in social commerce research.

Author Contributions

Conceptualization, A.A.and N.A.G. and S.H.; Methodology, A.A.and N.A.G. and S.H.; Formal analysis, A.A. and N.A.G. and S.H.; Writing—original draft preparation, A.A.; Writing—review and editing, N.A.G. and S.H.; Visualization A.A.; Supervision, N.A.G. and S.H.; All authors have read and agreed to the published version of the manuscript.