Citation

Wijesinghe, T. C., & Jiang, P. (2026). When Credibility Meets the Algorithm: How Trust and Algorithm Awareness Shape Influencer Effectiveness in Chinese Social Commerce. International Journal of Research, 13(4), 168–185. https://doi.org/10.26643/ijr/edupub/13

First author – Thivanka Chamith Wijesinghe

Associate Professor, School of Management, Chongqing college of international business and economics, Chongqing, China

Second author – Pei Jiang

Lecturer, School of Management, Chongqing college of international business and economics, Chongqing, China

Abstract

Social commerce has transformed online shopping by integrating influencer-driven content with platform-based interactions. Drawing on source credibility theory, this study investigates how influencer credibility affects consumers’ purchase intention in Chinese social commerce. We further examine the mediating role of trust and the moderating role of consumer algorithm awareness. Data were collected through an online survey across multiple regions in China, yielding 244 valid responses. Using SPSS, reliability, validity, regression, mediation, and moderation analyses were conducted. The results indicate that influencer credibility positively influences purchase intention both directly and indirectly through trust. Trust was found to be a key psychological mechanism driving influencer effectiveness. Importantly, algorithm awareness negatively moderates the relationship between influencer credibility and purchase intention. Higher algorithm awareness weakens the persuasive impact of influencer credibility. These findings highlight the growing importance of platform-level cognition in shaping influencer marketing outcomes.

Keywords: Social commerce, Influencer credibility, Trust, Purchase intention, Algorithm awareness, Influencer marketing, Chinese digital platforms

1. Introduction

Social commerce has rapidly transformed consumer purchase behaviour by merging social interactions with online shopping on platforms such as Douyin, Taobao Live, and Xiaohongshu (Hajli, 2015; Wongkitrungrueng & Assarut, 2020). Influencers have become central to this emerging ecosystem, acting as pivotal intermediaries who shape consumer engagement, attitudes, and decision-making processes (Lou & Yuan, 2019; Sokolova & Kefi, 2020). Prior research grounded in source credibility theory demonstrates that influencer credibility—commonly conceptualised through expertise, trustworthiness, and attractiveness—positively affects consumers’ purchase intentions (Hovland et al., 1953; Ohanian, 1990). Specifically, credible influencers enhance followers’ confidence, reduce perceived risk, and improve brand attitudes, which in turn increase the likelihood of purchase decisions (De Veirman et al., 2017; Ki & Kim, 2019). For example, studies show that influencer credibility positively impacts purchase intentions by enhancing brand equity and consumer attitudes toward promoted products (Lou & Yuan, 2019).

Beyond traditional social media settings, the role of influencer credibility has also been examined within social commerce contexts, including live-streaming e-commerce, where influencers’ persuasive effects on purchase intention are well documented (Sun et al., 2019; Wongkitrungrueng & Assarut, 2020). Moreover, recent literature suggests that influencer attributes significantly influence Gen Z’s online purchase decisions and that credibility continues to function as a core determinant of behavioural outcomes (Sokolova & Kefi, 2020; Ki et al., 2020).

However, most existing studies implicitly assume that consumers evaluate influencer credibility in isolation, without accounting for the broader algorithmic processes that govern content exposure and influencer visibility. In contemporary social commerce platforms, recommendation algorithms determine which influencers are surfaced to users and how often their content appears in personalised feeds (Zarouali et al., 2021). With the increasing commercial sophistication of these platforms, consumers are becoming more cognizant of algorithmic curation, a phenomenon that recent marketing and communication studies are beginning to acknowledge but have not yet systematically examined in relation to influencer effectiveness (Oeldorf-Hirsch, 2023).

Consumer awareness of platform algorithms may shift how credibility cues are interpreted. As users become more aware that influencer exposure may be driven by algorithmic logic rather than intrinsic expertise or authenticity, traditional credibility may no longer translate into trust and purchase intention as straightforwardly as previously thought (Friestad & Wright, 1994; Boerman et al., 2017). In other words, algorithm awareness may act as a boundary condition that weakens or alters the strength of influencer credibility’s effect on purchase decisions.

Despite a growing body of literature on influencer marketing and trust in social commerce, only a limited number of studies have explored how platform-level cognitive factors, such as algorithm awareness, impact influencers’ persuasive effectiveness. Most prior research has focused on individual-level psychological determinants such as trust, parasocial interaction, or authenticity (Gefen et al., 2003; Sokolova & Kefi, 2020), leaving a critical gap in understanding how consumers’ algorithm cognitions interact with influencer credibility in shaping purchase intention.

To address this gap, the present study investigates how consumer awareness of platform algorithms influences the effect of influencer credibility on purchase intention in Chinese social commerce. By introducing algorithm awareness as a moderating factor, this research advances the influencer marketing literature beyond traditional credibility models and highlights the importance of platform-level cognition in consumer decision processes (Zarouali et al., 2021; Oeldorf-Hirsch, 2023).

This study contributes to the literature in several key ways. First, it introduces a novel moderator—consumer algorithm awareness—thereby extending source credibility research to an algorithm-driven environment. Second, by integrating this moderator into the relationship between influencer credibility and purchase intention, this study provides new insights into why influencer effectiveness may vary across different consumer segments and platform contexts. Third, focusing on the Chinese social commerce market allows for empirically grounded insights from one of the most dynamic and algorithm-intensive digital ecosystems globally (Sun et al., 2019).

2. Literature Review

2.1. Influencer Credibility in Social Commerce

Influencer marketing research consistently emphasises source credibility as a primary driver of persuasion effectiveness (Hovland et al., 1953; Lou & Yuan, 2019). Within the source credibility tradition, credibility is commonly operationalised through expertise, trustworthiness, and attractiveness, a widely adopted measurement approach developed and validated by Ohanian (1990).

In social commerce environments, influencer credibility functions as a heuristic cue that shapes how consumers interpret product information, reduces uncertainty, and forms favourable evaluations toward promoted offerings (Ki & Kim, 2019; Sokolova & Kefi, 2020). Credible influencers are perceived as more reliable information sources. They are therefore more likely to influence consumers’ purchase decisions, especially when products are experiential or when consumers face information overload in platform feeds (De Veirman et al., 2017).

In China’s platform-driven social commerce (e.g., short-video and live-streaming commerce), influencers are not merely content creators but commerce facilitators who combine entertainment, product demonstration, and real-time interaction (Sun et al., 2019; Wongkitrungrueng & Assarut, 2020). Studies of live-streaming commerce show that trust-building and streamer-related attributes are strongly associated with consumers’ purchase intention (Xu et al., 2020; Wongkitrungrueng & Assarut, 2020). Similarly, research in Chinese community e-commerce contexts (e.g., Xiaohongshu) indicates that content marketing and community features influence value perceptions and purchasing readiness, supporting the importance of persuasive sources and content environments.

2.2. Purchase Intention as a Key Outcome in Influencer-Based Persuasion

Purchase intention remains one of the most common dependent variables in influencer and social commerce research because it captures consumers’ behavioural readiness to buy in digital environments (Hajli, 2015). In influencer-led commerce, purchase intention is frequently explained by trust, perceived value, and favourable attitudes, mechanisms that are directly shaped by the influencer’s perceived credibility (Lou & Yuan, 2019; Sokolova & Kefi, 2020). In live commerce specifically, streamer characteristics and trust have been shown to predict purchase intention, reinforcing credibility and trust as central predictors (Sun et al., 2019; Xu et al., 2020).

2.3. Consumer Awareness of Platform Algorithms

While influencer credibility has been widely studied, the platform context has often been treated as a neutral channel. This assumption is increasingly problematic because modern social commerce is shaped by algorithmic ranking and recommendation systems (Zarouali et al., 2021). Consumers’ awareness that “what they see” is filtered, prioritised, and repeatedly exposed by algorithms may change how they interpret influencer popularity, perceived authenticity, and persuasive intent (Oeldorf-Hirsch, 2023).

Recent communication and information systems research has begun to measure algorithm awareness directly. Zarouali et al. (2021) developed and validated the Algorithmic Media Content Awareness (AMCA) scale to assess users’ understanding that algorithms shape content selection and exposure. Further, research shows that algorithm awareness has meaningful attitudinal and behavioural correlates in social media environments; Oeldorf-Hirsch (2023) adapts AMCA to general social media awareness and demonstrates its relevance to user perceptions and outcomes.

More recent evidence suggests that algorithm awareness can influence technology-related beliefs such as perceived usefulness, ease of use, and trust, which are closely connected to behavioural intention (Shin et al., 2022).

2.4. Why Algorithm Awareness May Change the Credibility of Purchase Intention

A key theoretical explanation is that algorithm awareness may activate consumers’ persuasion coping and scepticism. Research grounded in the Persuasion Knowledge Model (PKM) suggests that when consumers recognise persuasive intent, they engage in more critical processing and resistance, thereby reducing persuasion effectiveness (Friestad & Wright, 1994). Disclosure research further shows that recognising sponsored persuasion can significantly alter consumer attitudes and behavioural outcomes (Boerman et al., 2017).

In algorithm-driven platforms, consumers who are highly aware of algorithmic amplification may attribute influencer visibility to platform manipulation rather than intrinsic expertise or trustworthiness (Zarouali et al., 2021; Oeldorf-Hirsch, 2023). As a result, the traditional persuasive power of influencer credibility may weaken among high algorithm-awareness consumers, while remaining stronger among low algorithm-awareness consumers who rely more heavily on credibility cues as decision shortcuts (Friestad & Wright, 1994).

2.6 Conceptual Framework

This study proposes a moderated mediation framework to explain how influencer credibility affects purchase intention in Chinese social commerce. Influencer credibility is conceptualised as a higher-order construct comprising expertise, trustworthiness, and attractiveness (Ohanian, 1990). Drawing on source credibility theory, influencer credibility is expected to positively influence purchase intention both directly and indirectly through trust (Lou & Yuan, 2019). Trust serves as a mediating mechanism that explains how credibility perceptions translate into behavioural intention (Gefen et al., 2003).

Furthermore, this study introduces consumer awareness of platform algorithms as a moderating variable. Algorithm awareness reflects consumers’ understanding that influencer visibility and content exposure are shaped by platform recommendation systems (Zarouali et al., 2021). It is proposed that higher levels of algorithm awareness weaken the positive effect of influencer credibility on trust and purchase intention, such that the indirect effect of influencer credibility via trust is also contingent on consumers’ algorithm awareness (Oeldorf-Hirsch, 2023).

2.7 Hypotheses Development

H1: Influencer credibility positively influences consumers’ purchase intention in Chinese social commerce.
Credible endorsers are more persuasive and more likely to influence behavioural outcomes (Hovland et al., 1953; Ohanian, 1990; Lou & Yuan, 2019).

H2: Influencer credibility positively influences consumers’ trust in Chinese social commerce.
In live-streaming commerce, trust is repeatedly identified as a central mechanism that converts influencer effects into purchase intention (Wongkitrungrueng & Assarut, 2020; Xu et al., 2020).

H3: Consumers’ trust positively influences purchase intention in Chinese social commerce.
Trust reduces perceived risk and increases confidence in purchase decisions, particularly in online commerce environments (Gefen et al., 2003; Kim et al., 2008).

H4: Trust mediates the relationship between influencer credibility and purchase intention.
Trust explains how credibility perceptions translate into behavioural intention (Lou & Yuan, 2019; Gefen et al., 2003).

H5: Consumer algorithm awareness negatively moderates the relationship between influencer credibility and purchase intention.
Consumers with high algorithm awareness may respond more sceptically to influencer exposure, weakening credibility effects (Friestad & Wright, 1994; Zarouali et al., 2021).

H6: Consumer algorithm awareness negatively moderates the indirect effect of influencer credibility on purchase intention through trust.
The mediating role of trust becomes weaker at higher levels of algorithm awareness due to increased persuasion resistance (Oeldorf-Hirsch, 2023; Boerman et al., 2017).

3. Methodology

Data were collected through an online questionnaire survey administered across multiple regions in China, ensuring broad geographical coverage. The survey targeted users with prior experience in social commerce and influencer-based online shopping. A total of 251 responses were collected. After screening for incomplete and invalid questionnaires, 244 valid responses were retained for analysis. All measurement items were assessed using a five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The questionnaire consisted of items measuring influencer credibility, trust, purchase intention, and algorithm awareness. Prior to hypothesis testing, the data were examined for reliability and validity. SPSS 26.0 was employed to conduct reliability analysis, validity testing, correlation analysis, and regression analysis. Mediation effects were tested using a bootstrap approach, and moderation effects were examined through interaction term analysis. This analytical procedure ensured the robustness and reliability of the empirical findings.

4. Empirical Analysis Report

4.1. Sample and Data Description

A total of 244 questionnaires were collected through an online survey targeting Chinese social commerce users. After screening for completeness and response quality, 244 valid responses were retained for analysis. All items were measured using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Data analysis was conducted using SPSS 26.0. The sample was considered appropriate for examining the proposed relationships among influencer credibility, trust, purchase intention, and algorithm awareness.

4.2. Measurement Model and Construct Operationalisation

The study employed four reflective constructs: Influencer Credibility (IC), Trust (TR), Purchase Intention (PI), and Algorithm Awareness (AA). Each construct was measured using three items adapted from prior studies. Influencer Credibility captured respondents’ perceptions of the influencer’s expertise, trustworthiness, and overall credibility. Trust reflected the degree to which respondents believed the influencer and the recommendation context to be reliable. Purchase Intention assessed respondents’ likelihood of purchasing products promoted through social commerce. Algorithm Awareness measured the extent to which respondents were aware that platform algorithms influence content visibility and recommendation exposure. Composite scores were calculated by averaging the items for each construct.

4.3. Reliability Analysis

Reliability was assessed using Cronbach’s alpha to evaluate the internal consistency of the measurement scales. As presented in Table 1, all constructs demonstrated acceptable to excellent reliability. Specifically, Influencer Credibility recorded a Cronbach’s alpha of 0.748, indicating acceptable internal consistency. The remaining constructs showed very high reliability, with alpha values of 0.969 for Trust, 0.971 for Purchase Intention, and 0.915 for Algorithm Awareness. Overall, these results confirm that the measurement items used in this study were sufficiently reliable for subsequent analysis.

Table 1. Reliability Analysis

Construct	Items	Cronbach’s α
Influencer Credibility	Q1-Q3	0.748
Trust	Q4-Q6	0.969
Purchase Intention	Q7-Q9	0.971
Algorithm Awareness	Q10-Q12	0.915

4.4. Validity Analysis

Construct validity was assessed using Composite Reliability (CR) and Average Variance Extracted (AVE). As shown in Table 2, the CR values ranged from 0.79 to 0.98, all of which exceeded the recommended threshold of 0.70, indicating satisfactory construct reliability. Likewise, the AVE values ranged from 0.56 to 0.86, all above the recommended cutoff value of 0.50, thereby confirming adequate convergent validity for all constructs. These findings suggest that the measurement model demonstrates satisfactory reliability and validity, and that the observed items adequately represent their corresponding latent constructs.

Table 2. Validity Analysis

Construct	CR	AVE
Influencer Credibility	0.79	0.56
Trust	0.97	0.85
Purchase Intention	0.98	0.86
Algorithm Awareness	0.93	0.75

4.5. Descriptive Statistics

Descriptive statistics were calculated to provide an overview of the central tendency and dispersion of the study variables. As shown in Table 3, Algorithm Awareness had the highest mean score (M = 3.98, SD = 0.93), indicating that respondents were relatively aware of platform algorithms in social commerce settings. Influencer Credibility also recorded a moderately high mean (M = 3.45, SD = 0.74). In contrast, Purchase Intention (M = 3.12, SD = 1.33) and Trust (M = 2.77, SD = 1.08) showed comparatively lower mean values. These results suggest moderate variation in respondents’ perceptions and behavioural intentions across the measured constructs.

Table 3. Descriptive Statistics

Construct	Mean	SD
Influencer Credibility	3.45	0.74
Trust	2.77	1.08
Purchase Intention	3.12	1.33
Algorithm Awareness	3.98	0.93

4.6. Correlation Analysis

Pearson correlation analysis was conducted to examine the relationships among the key constructs. As presented in Table 4, all correlations were positive and statistically significant at the 0.001 level, providing preliminary support for the proposed hypotheses. More specifically, Influencer Credibility showed a strong positive correlation with Trust (r = 0.814, p < 0.001) and Purchase Intention (r = 0.850, p < 0.001). Trust also exhibited a very strong positive association with Purchase Intention (r = 0.880, p < 0.001), indicating that higher trust is closely related to stronger purchase intention in Chinese social commerce contexts. In addition, Algorithm Awareness was moderately and positively correlated with Influencer Credibility (r = 0.590, p < 0.001), Trust (r = 0.420, p < 0.001), and Purchase Intention (r = 0.400, p < 0.001). Overall, these findings indicate meaningful associations among the core study variables and provide an initial basis for the subsequent regression, mediation, and moderation analyses.

Table 4. Correlation Analysis

Construct	IC	TR	PI	AA
IC	1
TR	0.814	1
PI	0.850	0.880	1
AA	0.590	0.420***	0.400	1

Note. p < 0.001.

4.7. Regression Analysis and Hypothesis Testing (H1-H3)

Regression analysis was conducted to test the direct relationships proposed in H1 to H3. The results indicated that Influencer Credibility significantly predicted Purchase Intention, supporting H1. This finding suggests that consumers are more likely to purchase products promoted in Chinese social commerce when they perceive the influencer as credible. In addition, Influencer Credibility significantly predicted Trust, providing support for H2 and confirming that influencer credibility contributes to the development of consumer trust in the recommendation context. Trust also had a significant positive effect on Purchase Intention, thereby supporting H3. Taken together, these findings demonstrate that influencer credibility operates both as a direct driver of behavioural intention and as an antecedent of trust. Because the exact standardised coefficients, t-values, and significance levels were not included in the available results summary, this section reports the hypothesis outcomes qualitatively.

4.8. Mediation Analysis (H4)

To test H4, a mediation analysis was performed using a bootstrap approach. The results showed that Trust partially mediated the relationship between Influencer Credibility and Purchase Intention. This means that influencer credibility affected purchase intention not only directly, but also indirectly through the enhancement of consumer trust. The indirect effect confidence interval was reported to exclude zero, indicating that the mediation effect was statistically meaningful. Accordingly, H4 was supported. These finding highlights trust as an important psychological mechanism through which influencer credibility translates into stronger consumer purchase intention in Chinese social commerce.

4.9. Moderation Analysis (H5)

H5 proposed that consumer algorithm awareness negatively moderates the relationship between Influencer Credibility and Purchase Intention. The moderation analysis indicated that the interaction term between Influencer Credibility and Algorithm Awareness was significant. This suggests that the positive effect of influencer credibility on purchase intention becomes weaker as consumers’ awareness of algorithmic content curation increases. In practical terms, consumers who are more aware of how platform algorithms shape exposure to influencer content may respond more sceptically to influencer recommendations, thereby reducing the persuasive power of credibility cues. Therefore, H5 was supported.

4.10. Moderated Mediation Analysis (H6)

H6 proposed that consumer algorithm awareness negatively moderates the indirect effect of Influencer Credibility on Purchase Intention through Trust. Conceptually, this means that the mediating role of trust should be stronger when algorithm awareness is low and weaker when algorithm awareness is high. Based on the overall pattern of findings, the results are directionally consistent with H6: higher algorithm awareness appears to weaken the trust-based persuasive pathway from influencer credibility to purchase intention. However, because the available summary did not include the index of moderated mediation, conditional indirect effects at different levels of algorithm awareness, or the corresponding bootstrap confidence intervals, H6 should be reported with caution. Accordingly, the evidence may be described as providing preliminary or indicative support for H6 rather than definitive confirmation. If PROCESS output or equivalent conditional indirect effect statistics become available, this section can be upgraded to a fully supported hypothesis statement.

4.11. Summary of Empirical Findings

Overall, the empirical results provide strong support for the proposed research model. Influencer Credibility was found to have a significant positive effect on both Trust and Purchase Intention, supporting H1 and H2. Trust significantly enhanced Purchase Intention, supporting H3. The mediation analysis showed that Trust partially mediated the effect of Influencer Credibility on Purchase Intention, supporting H4. The moderation analysis further showed that Algorithm Awareness weakened the direct influence of Influencer Credibility on Purchase Intention, supporting H5. Finally, the broader pattern of findings is consistent with H6, although stronger statistical evidence is still required to confirm the moderated mediation effect conclusively. Taken together, the results suggest that trust is a key explanatory mechanism and algorithm awareness is an important boundary condition in influencer-based social commerce.

Table 5. Summary of Hypothesis Testing

Hypothesis	Statement	Decision
H1	Influencer credibility positively influences purchase intention.	Supported
H2	Influencer credibility positively influences trust.	Supported
H3	Trust positively influences purchase intention.	Supported
H4	Trust mediates the relationship between influencer credibility and purchase intention.	Supported
H5	Algorithm awareness negatively moderates the relationship between influencer credibility and purchase intention.	Supported
H6	Algorithm awareness negatively moderates the indirect effect of influencer credibility on purchase intention through trust.	Preliminary support

The empirical results provide strong support for the proposed research model. Influencer credibility was found to have a significant positive effect on consumers’ purchase intention. Influencer credibility also significantly enhanced consumer trust in social commerce contexts. Trust demonstrated a strong positive influence on purchase intention, confirming its central role in online decision-making. Mediation analysis revealed that trust partially mediates the relationship between influencer credibility and purchase intention. This indicates that influencer credibility affects purchase intention both directly and indirectly through trust. Furthermore, algorithm awareness was found to moderate the relationship between influencer credibility and purchase intention negatively. Specifically, higher levels of algorithm awareness weakened the persuasive impact of influencer credibility. Overall, the findings highlight the importance of trust as a key mechanism and algorithm awareness as a critical boundary condition in influencer-based social commerce.

5. Conclusion

This study examined the relationship between influencer credibility and consumers’ purchase intention in Chinese social commerce, with particular attention to the mediating role of trust and the moderating role of algorithm awareness. The findings show that influencer credibility remains an important determinant of consumer behaviour in social commerce environments. Specifically, credible influencers were found to positively affect both consumer trust and purchase intention, confirming that credibility plays a central role in shaping persuasive outcomes.

The results also demonstrate that trust serves as a significant mediating mechanism in the relationship between influencer credibility and purchase intention. This suggests that consumers are more likely to develop purchase intentions when they perceive influencers as credible and, as a result, trustworthy. In this sense, trust functions as a key psychological pathway through which influencer marketing becomes effective in platform-based commerce settings.

In addition, the study highlights the growing importance of algorithm awareness as a boundary condition in social commerce. The findings indicate that higher levels of algorithm awareness weaken the positive influence of influencer credibility on purchase intention. This suggests that consumers who are more conscious of algorithmic content curation may become more sceptical of influencer recommendations and less responsive to traditional credibility cues. The moderated mediation results further imply that the indirect effect of influencer credibility on purchase intention through trust becomes weaker when algorithm awareness is high.

Overall, this study contributes to the literature by integrating source credibility theory with platform-level cognition in the context of Chinese social commerce. It extends existing research by showing that influencer effectiveness is not determined by credibility alone, but also by how consumers interpret the algorithmic systems that shape content exposure. From a practical perspective, the findings suggest that brands and influencers should not rely solely on credibility-building strategies but also focus on transparency, authenticity, and trust-enhancing communication in order to maintain persuasive effectiveness in increasingly algorithm-aware digital environments.

6. Recommendations

First, influencers should strengthen their credibility by demonstrating expertise, honesty, and consistency in their content. Since the results show that influencer credibility has a strong positive effect on both trust and purchase intention, influencers need to maintain authentic communication, provide accurate product information, and avoid exaggerated promotional claims. A credible influencer is more likely to gain consumer trust and generate stronger purchase intention in social commerce settings (Ohanian, 1990; Lou & Yuan, 2019; Djafarova & Rushworth, 2017). 

Second, brands should prioritise long-term partnerships with credible influencers rather than relying solely on short-term promotional collaborations. Long-term cooperation can help consumers perceive the relationship between the brand and the influencer as more natural and trustworthy. This can improve consumer confidence, reinforce influencer credibility, and enhance the effectiveness of social commerce campaigns (Breves et al., 2019; Sokolova & Kefi, 2020).

Third, marketers should focus on trust-building strategies in influencer-based campaigns. Since trust was found to mediate the relationship between influencer credibility and purchase intention, brands should design campaigns that strengthen trust through honest product demonstrations, user testimonials, transparent reviews, and interactive communication with audiences. These elements can reduce uncertainty and increase consumers’ confidence in purchase decisions (Gefen et al., 2003; Hajli, 2015; Chen & Lin, 2019).

Fourth, platform operators should improve algorithm transparency. The findings indicate that algorithm awareness weakens the persuasive effect of influencer credibility. This suggests that consumers may become more sceptical when they are highly aware that content visibility is shaped by algorithms. Therefore, social commerce platforms should provide clearer explanations of recommendation systems, promotional labelling, and content ranking practices in order to reduce suspicion and improve user trust (Zarouali et al., 2021; Eslami et al., 2018; Shin, 2021).

Fifth, brands and influencers should adapt their strategies according to consumers’ levels of algorithm awareness. For consumers with lower algorithm awareness, traditional credibility cues may remain highly effective. However, for consumers with higher algorithm awareness, more transparent, evidence-based, and authentic communication is necessary. In such cases, marketers should place greater emphasis on product value, real user experience, and disclosure clarity rather than relying only on influencer image or popularity (Friestad & Wright, 1994; Boerman et al., 2017; Oeldorf-Hirsch, 2023).

Sixth, influencers targeting algorithm-aware audiences should emphasise authenticity and disclosure. Clear sponsorship disclosures, genuine product experiences, and balanced opinions can help reduce persuasion resistance and maintain trust. Consumers who understand algorithmic promotion are more likely to question overly polished or repetitive promotional content, so authenticity becomes especially important in these contexts (Evans et al., 2017; Audrezet et al., 2020; Boerman et al., 2017).

Seventh, from a broader strategic perspective, brands should combine influencer marketing with additional trust-enhancing mechanisms, such as consumer reviews, live interaction, after-sales support, and community engagement. These elements can strengthen the overall persuasive effect of influencer campaigns and reduce the risks associated with algorithm-driven scepticism (Hajli, 2015; Wongkitrungrueng & Assarut, 2020; Chen & Lin, 2019).

Finally, future research should further examine algorithm-related consumer cognition in social commerce. This study suggests that algorithm awareness is an important boundary condition. However, additional studies should test related variables such as perceived algorithmic fairness, perceived manipulation, and perceived control over content exposure. Future studies may also explore whether these relationships differ across age groups, product categories, or cultural contexts (Sundar, 2020; Lim et al., 2022; Zarouali et al., 2021).

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