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Abstract

With the advancement of social media, influencer content credibility can potentially drive consumer participation in brand co-creation. However, the impact of influencer content credibility on brand co-creation is underexplored, particularly the mechanism and process by which influencer content credibility impacts brand co-creation. Thus, this study employs social exchange theory and social presence theory to investigate how influencer content credibility affects brand co-creation, considering the mediating effect of consumers’ psychological well-being and the moderating effect of social presence. The study employed a 2 (influencer content credibility: high vs. low) × 2 (social presence: high vs. low) between-subjects experimental design, which enabled the establishment of cause-effect relationships by involving 213 respondents. Data were analyzed using an independent samples T-test, PLS -SEM, and a two-way ANOVA. The findings reveal that high influencer content credibility has a significant impact on brand co-creation. Besides, consumers’ psychological well-being partially mediates the effect of influencer content credibility on brand co-creation. Moreover, social presence moderates the impact of influencer content credibility on brand co-creation, such that the likelihood of consumers’ willingness to co-create brands is higher when high influencer content credibility is aligned with low social presence. Therefore, the theoretical integration in this study contributes to understanding influencer content credibility’s mechanism and conditional impacts on brand co-creation by highlighting the mediating role of psychological well-being and the moderating role of social presence in this process. The study offers practical insights for marketers on how to utilize influencer credible content and social presence to enhance brand co-creation while improving consumers’ psychological well-being.

Plain language summary

How influencer content shape brand co-creation

This study employs social exchange theory and social presence theory to investigate how influencer content credibility affects brand co-creation, considering the mediating effect of consumers’ psychological well-being and the moderating effect of social presence. The study employed between-subjects experimental design, which enabled the establishment of cause-effect relationships by involving 213 respondents. The findings reveal that high influencer content credibility has a significant impact on brand co-creation. Besides, consumers’ psychological well-being partially mediates the effect of influencer content credibility on brand co-creation. Moreover, social presence moderates the impact of influencer content credibility on brand co-creation, such that the likelihood of consumers’ willingness to co-create brands is higher when high influencer content credibility is aligned with low social presence. The study offers practical insights for marketers on how to utilize influencer credible content and social presence to enhance brand co-creation while improving consumers’ psychological well-being.

Keywords

influencers credibility social presence brand co-creation

Introduction

The rise of influencers has changed the marketing landscape, especially how brands interact with their target consumers. Influencers are individuals with influence within a community of users of social media platforms, gaining influence through constantly sharing credible content about a specific topic (Farivar & Wang, 2022; Lou & Yuan, 2019). Influencers facilitate consumers in making purchase decisions (Han & Balabanis, 2024; Mrisha & Xixiang, 2024) and improve their well-being (S. Bhardwaj et al., 2024; Jamil et al., 2024; Pilgrim & Bohnet-Joschko, 2019). Besides, marketers collaborate with credible influencers to promote brands and enhance consumer-brand attachment (Vrontis et al., 2021). Additionally, influencers have the potential to facilitate consumer brand co-creation (BC) (Ju et al., 2021; Simmons & Durkin, 2023). For example, fashion brands like Zara collaborate with credible fashion influencers to promote their brands and co-create with consumers (Lindsay, 2021). BC refers to intentional, active, and interactive consumer behavioral activities associated with the consumer-brand relationship, resulting in more brand value (France et al., 2018; Hatch & Schultz, 2010). BC is essential for improving firm competitiveness (France et al., 2015). Thus, by collaborating with influencers, firms can improve their competitiveness through enhancing BC. However, the mechanism and process by which influencers can enhance consumer BC remains challenging for scholars and practitioners. Thus, a comprehensive understanding of how and when influencers affect BC remains necessary.

While there is progress in the literature on influencers and consumer co-creation aspects, most studies have focused on influencer attributes and value co-creation in general. How influencer content aspects shape co-creation from a branding perspective has remained unknown (see Table 1). For example, Bu, Thaichon, and Carlini (2022) report significant impacts of influencer credibility on customer value co-creation behavior. Similarly, Bu, Parkinson, and Thaichon (2022a) found that homophily (perceived similarity between influencer and consumers) impacts consumers’ value co-creation behavior. Additionally, live streamers’ credibility and influencer authenticity affect consumers’ decision to engage in value co-creation (Hasan et al., 2024; Y. Zhang & Prebensen, 2025). Thus, from the literature, it’s evident that little is understood about BC in the influencer marketing context (Nibras et al., 2025; Simmons & Durkin, 2023). BC is a subset of value co-creation focusing on co-creating brands (Nibras et al., 2025; Ramaswamy & Ozcan, 2016; Sarasvuo et al., 2022; Simmons & Durkin, 2023). BC involves consumers collaborating with brands to create brand identity, meanings, emotional attachments, and experiences. Whereas value co-creation concerns customer involvement in creating value for the entire service or product in different service touchpoints (Vargo & Lusch, 2004; Yi & Gong, 2013). A few available studies on BC provide insights into the effect of influencer social presence (SP) (Kilumile & Zuo, 2024) and influencer authenticity (Hasan et al., 2024), yet overlook the content aspects of an influencer. Thus, presents a knowledge gap on the interplay of influencer content aspects and BC. Investigating underlying mechanisms and the conditional effect of influencer content on BC remains essential to marketers. Marketers need insights on how and when to strategically leverage influencer content in driving BC for enhanced firm competitiveness.

Table 1.

Summary of Recent Key Studies.

Source	Methods	Relationship	Findings
Bu, Thaichon, and Carlini (2022)	Survey	Influencer credibility → consumer value co-creation behavior	Influencer credibility impacts consumer value co-creation behavior.
Kilumile and Zuo (2024)	Survey	Social presence → brand co-creation	Social presence significantly affects brand co-creation.
Bu, Parkinson, and Thaichon (2022a)	Survey	Homophily → consumer value co-creation behavior	Homophily significantly impacts consumer value co-creation behavior.
Jamil et al. (2024)	Survey	Source credibility and kindness → consumer well-being	Source credibility and kindness affect consumer well-being.
Qayyum et al. (2023)	Survey	Inclusive advertising → consumer well-being	Inclusive influencer advertising significantly affects eudaimonic and hedonic consumer well-being.
Y. Zhang and Prebensen (2025)	Survey	Live streamer credibility → consumer value co-creation behavior	Credibility traits of tourism live streamers impact customer co-creation behaviors.
Hasan et al. (2024)	Survey	Influencer authenticity → intention to co-create brand value	Influencer authenticity impact consumers’ intention to co-create brand value.
Bu, Parkinson, and Thaichon (2022b)	Experiment	Endorsement disclosure → customer value co-creation behavior	The effect of explicit disclosure on value co-creation behavior (participation behavior and citizenship behavior) is higher than when endorsement is either implied or not disclosed.
Gokce et al. (2023)	Survey	Social media content → value co-creation	Social media content about food significantly affects value co-creation behavior.
Ju et al. (2021)	Systematic Literature Review	Influencers can enhance the co-creation of brands and call for further investigation.

Influencers may significantly foster consumer participation in BC by turning passive followers into active brand co-creators (Ju et al., 2021). Consumers interact with influencers’ content on social media platforms (Lou & Yuan, 2019). The interaction goes beyond liking the content on the influencers’ pages. Consumers recommend the endorsed brand to others, compliment the endorsed brand, participate in brand-related contests, and provide feedback about the brand. Accordingly, such activities translate into BC, where consumers become active in co-creating the brand identity, meaning, and experiences (France et al., 2018, 2020). Although influencers may play a significant role in fostering BC, research on the effect of influencers on BC is scarce (Ju et al., 2021; Le et al., 2022; Simmons & Durkin, 2023). Thus, there is a knowledge gap, particularly regarding the effect of influencer content credibility (ICC) on BC and its underlying mechanisms and processes.

To address the research gaps, we employ social exchange theory (Blau, 1964; Homans, 1974) and social presence theory (Short et al., 1976) to investigate how and when ICC impacts BC. Based on two theoretical lenses, we propose that the effect of ICC on BC is mediated by consumers’ psychological well-being (PWB) and moderated by social presence (SP). Consumers’ PWB involves individual self-acceptance, self-growth, positive relations with others, and confidence (Jiang et al., 2019; Rahmani et al., 2018; Ryan & Deci, 2001; Ryff & Keyes, 1995). Individuals seek to improve their PWB by interacting with others using their skills and communication abilities (Çikrıkci, 2016; Ryff & Keyes, 1995). Consumers gain PWB when interacting with credible influencers on social media platforms (Jamil et al., 2024; Pilgrim & Bohnet-Joschko, 2019). Furthermore, according to social exchange theory (Blau, 1964; Homans, 1974); Human behavior is explained by transactions involving exchanging resources, benefits and costs between two or more people. Hence, influencers’ credible content about the endorsed brand offers important resources. In return, consumers may reciprocate by co-creating the endorsed brand through sharing endorsed brand content, making suggestions to improve the endorsed brand, and complimenting the brand. Also, M. Kim and Kim (2020) suggest that the reciprocity intensity of consumers (participating in BC) depends on the beneficial resources offered by the influencer (including content that improves PWB) to consumers. Therefore, in this study, we suggest that ICC affects consumers’ PWB and subsequently affects BC. Thus, progressing the literature by explaining the mechanism and process by which ICC impacts BC. Subsequently, offering insights to marketers about leveraging ICC to design influencer marketing campaigns that foster BC while improving consumers’ PWB.

Furthermore, to understand the conditions under which ICC impacts BC, the SP of an influencer is proposed as a moderating variable. Social presence theory suggests that the persuasiveness of the communication medium depends on the extent to which the channel induces SP or the perception of being in the same physical space as other people. Recent studies report that the SP of an influencer significantly affects BC (Kilumile & Zuo, 2024). On social media platforms, influencers can develop a sense of SP by quickly responding to comments, sharing intimate personal life stories, and live streaming. The SP consolidated on the influencers’ page may translate into BC. Thus, we suggest that SP moderates the effect of ICC on BC. Investigating the moderation effects of SP offers a novel understanding of circumstances in which ICC impacts BC.

Therefore integrating the theories contributes to the literature by highlighting the mediating role of consumers’ PWB and the moderating role of social presence in this process. The study suggests that ICC is an important resource in facilitating exchange, that is, enhancing consumers’ PWB and consumers responding by co-creating the endorsed brand. Further, we hold that SP moderates the effect of ICC on BC. This study offers important insights to marketers and influencers on how to optimize ICC and SP in fostering BC while improving consumers’ PWB.

Literature Review

Social Exchange Theory

Social exchange theory holds that human behavior is shaped by exchanging tangible and intangible resources, benefits, and costs between two or more people (Blau, 1964; Homans, 1974). Further, the exchange of resources is catalyzed by each party’s behavior (Homans, 1974). This implies that in the social media context, consumers’ or followers’ responses to influencers are subject to what influencers share on the page. As a result, influencers utilize their expertise to develop and disseminate attractive, useful, and educational brand endorsement content for their consumers (Lou & Yuan, 2019). The content helps consumers make decisions (Vrontis et al., 2021) and improves consumer well-being (Jamil et al., 2024; Mundel et al., 2022). In return, consumers may feel appreciative and indebted, hence responding by suggesting brand improvements, sharing endorsed brand content, and recommending the brand to others. Accordingly, such actions translate to BC (France et al., 2018; Nysveen & Pedersen, 2014). In addition, according to M. Kim and Kim (2020), influencer content triggers interpersonal exchange by offering content that improves consumers’ PWB, and consumers reciprocate by participating in BC in return. Thus, we employ this theory to explain that ICC is an important resource that may positively affect consumers’ PWB and subsequently stimulate consumer participation in BC. This is because, as consumers perceive benefits from the content shared by the influencers, they become willing to reciprocate by co-creating the endorsed brand.

Social Presence Theory

The theory explains how individuals communicate and form relationships through electronically mediated channels such as computers, telephones, and social media platforms (Short et al., 1976). It suggests that the effectiveness of electronic communication depends on the extent to which communication creates a sense of SP. The SP of an electronic medium, such as a website, articulates the persuasiveness capacity of the communication medium (Jiang et al., 2019).

The theory has been applied and presents a significant impact of SP on consumer behavior in influencer marketing settings (Kilumile & Zuo, 2024). Influencers can consolidate their SP by making real-time responses to comments, sharing personal life stories, and live video streaming on social media platforms. Thus, the SP cultivated on the influencers’ page may result in BC. Regardless of its potential to explain BC, the theory is less utilized in the influencer marketing context (Joshi et al., 2025).

Therefore, social presence theory is cross-fertilized with social exchange theory to explain circumstances in which ICC impacts BC. Considering the two theories, we hold that resources such as credible content shared by influencers are important in improving perceived consumers’ PWB. In return, consumers may reciprocate by co-creating the brand. Further, the effect of shared resources by the influencer (credible content) is likely to be higher when the influencer demonstrates SP through real-time replies, live video chatting, and sharing intimate personal life stories.

The Concept of Brand Co-creation

Brand co-creation (BC) involves the intentional, active, and interactive consumers’ behavioral activities that add value to the brand (France et al., 2018; Hatch & Schultz, 2010). Consumers co-create brand value by involving in creating brand identity, meaning, experiences, and emotional connections, resulting in innate brand relationships and a feeling of shared brand ownership. The concept of BC emerges from the brand community (Muniz & O’Guinn, 2001) and brand co-creation theories (Hatch & Schultz, 2010), which emphasizes the potential role of consumers in co-creating brand value. There is a confound in the conceptualization of BC and value co-creation; While the concepts are related, they are not identical, they are distinct. Value co-creation involves multiple stakeholders (e.g., customers, employees, suppliers, distributors, etc.) co-creating products and services. Value co-creation is rooted in the service-dominant logic, underscoring that value is co-created through interaction with stakeholders (Vargo & Lusch, 2004). Whereas BC is a subset of value co-creation, which involves the co-creation of brand identities, meanings, experiences, and emotional connections (Nibras et al., 2025; Ramaswamy & Ozcan, 2016; Simmons & Durkin, 2023). As such, BC primarily involves consumers and brand communities in the co-creation process as opposed to value co-creation which generally adds value to the product or service by involving multiple stakeholders.

Furthermore, while consumers remain at the center of the BC process, the advancement of social media has brought influencers as new important stakeholders in fostering consumer participation in BC (Ju et al., 2021; Simmons & Durkin, 2023). On social media, influencers create and disseminate attractive, expert, educational, and useful content about endorsed brands (Joshi et al., 2025; Vrontis et al., 2021). Accordingly, disseminated influencer content is key in BC as it enhances brand transparency and stimulates positive brand dialogue (Hatch & Schultz, 2010; Prahalad & Ramaswamy, 2004). Additionally, during brand dialogue, consumers co-create the brand by actively and voluntarily sharing feedback, making suggestions to improve the brand, helping fellow consumers by clarifying more about the brand and recommending the brand to others (France et al., 2018). Although influencers are important stakeholders in stimulating customer participation in BC (Ju et al., 2021; Nibras et al., 2025; Simmons & Durkin, 2023), literature on BC in influencer marketing settings is infant (Ju et al., 2021; Le et al., 2022). Prior research does not explain how influencers impact BC (Kilumile & Zuo, 2024), subsequently, scholars call for further research in this domain (Ju et al., 2021; Kilumile & Zuo, 2024). Thus, the current study investigates how and when ICC impacts BC to enrich BC literature. Hence providing essential insights to practitioners on how to enhance BC in influencer marketing settings.

Hypothesis Development

Influencer Content Credibility and Brand Co-creation

Influencers’ content is essential in driving and stimulating consumer behavior (Shen, 2021). Additionally, credibility determines the power of influencers to drive consumer behavior (Joshi et al., 2025; Kanaveedu & Kalapurackal, 2024; Vrontis et al., 2021). Influencer credibility significantly impacts consumer value co-creation behavior (Bu, Thaichon, and Carlini, 2022). Besides, Influencer content credibility has a greater influence on e-WoM, which adds value to the brand (Muda & Hamzah, 2021). A noteworthy contribution to the literature on the understanding of the significant role of influencer credibility on value co-creation is revealed. However, the understanding of the influence of ICC on BC is limited, regardless of its importance. Thus, in line with social exchange theory, we argue that content is a crucial resource in the exchange process. When influencers share credible content about the endorsement, consumers are expected to reciprocate by co-creating a brand. Therefore, with these arguments, the following hypothesis is proposed:

H1: Influencer content credibility positively affects brand co-creation. Such that consumers exposed to high-credibility content are likelier to participate in brand co-creation than those exposed to low-credibility content.

Mediating Effect on Psychological Well-being

Consumers’ Psychological well-being has the potential to explain how ICC impacts BC. Social media platforms give avenues for consumers to develop and maintain relationships with others, including influencers, to enhance their well-being through gaining social capital (Verduyn et al., 2017; C. Zhang et al., 2023). Furthermore, consumers’ PWB is improved when interacting with credible influencers (Jamil et al., 2024; Pilgrim & Bohnet-Joschko, 2019) through reducing anxiety (Mundel et al., 2022). Such enhanced consumers’ PWB subsequently affects consumer behavior (P. Bhardwaj & Kalro, 2024) and fosters brand engagement (Qayyum et al., 2023). Accordingly, brand value is co-created throughout brand engagement (Bazi et al., 2020). Based on these arguments, enhanced consumers’ PWB can potentially catalyze consumers’ likelihood to co-create the endorsed brand. However, the literature remains silent on the role of consumers’ PWB in fostering BC. Therefore, in line with social exchange theory, we argue that the resources shared by an influencer (credible content) enhance consumers’ PWB. Enriched consumers’ PWB makes consumers feel indebted, hence reciprocating by participating in co-creating the endorsed brand. Thus, we hold that the consumer’s PWB can explain the mechanism and process by which ICC affects BC. Therefore, the following hypothesis is suggested.

H2: Psychological well-being mediates the effect of influencer content credibility on brand co-creation.

Moderation Effects of Social Presence

The social presence of an influencer has the potential to explain the conditional effects of ICC on BC. The literature indicates a positive relationship between SP and consumer behavior (Jiang et al., 2019; Rashid et al., 2022; H. Zhang et al., 2012). Besides, SP is important for developing customer loyalty to the brand (Attar et al., 2022). Additionally, the perceived SP of an influencer significantly impacts consumers’ decisions to co-create the endorsed brand (Kilumile & Zuo, 2024). In an influencer marketing setting, influencers can enhance their SP by quickly responding to comments, sharing intimate life stories, and live streaming. Such a situation may trigger consumers to co-create endorsed brands by sharing brand messages, brand experiences, and advocating for the endorsed brand. By cross-fertilizing social exchange theory and social presence theory, we suggest that ICC is an important resource in facilitating exchange, that is, enhancing consumers’ PWB and consumers co-creating the endorsed brand in return. However, the effect of ICC on BC will be high when the influencer consolidates SP. Thus, we propose the following hypothesis (Figure 1).

H3: Social Presence moderates the effect of influencer content credibility on brand co-creation. Such that the effect of influencer content credibility on brand co-creation is strengthened when there is a high social presence rather than a low social presence.

Figure 1.

Research model.

Methodology

Study Design, Population, and Intervention

This study employed a 2(ICC: High vs. Low) × 2(SP: High vs. Low) factorial between-subjects design to explore the effect of ICC and SP on consumers’ PWB and BC. A factorial between-subjects design is suitable when a short-term experimental study is undertaken and the influence of more than one independent variable is investigated (Viglia et al., 2021; Whatley, 2022). The population of interest was youth, since their consumption behavior is predominantly influenced by social media platforms (John et al., 2025). In this study, youths are regarded as individuals aged 15 to 35 (United Republic of Tanzania, 2024) who are typically reflected by university students. Therefore, Mzumbe university students were purposefully involved in this study to represent the youth population. Student sample is a surrogate of the general youth population which consumes via social media platforms (John et al., 2025).

Students are used in investigating matters that they are representing, such as testing general principles, investigating personal traits, and consumer behavior (Viglia et al., 2021). As a suitable surrogate of samples for investigating consumer behavior, recent studies, such as Du et al. (2025) and John et al. (2025) have also employed data from university students to contribute significantly to consumer behavior literature. Accordingly, this study used university students to investigate consumers’ decisions to co-create endorsed brands. Youth consumers frequently interact with influencer content on social media platforms, particularly Instagram, for brand familiarity to make an informed decision (Kilumile & Zuo, 2024).

Since influencers are not inclined to allow research to be conducted with their real followers, fictitious influencer posts were used in this study (AlRabiah et al., 2022). As also applied by T. Kim and Yoon (2024), the fictitious posts of Instagram fashion influencers were created using Adobe Photoshop (See Appendix 1). We adapted the approach by AlRabiah et al. (2022) to provide diverse, relevant fashion suggestions for both male and female participants. The choice for Instagram fashion influencers is because influencer marketing is highly implemented in the fashion industry compared to other industries (Samet, 2023).

Four groups for the intervention purpose were created as follows: Group A (High ICC × High SP), Group B (High ICC × Low SP), Group C (Low ICC × High SP), and Group D (Low ICC × Low SP). Each group experienced its intervention post in a respective classroom. The intervention influencer’s content posts were colored printed and distributed to each participant within their respective classroom. We avoided using smartphones and electronic gadgets during the intervention process to control possible experimental contamination.

Sampling, Sample Size, and Data Collection

This study involved consumers who actively interact with influencers on Instagram. The study was guided by both the PLS-SEM sample size rule of thumb and G*Power software to determine a suitable minimum sample size from which data for robust findings was collected. PLS-SEM rule of thumb suggests that at least 10 cases per measurement item are required to ascertain a minimum sample size (Hair et al., 2014). This was supplemented with sample size estimation with G*Power software (Erdfelder et al., 1996) by suggesting the effect size in predicting BC. The sample estimation by G*Power considered an effect size of 0.15, an alpha level of 0.05, and a power of 0.95, while the predictor variables were 3. Therefore, as per the PLS-SEM rule of thumb, a minimum sample size was estimated to be 120, while G*Power estimated a minimum sample size of 119. Thereafter, researchers visited classrooms to recruit the participants. The Google link was distributed to the students; only those who use Instagram and frequently interact with influencers’ content on Instagram were purposefully recruited.

From the distributed Google link, 213 participants were recruited and randomly distributed to four groups to experience different intervention posts as described earlier. Random distribution of the participants guaranteed internal validity due to the balanced characteristics of the groups in terms of both non-observed and observed variables. Moreover, during data analysis, we considered the sampling variability concern. Thus, confounding factors including respondents’ hours spent on influencer’s page per day (stickiness), age and gender were controlled. This was done to ensure that the differences in BC scores between the groups were only caused by the intervention process rather than other possible factors. After being exposed to colored printed posts, data was collected by using offline questionnaires. A maximum of 3.5 hr was spent on the entire process. This timeframe was reasonable enough since between-subjects designs require a short timeframe (Viglia et al., 2021). Measurement items for consumers’ PWB were modified from Ryff and Keyes (1995) and items for BC were adapted from Kilumile and Zuo (2024).

Realism and Validity Control Mechanism

To enhance experimental realism and validity (Viglia et al., 2021), different measures were taken. First, we ensured randomization of participants into sample groups. Randomization guaranteed balanced similarities in the groups and minimized confounding factors that could have affected the study’s specific objectives. Second, as recommended by Whatley (2022), we avoided the possibility of experimental contamination by restricting participants from using smartphones and electronic gadgets during the experimental intervention. Consequently, the possibility of online interactions between participants from different groups was minimized. Therefore, this ensured that the participants’ behavior in the respective groups was influenced by the intervention within the group rather than any information from outside the group.

Third, attrition as a threat to validity (Whatley, 2022) was controlled by maintaining the assigned number of participants in each experimental group. From the beginning of the study, the researchers made a clear purpose of the study, which enhanced participants’ confidence to be involved in the study until the end of the study. The intervention and data collection were made as reasonably short as possible to avoid the tiredness and boredom of the participants. Therefore, this maintained the study’s sample size, which yielded rich data for analysis and generalization of the findings to the youth population. Fourth, the researchers’ fidelity of implementation was guaranteed (Whatley, 2022). This was enhanced by frequently engaging with the participants to seek participants’ understanding of the intervention posts and researchers’ additional clarifications to ensure clarity of the posts. Lastly, a 3.5-hr experimental session made our experiment as reasonably short as possible. This avoided break time that could have allowed participants from different groups to meet outside the classrooms and discuss different matters which were going on in the experimental groups, thus, avoiding contamination possibilities. As such, by taking into consideration these initiatives, the established results convey possible causal-effect relationships. Therefore, informing both the consumer behavior and influencer marketing literature and practitioners about the role of ICC and SP in influencing consumers’ PWB and BC.

Manipulation Checks and Results

A study involved 213 participants, composed of females (n = 108, 50.70%, mean age = 22.08, SD = 2.08) and males (n = 105, 49.30%, mean age = 23.77, SD = 2.16) and on average, participants often spent 4.48 hr per day on social media influencers’ pages.

A manipulation check was conducted using an independent samples T-test to compare the scores for ICC and SP between the two groups. On a five-point Likert scale, participants were asked to rate their perception of the social presence and influencer content credibility. The results indicated a significant difference in perceived ICC between participants exposed to high ICC conditions (M = 4.14, SD = 0.55) versus low ICC conditions (M = 2.33, SD = 0.44); t = 26.44, df = 211, p < .001. The mean difference in ICC scores is 1.81 (SE = 0.069) with 95% CI: 1.68-1.95. Regarding SP, the results indicated a significant difference in perceived SP between participants exposed to high SP conditions (M = 4.12, SD = 0.52) versus low SP conditions (M = 2.36, SD = 0.64); t = 22.03, df = 211, p < .001. The mean difference in SP scores is 1.76 (SE = 0.08) with 95% CI: 1.60-1.92. Thus, the manipulation was successful.

Measurement Model Assessment

The measurement model was examined before testing hypotheses (see Table 2). First, indicator reliability results were examined; the results indicated that all measurement items had loading values above .708. Therefore, demonstrating that the items were explained by a respective construct by more than 50% (Sarstedt et al., 2022). Second, construct reliability results showed that the composite reliability (Rho_a) values were above .70. Thus, demonstrating that measurement items measuring a particular construct consistently measure the same construct (Sarstedt et al., 2022). Third, the convergent validity was assessed using average variance extracted (AVE). The results indicated that AVE values for all constructs were above the minimum value of 0.50. Lastly, the HTMT values were obtained to measure the discriminant validity. As presented in Table 3, the results indicated that all HTMT values were below the acceptable value of 0.85. Hence, indicating that the underlying study constructs were different from each other (Henseler et al., 2015). Table 2 presents the item loadings, Cronbach’s α, and AVE.

Table 2.

Validity and Reliability Test.

Construct Items	Loadings
Psychological Well-being (PWB) (Ryff & Keyes, 1995) (Cronbach α = .871, CR (rho_a) = .966 and AVE = .714)
PWB1: The posts shared by this influencer are supportive of gaining new fashion skills	.838
PWB2: The posts shared by this influencer can improve my lifestyle	.915
PWB3: The posts shared by this influencer make me more positive about myself.	.836
PWB4: The posts shared by this influencer make me more confident	.786
Brand Co-Creation (BC) (Kilumile & Zuo, 2024) (Cronbach α = .937, CR (rho_a) = .941 and AVE = .694)
BC1: I would repost the recommended brand-related content shared by this influencer.	.880
BC2: I would encourage my friend to consider buying a brand that the influencer recommends.	.825
BC3: I would sometimes explain to my friends the benefits of a brand-recommended	.823
BC4: I would help other followers solve difficulties with the recommended brand.	.839
BC5: I would provide suggestions on how to improve the recommended brand.	.836
BC6: I want to inform others about new brand recommendations made by the influencer.	.849
BC7: I would help other followers/friends buy a recommended brand	.775
BC8: I would adhere to influencers’ requests to share my experiences with others	.832

Table 3.

HTMT Results Indicating Discriminant Validity.

Construct	BC	ICC	PWB
BC
PWB	0.097	0.133

Structural Model Assessment

The robustness of the structural model was assessed using the variance inflation factor (VIF) of the inner model, the explanatory power of the model, and model fit. Table 4 presents results for structural model assessment. The VIF values of the exogenous constructs were below 3.3. Therefore, there are no collinearity issues (Sarstedt et al., 2022). The model’s explanatory power was also assessed using R ². The results indicated that the R ² for PWB and BC was .015 and .490, respectively. Thus, indicating that ICC somewhat weakly explains consumers’ PWB and moderately explains BC. Furthermore, the f-squared values for ICC were obtained. The results indicated that ICC strongly affects BC with an f-squared value of 0.447. Besides, the ICC weakly affect PWB with an f-squared value of 0.020. Similarly, PWB weakly affects BC with an f-squared value of 0.034.

Table 4.

Structural Model Assessment.

Construct	VIF	f-square
Influencer content credibility	1.973	0.447
Influencer content credibility	1.000	0.020
Psychological well-being	1.044	0.034

SRMR = 0.055, R ²: PWB = 0.015, BC = 0.490.

Lastly, the model fit was assessed using a standardized root mean square residual (SRMR). As presented in Table 4, the SRMR value obtained was 0.055, which is below the accepted SRMR value of 0.080. Therefore, the model is considered satisfactory in representing the data obtained.

Hypothesis Test Results

The study investigated the mechanism and process of ICC in affecting BC. The study employed PLS-SEM to test the proposed hypotheses. The bootstrapping procedure was applied using 10,000 resamples to ensure robust estimates. While testing the hypotheses, the age, gender, and time the participants spent on the influencer's page (stickiness) per day were included in the model as control variables. As presented in Table 5, age (β = .028, T = 0.580, p > .05), gender (β = −.041, T = 0.419, p > .05), and stickiness (β = .078, T = 1.561, p > .05) did not have significant effects on BC.

Table 5.

Hypothesis Results.

Hypotheses	β	T-statistics	p Value	Decision
H1: ICC → BC	1.294	8.587	.000	Supported
H2: ICC → PWB → BC	.037	1.659	.049	Supported
H3: SP × ICC → BC	−1.630	8.820	.000	Not supported
Age → BC	.028	0.580	.281
Gender → BC	−.041	0.419	.338
Stickiness → BC	.078	1.561	.060

Regarding H1, the study aimed to examine the effect of ICC (high vs. low) on BC. Such that participants exposed to high ICC are likelier to participate in BC than those exposed to low ICC conditions. The study found that high ICC conditions significantly affect BC than low ICC conditions (β = 1.294, t = 8.587, p < .001). Thus, supporting H1. The results imply that consumers are likelier to co-create the endorsed band when influencers’ content demonstrates high credibility rather than low credibility.

Also, the study investigated whether the effect of ICC on BC is mediated by consumers’ PWB. The results indicate that consumers’ PWB significantly mediated the effect of ICC on BC (β = .037, t = 1.659, p < .001). Thus, supporting hypotheses H2. The results imply that consumers’ PWB is also important in fostering the effect of ICC on consumers’ decisions to co-create the endorsed brand.

Lastly, the study examined the interaction effect of SP (high vs. low) and ICC (high vs. low) on BC. The results indicated a significant difference in moderation of SP (high vs. low), whereby low SP significantly strengthened the effect of ICC on BC than high SP (β = −1.630, t = 8.820, p < .001). Further analysis was done using a two-way ANOVA to test the significant difference in moderating effects of SP (high vs. low) in the relationship between ICC and BC. Similarly, the results indicated significant differences in interaction effect between SP (high vs. low) and ICC, F (1, 209) = 62.56, p < .001, η² = .230. The η² = .230 indicates that consumers’ decisions to co-create the endorsed brand are moderately affected by the interaction effect of SP and ICC. Figure 2 provides a further description of the results. As portrayed, in a situation where ICC is perceived as high, its effect on BC is strengthened by a low SP. Contrary, when ICC is perceived as high, the effect of ICC on BC is weakened by high SP. Demonstrating that consumers are likely to increase their willingness to co-create brands when low-credibility content is aligned with high SP. Also, high-credibility content stimulates co-creation when SP is low.

Figure 2.

Interaction effect of SP and ICC on BC.

Discussion and Theoretical Implications

With the development of social media, influencers have become significant stakeholders in brand co-creation (Ju et al., 2021). Consumers spend significant time on the influencer’s page, which gives more chances for the influencer to stimulate consumers’ decisions to co-create the recommended brand. Prior studies indicate that influencers significantly affect BC (Kilumile & Zuo, 2024). However, literature is still at a nascent stage, and little is understood about the effects, mechanisms, and conditional effects of ICC on BC. This knowledge gap limits marketers in strategically utilizing the potential of influencer marketing strategy in fostering BC. Therefore, this study advances the scarce literature on the understanding of the influence of ICC on BC. The study explains the mediation role of PWB and the moderation effect of SP.

The current study found that ICC significantly affects BC. Such that consumers exposed to high ICC conditions are likelier to participate in BC than those exposed to low ICC conditions. This implies that with high ICC, consumers are more willing to co-create the endorsed brand by helping fellow consumers improve their brand experience, clarifying the brand, and suggesting solutions to improve the value of the brand. Therefore, we extend the previous studies on the potential role of influencers in stimulating BC among consumers (Ju et al., 2021; Kilumile & Zuo, 2024; Le et al., 2022) by explaining how ICC levels shape the co-creation of endorsed brands among consumers. According to social exchange theory, resources are key to the exchange process (Blau, 1964). The results signify that ICC is an important communication resource in fostering BC. When consumers receive credible content from an influencer, they perceive it as a valuable resource, and subsequently actively participate in the exchange process by co-creating the brand. Our results precisely provided insights focusing on BC as opposed to extant studies which focused on value co-creation in general. Therefore, marketers collaborating with influencers with the ability to create content demonstrating high knowledge, competence, and experience are more likely to stimulate BC, hence adding significant value to the endorsed brand.

To explain the mechanism effect of ICC on BC, the study investigated the mediating effect of consumers’ PWB. This uniquely contributes to the literature by demonstrating how perceived PWB can translate into consumers’ willingness to co-create the endorsed brand, which is not well captured in the literature, particularly the BC literature. The study showed that consumers’ PWB partially mediates the effects of ICC on BC. The partial mediation effect of consumers’ PWB underscores the understanding of what drives PWB and its subsequent effect on consumer behavior. Self-acceptance, self-growth, positive relations with others, and confidence, translating into individuals’ PWB (Ryff, 1989; Ryff & Keyes, 1995), are gained through interacting with credible influencers (Jamil et al., 2024; Pilgrim & Bohnet-Joschko, 2019). Additionally, considering the social exchange theory (Blau, 1964; Homans, 1974), human behavior is explained by transactions involving the exchange of resources. Thus, ICC is an important resource in enhancing consumers’ PWB; in turn, consumers may reciprocate by co-creating the endorsed brand. This suggests that the reciprocity intensity of consumers (participating in BC) depends on beneficial resources offered by the influencer’s content (content that improves PWB) to consumers. The results underline the importance of paying attention to consumers’ well-being in the marketing process. Thus, it is important to streamline influencer marketing strategies that help to achieve business goals while taking care of the well-being of their consumers.

Additionally, to understand further the conditions under which ICC affect BC, the study investigated the interaction effect of SP in the interplay of ICC and BC. The results indicated that the SP significantly moderate the effect of ICC on BC. Such that in a situation where there is a high SP of an influencer, the effect of ICC on BC is weakened than when there is a low SP of an influencer. This implies that consumers’ willingness to co-create the endorsed brand is high when the influencer’s SP is low. While social presence theory posits that social presence is an important aspect in shaping consumer behavior (Kilumile & Zuo, 2024; Rashid et al., 2022); This study enriches the understanding by underscoring that high and low SP aspects have different effects on consumers behaviors. Such that under the situation where there is high ICC, consumers pay less attention to high SP aspects and vice versa. In the same vein, high SP may lower the effect of high ICC on BC by unnecessarily increasing cognitive load, thus diverting the ICC cues (Sweller, 1988). Excessive interactions with an influencer overload consumers’ working memory. Hence, it may reduce consumers’ willingness to co-create the endorsed brand. Besides, while collaborating with less established influencers who are perceived as having low-credibility content, marketers can stimulate BC by fostering high SP. Perceived SP can be enhanced through personalized, warmer, instant, and interactive communication. These results offer a foundation for future research to employ cognitive load theory (Sweller, 1988) to explain why high social presence may overwhelm consumers interacting with ICC and its subsequent effect on consumer behavior.

Lastly, this study contributes to the advancement of social exchange theory and social presence theory in influencer marketing and consumer behavior settings. The study highlights the combined effect of the two theories in explaining consumers’ PWB and BC. We underscore the combination effect of resources and SP in fostering the exchange process. The integration of two theories suggests that although ICC is an important resource to foster consumers’ PWB and consumer reciprocation (participating in BC), the likelihood and magnitude of reciprocation (co-creating the endorsed brand) may depend on the extent to which the influencer manifests SP during the exchange process. While SP is important in fostering consumer behavior in online settings, high SP overwhelms consumers in evaluating high credibility content, thus reducing the effect of ICC as an important resource in fostering BC. Therefore, while marketers may collaborate with influencers with high ICC, more attention should also be paid to SP levels. In a situation where an influencer can generate highly credible content, marketers may reduce the frequency of interactions with consumers. Solely because high SP overwhelms consumers in processing content, which subsequently reduces their willingness to co-create the endorsed brand.

Practical Implications

The current study offers several practical implications to marketers and influencers. First, influencers whose content is perceived to be less credible can improve their ability to foster BC by enhancing their SP. Perceived SP can be improved by increasing personalized, friendly, prompt responses and sharing intimate life stories when interacting with consumers. Additionally, harnessing interactive content such as polls, questions, and answer sessions may also enhance emotional attachment and SP feelings among consumers.

Second, marketers and influencers in the fashion industry may improve consumers’ PWB by creating and curating content that helps consumers develop confidence, positive self-expression, mood enhancement, and improve fashion knowledge. Therefore, developing interactive videos on social media such as Instagram and TikTok demonstrating an influencer’s happiness, confidence, and positive mood would increase consumers’ PWB, thus resulting in more co-creation of endorsed brands.

Third, consumers’ PWB and BC metrics can be used as important metrics for assessing influencers’ performance. By using natural language processing (NLP) and text mining technology, marketers can trace consumers’ sentiments associated with their PWB and co-creation conversation rooted in influencers’ content and other endorsement activities on social media platforms.

Lastly, under low marketing budget circumstances, marketers may partner with less established influencers who are perceived as having low-credibility content and capitalize on GenAI empowered chatbots such as Grok to enhance contextualized live chatting, real-time replies, and warm, personalized communication. Doing so will foster a high perceived SP, consolidate influencer-consumers’ emotional attachment, and subsequently accelerate consumers’ decisions to co-create the endorsed brand.

Limitations of the Study and Future Research Directions

While the current study offers insightful contributions to the influencer marketing and consumer behavior literature, it has some limitations that also offer opportunities for future research. First, the current study employed a sample from the Tanzania context only. However, influencer marketing arrangements (Hu et al., 2020; Nanevi et al., 2022) and consumer reactions to marketing messages may vary from one context to another (Pentina et al., 2013). Therefore, the interpretation of findings reflects consumer behavior in the context of Tanzania and other similar contexts. Based on this research model, future studies may compare the impact of ICC on BC across different contexts, particularly in Europe and Africa. Additionally, the study considered proper mechanisms and initiatives to enhance realism and validity for the purpose of generalization of the results. However, a fictitious influencer, to some extent, may reduce experimental realism. Future studies may consider using real influencers to test our model. Besides, BC was investigated as consumers’ intention to co-create the endorsed brand. Future studies may employ BC conceptualization in the current study to measure actual behavior to strengthen the external validity (Morales et al., 2017). Lastly, the study used a student sample to represent youth consumers’ decisions to co-create the endorsed brand. Although it is suitable to use student samples as surrogates of matters related to consumer behaviors (Viglia et al., 2021), especially due to their experience of using social media and interacting with influencers; however, the sample may somehow reduce the external validity. Therefore, future studies may test this model using a non-student sample to improve external validity.

Conclusion

This study cross-fertilized social exchange theory and social presence theory to investigate the impact of ICC on BC through the mediation of consumers’ PWB and the moderation of SP. The results indicated that ICC significantly impacts BC, whereby consumers exposed to high ICC conditions are likely to co-create the endorsed brand than those exposed to low ICC conditions. Besides, the effect of ICC on BC is also mediated by consumers’ PWB, suggesting that improved consumer PWB may stimulate consumers’ willingness to co-create brands. In the same vein, the effect of ICC on BC is moderated by SP, whereby high SP weakens the effect of ICC on BC in high ICC conditions than in low ICC conditions. In line with social exchange theory and social presence theory, this study suggests that ICC is an important resource in facilitating exchange, that is, enhancing consumers’ PWB and consumers co-creating the endorsed brand in return. However, its potential role in facilitating the exchange process can be moderated by SP. Thus, marketers should pay attention to SP, particularly when fostering BC using ICC. Heightening SP when influencers’ content is perceived as less credible can increase consumers’ willingness to co-create the brand. Similarly, reducing SP when influencer content is perceived as highly credible may also simulate consumers’ willingness to co-create the endorsed brand. The study offers useful insights to influencers and marketers, particularly on how to enhance SP and ICC and leverage them to foster BC while improving consumers’ PWB. The study was conducted in Tanzania, thus reflecting consumer behavior in the Tanzanian and other similar contexts. Additionally, the study offers insights into consumers’ intention to co-create the endorsed brand. Thus, providing a foundation for future scholars to consider investigating the actual behaviors of consumers’ co-creation of endorsed brands.

Footnotes

Appendix 1 Acknowledgements

The authors give thanks to Mzumbe University, Shumbana Walwa, Angel Mwoleka, Reuben Kamuga, Baraka Mtebe, Sadick Mchia, Samuel Mrisha, Lubida Lwako, Krantz Mwantepele, Gillsant Mlaseko, Weapon Mwajombe, Nathan Mwajombe, Michael Mwampashe, and Wandera Joseph for their unconditional support.

ORCID iDs

Jerum William Kilumile

Li Zuo

Johakim Katekele John

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data for this study are available and can be requested from the corresponding author.

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How Influencer Content Credibility Drives Brand Co-creation: Do Social Presence and Consumers’ Psychological Well-being Matter?

Abstract

Plain language summary

Keywords

Introduction

Literature Review

Social Exchange Theory

Social Presence Theory

The Concept of Brand Co-creation

Hypothesis Development

Influencer Content Credibility and Brand Co-creation

Mediating Effect on Psychological Well-being

Moderation Effects of Social Presence

Methodology

Study Design, Population, and Intervention

Sampling, Sample Size, and Data Collection

Realism and Validity Control Mechanism

Manipulation Checks and Results

Measurement Model Assessment

Structural Model Assessment

Hypothesis Test Results

Discussion and Theoretical Implications

Practical Implications

Limitations of the Study and Future Research Directions

Conclusion

Footnotes

Appendix 1

Acknowledgements

ORCID iDs

Funding

Declaration of Conflicting Interests

Data Availability Statement

References