Exploring Purchase Intention in Online Influencer Marketing Campaigns Through an Integrated Model

Abstract

Social media offer a digital space for people to connect with online influencers, such as bloggers, YouTubers, and Instagram celebrities. This study aims to investigate the factors affecting consumers’ purchase intention in social media-based influencer marketing context, while simultaneously revealing the underlying mechanisms through an integrated model. An online survey with 335 participants who have followed social media influencers (SMIs) was conducted to empirically test our proposed model. PLS-SEM was employed to analyze the measurement and structural models. The findings indicate that SMIs, when perceived as credible sources exhibiting high levels of expertise, trustworthiness, and homophily, stimulate consumers’ fear of missing out (FOMO) and establish parasocial relationships with these influencers, which in turn enhance purchase intention. Furthermore, when consumers perceived a high level of serendipity, the relationship between FOMO and purchase intention will be strengthened. This study extends the perspective of source credibility and parasocial relationships in the context of influencer marketing by incorporating the impact of smart recommendation algorithms and the emotional anxiety experienced by consumers. It offers a novel approach to analyze why consumers are motivated to purchase products endorsed by SMIs.

Keywords

influencer marketing parasocial relationships FOMO source credibility serendipity

Introduction

In recent years, traditional marketing practices have been undergoing a transformation driven by social media influencers (SMIs). An increasing number of consumers are swayed by suggestions and recommendations from these online influencers which in turn shapes brand perceptions and consumption habits (Casaló et al., 2020; Sokolova & Kefi, 2020). Influencer marketing involves companies partnering with SMIs to develop or promote branded content aimed at boosting brand recognition. Its growth is driven by the ability to connect brands with consumers through creators who deeply understand and actively engage with their followers (Saternus et al., 2024). High-quality content from online influencers with large numbers of audiences can drive consumers’ purchase intentions (Park & Lin, 2020; Rungruangjit, 2022), which is a key predictor of actual purchase activity and successful marketing (Dodds et al., 1991; Shin & Biocca, 2017).

In light of this, previous researchers in the influencer marketing field have launched a series of explorations on this issue. Some of them examined the purchase intention by considering source characteristics of online influencers and consumer attitude toward them (Park & Lin, 2020). They have found that product-source fit enhances perceived trustworthiness, while product-content fit boosts hedonic attitude. Both factors significantly influence buying intention. Apart from the evaluations of an SMI, consumers’ psychological ownership related to an influencer is another crucial avenue of exploration (Pick, 2021). E. C.-X. Aw and Chuah (2021) indicated that attractiveness, prestige, and expertise of SMIs perceived from their posted content can positively influence a type of one-sided relationships called parasocial relationships with these influencers. Although the credibility of SMIs does not directly influence their followers’ purchase intention, it exerts influence through parasocial relationships/interactions or identification, both of which have the potential to enhance purchase intention, and consumer stickiness (Melnychuk et al., 2024; Wahab et al., 2022). From these studies, it is not difficult to find that the source credibility of SMIs and the one-sided connections between influencers and consumers called parasocial relationships play significant roles in shaping consumers’ attitudes toward products and their subsequent purchase intentions. Such “source credibility-parasocial relationship” framework can be considered as an important perspective for analyzing the purchase intention in influencer marketing context.

But on the other hand, with the widespread application of AI-based recommendation algorithms, consumers’ shopping experiences are more shaped by smart recommendation systems than search-oriented mechanisms (H. Zhao & Wagner, 2023). During the shopping process, consumers’ judgments regarding product quality are sometimes swayed by exceptional cases. Many beneficial discoveries often occur unexpectedly, fostering a sense of serendipity — defined as the accidental discovery of valuable information (Grange et al., 2019). In addition, the psychological concept of fear of missing out (FOMO) often emerges as a powerful driving force in consumer behavior, closely tied to emotional anxiety influencing purchasing decisions (Dinh & Lee, 2022; Hodkinson, 2019). It results from the feeling of being left out of an appealing experience and a strong desire to continuously remain connected to what others are doing (Buglass et al., 2017). When social media influencers showcase products or experiences on social media and visibly enjoy them, it often triggers a potent sense of FOMO, which in turn motivates consumers to consider trying, or purchasing the endorsed items themselves (Sun & Bao, 2023). However, to the best of our knowledge, while examining factors that affect purchase intention in influencer marketing context, few studies have considered the roles of serendipity and FOMO in the “source credibility-parasocial relationship” framework. The interaction of serendipity and FOMO with the source credibility of SMIs and the parasocial relationships between consumers and SMIs remains largely unexplored.

To fill this gap, this study aims to establish a well-organized framework to give some new insights regarding the purchase intention by integrating source credibility, parasocial relationships, FOMO, and serendipity. We seek to identify the determinants of purchase intention within the realm of influencer marketing campaigns, striving to uncover the underlying mechanisms that drive this process. This investigation is anticipated to yield numerous contributions to the existing body of literature. First, our research extends the “source credibility-parasocial relationship” framework and develops a theory-grounded research model to analyze the purchase intention considering smart recommendation algorithms and emotional anxiety consumers experience during the influencer marketing process. Second, a deeper understanding of FOMO has been provided by elucidating its relationships with source credibility and parasocial relationships. Third, we enrich the current literature on serendipity by assessing its influence on consumers’ purchase intention, taking into account the unique contexts and characteristics of online influencer marketing.

The layout of this paper is structured in the following manner. The subsequent section offers the theoretical foundation of our research, succeeded by the formulated hypotheses and the research framework in Section 3. Sections 4 and 5 outline the study’s approach, data analysis, and findings. The paper is finalized with the conclusions, implications, and constraints of our research in Section 6.

Theoretical Background and Hypotheses

Fear of Missing Out (FOMO)

FOMO is described as an emotional anxiety that other people are doing, in possession of something better, or having more rewarding experiences from which one is absent (Good & Hyman, 2020; Przybylski et al., 2013). People with a high level of FOMO tend to have a desire to stay continually connected with peers’ activities, because they hope to constantly know what others are doing (J. A. Lee et al., 2021; Przybylski et al., 2013). FOMO is also an important and common phenomenon among online consumers, especially in the influencer marketing context. When SMIs display products on their social media platforms, demonstrating their enjoyment, it frequently evokes a strong sense of FOMO. This is because individuals afflicted by FOMO may exhibit a heightened inclination to align with group norms in buying choices, driven by a desire to acquire products trending in their social circles and mitigate the feeling of being left out (Bläse et al., 2024). The potential regret of missing out often acts as a situational motivator for individuals, as it drives them to engage in certain activities to remain up-to-date (Dinh & Lee, 2022; Good & Hyman, 2020). To imitate and compare themselves with ideal images, FOMO occur on the products that influencers endorsed at a deeper level with personalized experiences (Dinh & Lee, 2022). It may amplify consumers’ apprehension about missing out, thereby driving them to make purchases. Accordingly, we propose that:

H1. FOMO is positively related to purchase intention.

Serendipity

Unexpectedness is the most obvious feature of serendipity, that is reflected as surprise. Previous studies note that serendipity involves something happens in an unexpected way or unexpected circumstances (Grange et al., 2019; Makri et al., 2014). Aside from unexpectedness, serendipity also requires an element of “insight” rather than just “blind luck” (Makri & Blandford, 2012). An individual’s valuable interaction with ideas, information, objects, or phenomena often trigger it (McCay-Peet et al., 2015). Therefore, serendipity can be defined as a combination of unexpectedness and insight, culminating in a valuable and unforeseen result. Social media affordances cultivate serendipity in online shopping environments. A variety of social features including consumer-generated product tags and endorsed recommenders in social circles foster serendipitous search experiences (Grange et al., 2019).

The feeling of serendipity can evoke positive emotions, thereby amplifying consumer satisfaction. (Ng et al., 2023; Reisenzein et al., 2019). For example, consumers in an unexpected situation with pleasant surprises often have fun browsing and enjoy so-called “Aha! moment” (Chung et al., 2017; McCay-Peet & Toms, 2011). They are more likely to enjoy their shopping experience and engage in information acquisition. Such a situation may encourage consumers to become engrossed in product selection and immerse themselves in this experience, which in turn spur unplanned spending and consequently increase their purchase intention. (Niu et al., 2021; Valenzuela et al., 2010).

On the other hand, serendipity implies additional informational value (Yang et al., 2023). Advertising content in social media that appeals to missing-out anxiety has been found a common phenomenon in social media marketing (J. Li et al., 2021, Bright & Logan, 2018). As perceived serendipity is along with unexpected discoveries that exactly fit latent preferences, consumers may concern more about they miss out products extolled by SMIs, which enhance the effect of FOMO. Such a “Wow!” moment allows consumers to obtain diverse shopping values and then to be more satisfied with their preferences, contributing more to the intention to purchase (Akram et al., 2023; Chung et al., 2017). As a result, this study expects that FOMO tends to be a stronger determinant of purchase intention for consumers who perceive high serendipity within influencer marketing context. According to the above analysis, the following hypotheses are proposed:

H2a. Perceived serendipity is positively related to purchase intention.

H2b. Perceived serendipity positively moderates the relationship between FOMO and purchase intention such that the relationship will be stronger when perceived serendipity is high.

Parasocial Relationships

Parasocial relationship refers to unilateral psychological affiliation that audiences form with media celebrities or personalities, which then imitate an imaginary interpersonal relationship (E. C. X. Aw et al., 2023). It describes a form of one-sided relationship that followers perceive to have with an influencer. Individuals are allowed to unilateral feel strong interpersonal connections with their favorite celebrities and even regard these celebrities as their personal friends or alike other associations (Hung et al., 2011). Parasocial relationships have been applied to analyze audiences’ relationships with a variety of media personas from traditional mass media to online environment (Labrecque, 2014). Given that social media platforms enable people to effortlessly connect with peers who share similar and common interests, facilitating regular interactions, the formation of parasocial relationships becomes more probable (Farivar et al., 2021). In influencer marketing, parasocial relationships are widely perceived or developed by consumers, which are manifested by intense intimacy and psychological connections toward some SMIs (E. C.-X. Aw & Chuah, 2021).

Prior research indicates that parasocial relationships serve as a motivating factor for followers. This unique interpersonal bond can foster parasocial identification, and the resulting pleasurable experience significantly shapes consumers’ cognitions and behavioral responses (Gu & Duan, 2024), ultimately influencing their intentions to heed influencers’ recommendations and make purchase choices (e.g., Farivar et al., 2021; J. E. Lee & Watkins, 2016). Owing to strong parasocial relationships, SMIs share purchase assessments on social media platforms can provoke followers to imitate these influencers’ actions or follow their credible recommendations that will possibly lead to buy particular products (Zafar et al., 2020).

Furthermore, as parasocial relationships are imaginary intimate relationships, the stronger parasocial relationships with influencers perceived on social media, the higher followers’ positive emotions (C. Zhao et al., 2022). Prior literature has argued that believing an experience will be favorable and relevant is a necessary condition for FOMO to occur (Good & Hyman, 2020). SMIs typically embody idealized images, inspiring followers to aspire to their lifestyles and engage in upward social comparisons (Dinh & Lee, 2022). This often leads to heightened concern among followers when they are unable to acquire products or services enjoyed by an influencer, instigating feelings of being left out and triggering anxiety related to missing out. Based on the above analysis, the following hypotheses have been proposed:

H3a. Parasocial relationships have positive effects on purchase intention.

H3b. Parasocial relationships have positive effects on FOMO.

Source Credibility

The source credibility explains the efficacy of persuasive messages communicated by a brand endorser. To be a credible source with high message efficacy, an endorser’s perceived expertise, trustworthiness, and attractiveness are three requirements which should be considered (e.g., Amos et al., 2008; Ohanian, 1990). Here, attractiveness can be categorized into physical attractiveness and social attractiveness. Physical attractiveness addresses the degree to which a source is perceived as visually appealing or pleasing to observe (Ki & Kim, 2019; Wiedmann & von Mettenheim, 2021). Social attractiveness implies the extent to which one holds another’s beliefs, values, experiences, lifestyles, and social status to be similar to his or her own, which is also termed homophily (Le & Hancer, 2021). In the context of social media, attractive influencers do not have to be good-looking or esthetically appealing, because the influencer-follower relationship is not necessarily based on instant interactions, or communications. As followers engage with an influencer’s content over time, the sustained nature of this relationship lessens the impact of physical attractiveness as a catalyst for social exchange (Kim & Kim, 2021). Therefore, in this research, we regarded credibility is a general term that mainly focuses on three requirements: homophily, trustworthiness, and expertise.

People prefer to stimulate a positive affective reaction toward others who are similar to them, because the sense of homophily can reduce the uncertainty of a relationship (Hu et al., 2020; Sakib et al., 2020). On social commerce platforms, researchers found that consumers tend to share information with others who possess similar shopping goals, interests, and styles, which can foster the development of parasocial interactions among them (Xiang et al., 2016). As influencer marketing can be viewed as taking place on designated social commerce platforms, the interactions between influencers and consumers tend to be quite similar. The greater the perceived homophily between consumers’ beliefs and those of an SMIs, the stronger their likelihood to sustain their parasocial relationships with the influencer (Hu et al., 2020; Masuda et al., 2022).

On the other hand, people are naturally inclined to connect with some ones who share their interests or find themselves in similar life situations. Homophily-based credibility plays an important role in driving the persuasive process in online discussion groups (Z. Wang et al., 2008). Unlike traditional advertising, SMIs not only seek to inform consumers about new products, but also convey emotions regarding the FOMO (Bläse et al., 2024). The tendency of consumers to align themselves with individuals sharing similar interests or circumstances enhances the potential for homophily to amplify the FOMO experienced in the context of influencer marketing (Y. Li & Peng, 2021). Following these evidences, we hypothesize:

H4a. The homophily of an SMI is positively related to parasocial relationships.

H4b. The homophily of an SMI is positively related to FOMO.

SMIs, through their carefully curated content across various platforms, cultivate a sense of relatability and trust that often surpasses that of traditional celebrities (Koay & Lim, 2025). Their perceived trustworthiness significantly shapes consumer perceptions: when consumers believe an SMI is trustworthy, they are more likely to consider the communicated messages as highly credible. (S. W. Wang et al., 2017). In social commerce, the role of endorsers’ trustworthiness in the strength of parasocial relationships has been indicated by Lou and Kim (2019). Consumers are likely to develop more positive attitudes toward establishing a close relationship with the endorser when they perceive a high level of trustworthiness (Yuan et al., 2021). Besides, trustworthiness will provide a subjective guarantee that a good experience in the future will be obtained as consumers believe that one SMI is dependable and honest to recommend products to them (Gao et al., 2015). In such context, consumers who do not acquire a product endorsed by that SMI are likely to experience emotional responses, leading to feelings of “missing out.” Based on the above analysis, we provide the following two hypotheses:

H5a. The trustworthiness of an SMI positively influences parasocial relationships.

H5b. The trustworthiness of an SMI positively influences FOMO.

Expertise is related to the depth of knowledge about products and the particular domain in which the source is making claims (Ismagilova et al., 2020). Since people generally align with the opinions of experts and often modify their attitudes based on expert advice, perceived expertise is a crucial factor in interpersonal interaction. Existing studies regarding influencer marketing have found that an SMI’s expertise in a particular domain can positively influence parasocial relationships with their followers (e.g., Ashraf et al., 2023; E. C.-X. Aw & Chuah, 2021). In addition, expertise, also known as the authority, competence, and qualification a communicator possesses, has significant value in persuasion. Consumers who draw upon information from these experts frequently feel a sense of reward, perceive enhanced value, and subsequently experience an amplified desire to mimic (Ki & Kim, 2019). This heightened value perception can lead to a fear of not acquiring a product highly praised by SMIs who are viewed as experts in certain domains, manifesting the “missing out” sensation (Good & Hyman, 2020), which in turn may give rise to FOMO. Therefore, the following two hypotheses are proposed:

H6a. The expertise of an SMI has a positive effect on parasocial relationships.

H6b. The expertise of an SMI has a positive effect on FOMO.

In conclusion, according to the above hypotheses, we proposed a research model by integrating parasocial relationship, source credibility model, FOMO, and serendipity. Specifically, factors such as an SMI’s homophily, trustworthiness, and expertise, derived from the source credibility model, serve as antecedents of parasocial relationship and FOMO which subsequently influence consumers’ purchase intention. Additionally, while serendipity has a direct influence on purchase intention, it also strengthens the relationship between FOMO and purchase intention when perceived serendipity is high. Figure 1 shows the research model of our study.

Figure 1.

Research model.

Methodology

Measures

To maintain content validity, we adapted all measures for latent constructs from existing literature, ensuring their relevance and alignment with the context of this study. The construct of parasocial relationship was measured with four items following Farivar et al. (2021). Serendipity was measured with items adapted from Chung et al. (2017). To measure homophily, we adapted four items from Masuda et al. (2022). The items of expertise were modified from Ki and Kim (2019). Besides, measures for trustworthiness and purchase intention were modified from Wiedmann and von Mettenheim (2021). Finally, the scales to measure FOMO were obtained from the study by Dinh and Lee (2022). The Appendix of this paper depicts the measures of each variable in the questionnaire. All the items were rated on a 7-point Likert scale, ranging from 1 = strongly disagree to 7 = strongly agree.

Data Collection

Data collection was conducted via an online survey aimed at subjects who have followed SMIs. The link of our online questionnaire was disseminated through social media platforms. Initially, the link was distributed to the 30 experienced consumers and 7 IS scholars who participated in our pilot study. Subsequently, these participants were encouraged to share the online questionnaire within their social media groups. Initially, a total of 352 questionnaires were collected. During the survey process, a purposive sampling technique was applied in line with prior literature regarding influencer marketing (e.g., E. C. X. Aw et al., 2023; Jacobson & Harrison, 2022), because only a limited population of people can possess the required information. In our survey, eligibility was limited to individuals who possess at least one social media account and have followed SMIs. Initially, participants were asked to identify a specific SMI they follow, as well as the social media platform they use to follow this influencer, serving as a screening question. Data from participants who failed the screening question were removed. Besides, to retrieve more accurate responses from the all participants, a brief definition of SMIs was also explicated in the remainder of the questionnaire. After the data screen, 335 responses were used for analysis. Demographic details are shown in Table 1.

Table 1.

Demographic Characteristics of Respondents.

Measure	Items	Frequency	Percentage (%)
Gender	Male	142	46.3
Gender	Female	165	53.7
Age	<20	34	11.1
	21–29	168	54.7
	30–39	78	25.4
	40–49	23	7.5
	≥50	4	1.3
Monthly income (CNY)	<4,000	50	16.3
	4,000–5,999	119	38.8
	6,000–7,999	81	26.4
	8,000–9,999	32	10.4
	≥10,000	25	8.1
Use time (hours per day)	<0.5	133	43.3
	0.5–1	125	40.7
	1–2	41	13.4
	>2	8	2.6
Educational level	High school and below	62	20.2
	College/university	203	66.1
	Graduate school and above	42	13.7

Data Analysis

Partial least squares structural equation modeling (PLS-SEM) approach was employed to conduct data analysis in our research. Compared with covariance-based SEM (CB-SEM), PLS-SEM is deemed more appropriate for exploratory and prediction-oriented research, particularly when dealing with complex models (Gefen et al., 2000; Hair et al., 2011), which a scenario that aligns with our research context. Following a two-step analysis procedure (Anderson & Gerbing, 1988), measurement model and structural model were checked respectively.

In addition, as this study used single-source data based on a self-reported survey method, thus common method bias (CMB) may exist. We performed Harman’s one-factor test to statistically assess the possible impacts from CMB. Using principal component analysis, all the indicators were entered into an exploratory factor analysis with unrotated principal component analysis. The results show that the largest variance explained by individual factors was 32.38% is less than 40%. Therefore, CMB should not be a serious issue for this study.

Results

Measurement Model

We assessed the measurement model based on its reliability, convergent validity, and discriminant validity (Hair et al., 2017). As shown in Table 2, all item loadings are above the threshold of .7. Cronbach’s α for each construct ranges from .792 to .910. Moreover, all the values of composite reliability (CR) are above .85, which are much larger than the threshold value of .70. According to the criteria recommended by Fornell and Larcker (1981) and Nunnally (1978), we can conclude that the internal consistency (reliability) of the measures for each construct can be confirmed. In addition, all the AVE values are well above the required minimum level of 0.50, showing good convergent validity for all measures (Fornell & Larcker, 1981).

Table 2.

Reliability and Validity.

Constructs	Items	Factor loadings	CA	AVE	CR
Expertise	EP1	0.834	.792	0.704	.877
	EP2	0.840
	EP3	0.844
FOMO	FOMO1	0.844	.910	0.690	.930
	FOMO2	0.851
	FOMO3	0.804
	FOMO4	0.810
	FOMO5	0.838
	FOMO6	0.836
Homophily	HM1	0.849	.882	0.738	.918
	HM2	0.832
	HM3	0.871
	HM4	0.882
Parasocial relationship	PR1	0.842	.871	0.721	.912
	PR2	0.848
	PR3	0.849
	PR4	0.857
Purchase intention	PI1	0.894	.872	0.796	.921
	PI2	0.898
	PI3	0.884
Serendipity	SE1	0.865	.890	0.751	.923
	SE2	0.898
	SE3	0.839
	SE4	0.862
Trustworthiness	TW1	0.872	.906	0.779	.934
	TW2	0.877
	TW3	0.887
	TW4	0.896

Note. CA = Cronbach’s alpha; CR = composite reliability; AVE = average variance extracted.

To assess discriminant validity, Fornell and Larcker (1981) criterion was first applied. In Table 3, the values of the square roots of the AVEs of each construct are greater than the inter-construct correlations, suggesting the existence of discriminant validity. Besides, to further confirm discriminant validity, the heterotrait-monotrait (HTMT) method was also conducted as suggested by Henseler et al. (2015). The values of HTMT ratio in Table 4 all fall below the criterion of .85 (Henseler et al., 2015), further confirming that there is no discriminant validity issue in our research.

Table 3.

Discriminant Validity.

Constructs	1	2	3	4	5	6	7
1. Expertise	0.839
2. FOMO	0.358	0.831
3. Homophily	0.215	0.310	0.859
4. Parasocial relationship	0.304	0.363	0.358	0.849
5. Purchase intention	0.272	0.375	0.393	0.479	0.892
6. Serendipity	0.320	0.245	0.268	0.405	0.284	0.866
7. Trustworthiness	0.443	0.353	0.368	0.339	0.495	0.266	0.883

Note. The bold values represent the square roots of the AVE.

Table 4.

Heterotrait-Monotrait Ratio (HTMT).

Constructs	1	2	3	4	5	6	7
1. Expertise	1.000
2. FOMO	0.416	1.000
3. Homophily	0.254	0.340	1.000
4. Parasocial relationship	0.361	0.404	0.404	1.000
5. Purchase intention	0.321	0.414	0.445	0.548	1.000
6. Serendipity	0.370	0.265	0.292	0.454	0.314	1.000
7. Trustworthiness	0.509	0.383	0.416	0.379	0.554	0.289	1.000

Structural Model

The quality of the structural model was assessed by path coefficient values, explanatory power (R²) and predictive relevance (Q²). The path coefficients and their statistical significance were tested using the bootstrapping with 5,000 subsamples. All the details of hypotheses testing are shown in Figure 2. FOMO has a positive effect on purchase intention (β = .216, p < .001) Therefore, H1 is supported. Parasocial relationship is positively related to purchase intention (β = .362, p < .001) and FOMO (β = .202, p < .001) supporting H3a and H3b. Besides, homophily is positively associated with parasocial relationship (β = .259, p < .001) and FOMO (β = .141, p < .01), thus supporting H4a and H4b. Trustworthiness is positively related to parasocial relationship (β = .167, p < .01) and FOMO (β = .143, p < .05), showing that H5a and H5b are supported. Additionally, expertise has significant and positive impacts on parasocial relationship (β = .175, p < .01) and FOMO (β = .203, p < .001), providing supports for H6a and H6b. However, purchase intention cannot be significantly influenced by serendipity (β = .088, p > .05). Thus, H2a is not supported. Furthermore, the moderation analysis of serendipity was examined by using the PLS-product indicator approach. To alleviate multicollinearity, we mean-centered each measurement (Aiken & West, 1991), and then the interaction effect (serendipity × FOMO) was calculated. The result demonstrates that the interaction between serendipity and FOMO is significantly and positively related to purchase intention (β = .194, p < .01), supporting H2b.

Figure 2.

Results of the research model.

The R² value for purchase intentions indicates 31.4% of the variance. The results of R² also shows that the model accounts for 20.2% and 24% of the variances in parasocial relationship and FOMO respectively. Meanwhile, the Q² value is used to interpret predictive relevance of the model with regard to each endogenous construct. The Q² values are 0.241 for purchase intention, 0.140 for parasocial relationship, and 0.159 for FOMO. Given that the Q² of all endogenous constructs are above zero, we can regard this as indicative of good predictive relevance (Hair et al., 2017).

Discussion

Theoretical Implications

This study offers several theoretical implications. First, it presents a theory-based research model by adapting it to the practices prevalent in the influencer marketing campaigns, focusing on the interplay between smart recommendation algorithms and the emotional anxiety. The results of the data analysis demonstrate that the source credibility of SMIs and the parasocial relationships perceived by consumers are important determinants of purchase intention within the influencer marketing context. The homophily, trustworthiness, and expertise of SMIs not only exert a direct positive impact on FOMO, but also play important roles in shaping parasocial relationships with consumers, which in turn influence FOMO. Consequently, these factors ultimately affect purchase intentions.

Second, a deeper comprehension of FOMO in the influencer marketing sphere has been garnered by shedding light on its ties to source credibility and parasocial relationships. When examining purchase intention in influencer marketing context, it is insufficient to solely focus on the information-level interactions between consumers and SMIs. The empirical findings indicate that high source credibility enhances a listener’s positive perception, thereby increasing receptivity. Influencer marketing introduces new products to consumers while simultaneously inducing feelings of FOMO related to potential missed buying opportunities. As an anticipatory emotion, FOMO sheds light on individuals’ decision-making processes and corresponding behaviors. Consequently, this study integrates FOMO into the “source credibility–parasocial relationship” framework, laying the foundation for uncovering novel psychological processes that influence consumers’ purchasing decisions.

Third, this research enriches the understanding of serendipity by examining its influence on consumers’ purchase intentions within the unique context of influencer marketing. Our empirical results delve deeper into the role of serendipity by incorporating the phenomenon of FOMO, highlighting its moderating effect on the relationship between FOMO and purchase intention. Although serendipity—cultivated by social media affordances—does not directly affect consumers’ purchase intention, it strengthens the positive relationship between FOMO and purchase intention. In other words, serendipitous experiences lead consumers to perceive browsed products as having high diagnostic value, reinforcing the belief that these products should not be overlooked. This process elucidates the mechanism through which SMIs persuade consumers. This process elucidates the mechanism through which SMIs persuade consumers.

Practical Implications

The findings of our research also provide some practical implications. First, the significant effects of source credibility on parasocial relationships suggest that the credibility of SMIs is crucial for effective marketing communications, as it not only establishes the authenticity and persuasive power of the message but also directly influences audience acceptance and trust. Consequently, social media marketers should actively explore and implement scientifically grounded strategies to identify and select the most suitable influencers as partners, thereby optimizing their marketing efforts. In this context, our study provides comprehensive evaluation criteria that encompass key dimensions, such as an influencer’s expertise, trustworthiness, and homophily, aiming to offer marketers a well-rounded framework to assess influencer attributes and, in turn, foster stronger emotional connections and enhanced trust between consumers and brands.

Second, the connection between source credibility and FOMO in driving purchase intention offers valuable and actionable insights for marketers who are focused on increasing online sales. FOMO not only amplifies consumers’ concerns about falling behind their peers, but also strengthens the belief that products endorsed by credible sources are more likely to meet their needs and desires. For SMIs looking to enhance the effectiveness of their promotional efforts, leveraging FOMO-driven emotions can be a powerful tool. This can be achieved through strategically designed events or campaigns that highlight their expertise and trustworthiness, which in turn build consumer confidence. Additionally, emphasizing their similarities and shared values with their audience can foster a sense of connection and relatability, further intensifying FOMO and motivating consumers to take action. By tapping into these psychological drivers, marketers can more effectively influence consumer behavior and drive higher engagement levels.

Third, social media platforms, by their very nature, provide open, and dynamic environments that facilitate serendipity, where unexpected discoveries can occur. Given this unique characteristic, marketing practitioners should strategically design and deliver tailored messages to the right consumers based on their individual preferences and behavior patterns. By collaborating closely with SMIs who possess a deep understanding of their followers’ tastes and interests, marketers can refine the presentation and format of product information, ensuring alignment with both the platform’s context, and the consumer’s browsing experience across various pages and scenarios. This personalized approach ensures that the message resonates with the target audience and enhances the potential for serendipitous encounters between consumers and product offerings. As a result, consumers are more likely to develop a sense of receptivity toward endorsements from SMIs, thereby heightening the chances of fostering positive and spontaneous interactions that lead to stronger consumer relationships.

Limitations and Future Research

Some limitations during our research process also exist. First, the context of our study is social media in mainland of China. It is possible that our results are not generalizable to other cultural contexts. Research in the next stage could broaden its scope by acquiring data directly from SMIs and various social media platforms around the world, aiming to derive more comprehensive and diverse conclusions. Second, the data for this study were gathered through self-reported surveys at a single time point, making the study vulnerable to common method bias that may inflate or distort the observed relationships (Podsakoff et al., 2003). Such biases include recall bias and social desirability bias, whereby participants’ responses might not accurately reflect their true behaviors or attitudes. This reliance on a single data collection technique suggests that the findings should be interpreted with caution, underscoring the need for future research to incorporate diverse data sources or adopt longitudinal designs to mitigate these inherent limitations. Therefore, future studies should consider longitudinal investigations from diverse social media platforms to reveal causal relationships empirically and then find out more insights into the effects of parasocial relationship, FOMO, serendipity, and their antecedents on purchase intention within the influencer marketing context.

Footnotes

Appendix. Measurement Items

ORCID iD

Shu Liu

Ethical Considerations

I certify that this manuscript is original and has not been published. Informed consent was obtained from all individual participants included in the study.

Funding

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

Declaration of Conflicting Interests

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

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

Aiken

L. S.

West

S. G.

(1991). Multiple regression: Testing and interpreting interactions. Sage.

Akram

Ansari

A. R.

Ulhaq

Yan

(2023). Cosmetics makers have always sold ‘hope in a jar’! Understanding the cosmetics purchase intention in the Chinese mobile commerce environment. Journal of Retailing and Consumer Services, 73, 103337.

Amos

Holmes

Strutton

(2008). Exploring the relationship between celebrity endorser effects and advertising effectiveness. International Journal of Advertising, 27(2), 209–234.

Anderson

J. C.

Gerbing

D. W.

(1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423.

Ashraf

Hameed

Saeed

S. A.

(2023). How do social media influencers inspire consumers’ purchase decisions? The mediating role of parasocial relationships. International Journal of Consumer Studies, 47(4), 1416–1433.

E. C.-X.

Chuah

S. H.-W.

(2021). “Stop the unattainable ideal for an ordinary me!” fostering parasocial relationships with social media influencers: The role of self-discrepancy. Journal of Business Research, 132, 146–157.

E. C. X.

Tan

G. W. H.

Chuah

S. H. W.

Ooi

K. B.

Hajli

(2023). Be my friend! Cultivating parasocial relationships with social media influencers: Findings from PLS-SEM and fsQCA. Information Technology and People, 36(1), 66–94.

Bläse

Filser

Kraus

Puumalainen

Moog

(2024). Non-sustainable buying behavior: How the fear of missing out drives purchase intentions in the fast fashion industry. Business Strategy and the Environment, 33, 626–641.

Bright

L. F.

Logan

(2018). Is my fear of missing out (FOMO) causing fatigue? Advertising, social media fatigue, and the implications for consumers and brands. Internet Research, 28(5), 1213–1227.

10.

Buglass

S. L.

Binder

J. F.

Betts

L. R.

Underwood

J. D. M.

(2017). Motivators of online vulnerability: The impact of social network site use and FOMO. Computers in Human Behavior, 66, 248–255.

11.

Casaló

L. V.

Flavián

Ibáñez-Sánchez

(2020). Influencers on Instagram: Antecedents and consequences of opinion leadership. Journal of Business Research, 117, 510–519.

12.

Chung

Song

H. G.

Lee

(2017). Consumers’ impulsive buying behavior of restaurant products in social commerce. International Journal of Contemporary Hospitality Management, 29(2), 709–731.

13.

Dinh

T. C. T.

Lee

(2022). “I want to be as trendy as influencers”– How “fear of missing out” leads to buying intention for products endorsed by social media influencers. Journal of Research in Interactive Marketing, 16(3), 346–364.

14.

Dodds

W. B.

Monroe

K. B.

Grewal

(1991). Effects of price, brand, and store information on buyers’ product evaluations. Journal of Marketing Research, 28(3), 307–319.

15.

Farivar

Wang

Yuan

(2021). Opinion leadership vs. para-social relationship: Key factors in influencer marketing. Journal of Retailing and Consumer Services, 59, 102371.

16.

Fornell

Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

17.

Gao

Waechter

K. A.

Bai

(2015). Understanding consumers’ continuance intention towards mobile purchase: A theoretical framework and empirical study – A case of China. Computers in Human Behavior, 53, 249–262.

18.

Gefen

Straub

Boudreau

M.-C.

(2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(1), 7.

19.

Good

M. C.

Hyman

M. R.

(2020). ‘Fear of missing out’: Antecedents and influence on purchase likelihood. Journal of Marketing Theory and Practice, 28(3), 330–341.

20.

Grange

Benbasat

Burton-Jones

(2019). With a little help from my friends: Cultivating serendipity in online shopping environments. Information Management, 56(2), 225–235.

21.

Duan

(2024). Exploring the dynamics of consumer engagement in social media influencer marketing: From the self-determination theory perspective. Humanities and Social Sciences Communications, 11(1), 587.

22.

Hair

J. F.

Hult

G. T. M.

Ringle

C. M.

Sarstedt

(2017). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.

23.

Hair

J. F.

Ringle

C. M.

Sarstedt

(2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152.

24.

Henseler

Ringle

C. M.

Sarstedt

(2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.

25.

Hodkinson

(2019). ‘fear of missing out’ (FOMO) marketing appeals: A conceptual model. Journal of Marketing Communications, 25, 65–88.

26.

Min

Han

Liu

(2020). Understanding followers’ stickiness to digital influencers: The effect of psychological responses. International Journal of Information Management, 54, 102169.

27.

Hung

Chan

K. W.

Tse

C. H.

(2011). Assessing celebrity endorsement effects in China: A consumer-celebrity relational approach. Journal of Advertising Research, 51(4), 608–623.

28.

Ismagilova

Slade

Rana

N. P.

Dwivedi

Y. K.

(2020). The effect of characteristics of source credibility on consumer behaviour: A metaanalysis. Journal of Retailing and Consumer Services, 53(3), 101736–101739.

29.

Jacobson

Harrison

(2022). Sustainable fashion social media influencers and content creation calibration. International Journal of Advertising, 41(1), 150–177.

30.

Kim

(2019). The mechanism by which social media influencers persuade consumers: The role of consumers’ desire to mimic. Psychology and Marketing, 36(10), 905–922.

31.

Kim

D. Y.

Kim

H.-Y.

(2021). Trust me, trust me not: A nuanced view of influencer marketing on social media. Journal of Business Research, 134, 223–232.

32.

Koay

K. Y.

Lim

W. M.

(2025). Congruence effects in social media influencer marketing: The moderating role of wishful identification in online impulse buying intentions. Journal of Product & Brand Management, 34(3), 265–278.

33.

Labrecque

L. I.

(2014). Fostering consumer–brand relationships in social media environments: The role of parasocial interaction. Journal of Interactive Marketing, 28(2), 134–148.

34.

Lee

J. A.

Bright

L. F.

Eastin

M. S.

(2021). Fear of missing out and consumer happiness on Instagram: A serial mediation of social media influencer-related activities. Cyberpsychology Behavior and Social Networking, 24(11), 762–766.

35.

Lee

J. E.

Watkins

(2016). YouTube vloggers’ influence on consumer luxury brand perceptions and intentions. Journal of Business Research, 69, 5753–5760.

36.

L. H.

Hancer

(2021). Using social learning theory in examining YouTube viewers’ desire to imitate travel vloggers. Journal of Hospitality and Tourism Technology, 12(3), 512–532.

37.

Shi

Zhang

Zheng

(2021). The continued use of social commerce platforms and psychological anxiety-the roles of influencers, informational incentives and FoMO. International Journal of Environmental Research and Public Health, 18(22), 12254.

38.

Peng

(2021). Influencer marketing: Purchase intention and its antecedents. Marketing Intelligence & Planning, 39(7), 960–978.

39.

Lou

Kim

H. K.

(2019). Fancying the new rich and famous? Explicating the roles of influencer content, credibility, and parental mediation in adolescents’ parasocial relationship, materialism, and purchase intentions. Frontiers in Psychology, 10, 2567.

40.

Makri

Blandford

(2012). Coming across information serendipitously: Part 2-a classification framework. Journal of Documentation, 5(68), 1–16.

41.

Makri

Blandford

Woods

Sharples

Maxwell

(2014). ‘Making my own luck’: Serendipity Strategies and how to support them in digital information environments. Journal of the Association for Information Science and Technology, 65(11), 2179–2194.

42.

Masuda

Han

S. H.

Lee

(2022). Impacts of influencer attributes on purchase intentions in social media influencer marketing: Mediating roles of characterizations. Technological Forecasting and Social Change, 174, 121246.

43.

McCay-Peet

Toms

E. G.

(2011). The serendipity quotient. Proceedings of the American Society for Information Science and Technology, 48(1), 1–4.

44.

McCay-Peet

Toms

E. G.

Kelloway

E. K.

(2015). Examination of relationships among serendipity, the environment, and individual differences. Information Processing & Management, 51(4), 391–412.

45.

Melnychuk

H.-A.

Arasli

Nevzat

(2024). How to engage and attract virtual influencers’ followers: A new non-human approach in the age of influencer marketing. Marketing Intelligence & Planning, 42(3), 393–417.

46.

Law

Lam

Cui

(2023). A study of the factors influencing the viewers’ satisfaction and cognitive assimilation with livestreaming commerce broadcast in Hong Kong. Electronic Commerce Research, 23(3), 1565–1590.

47.

Niu

Huang

Chen

(2021). Spanning from diagnosticity to serendipity: An empirical investigation of consumer responses to product presentation. International Journal of Information Management, 60, 102362.

48.

Nunnally

J. C.

(1978). Psychometric theory. McGraw-Hill.

49.

Ohanian

(1990). Construction and validation of a scale to measure celebrity endorsers’ perceived expertise, trustworthiness and attractiveness. Journal of Advertising, 19(3), 39–52.

50.

Park

H. J.

Lin

L. M.

(2020). The effects of match-ups on the consumer attitudes toward internet celebrities and their live streaming contents in the context of product endorsement. Journal of Retailing and Consumer Services, 52, 101934.

51.

Pick

(2021). Psychological ownership in social media influencer marketing. European Business Review, 33(1), 9–30.

52.

Podsakoff

P. M.

MacKenzie

S. B.

Lee

J.-Y.

Podsakoff

N. P.

(2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

53.

Przybylski

A. K.

Murayama

DeHaan

C. R.

Gladwell

(2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29(4), 1841–1848.

54.

Reisenzein

Horstmann

Schützwohl

(2019). The cognitive-evolutionary model of surprise: A review of the evidence. Topics in Cognitive Science, 11(1), 50–74.

55.

Rungruangjit

(2022). What drives Taobao live streaming commerce? The role of parasocial relationships, congruence and source credibility in Chinese consumers’ purchase intentions. Heliyon, 8(6), e09676.

56.

Sakib

M. D. N.

Zolfagharian

Yazdanparast

(2020). Does parasocial interaction with weight loss vloggers affect compliance? The role of vlogger characteristics, consumer readiness, and health consciousness. Journal of Retailing and Consumer Services, 52, 101733.

57.

Saternus

Mihale-Wilson

Hinz

(2024). Influencer marketing on Instagram—the optimal disclosure strategy from influencers’ and marketers’ perspectives. Electronic Markets, 34, 60.

58.

Shin

D.-H.

Biocca

(2017). Explicating user behavior toward multi-screen adoption and diffusion: User experience in the multi-screen media ecology. Internet Research, 27(2), 338–361.

59.

Sokolova

Kefi

(2020). Instagram and YouTube bloggers promote it, why should I buy? How credibility and parasocial interaction influence purchase intentions. Journal of Retailing and Consumer Services, 53, 101742.

60.

Sun

Bao

(2023). Live streaming commerce: A compulsive buying perspective. Management Decision, 61(11), 3278–3294.

61.

Valenzuela

Mellers

Strebel

(2010). Pleasurable surprises: A cross-cultural study of consumer responses to unexpected incentives. Journal of Consumer Research, 36(5), 792–805.

62.

Wahab

H. K. A.

Tao

Tandon

Ashfaq

Dhir

(2022). Social media celebrities and new world order. What drives purchasing behavior among social media followers? Journal of Retailing and Consumer Services, 68, 103076.

63.

Wang

S. W.

Kao

G. H.-Y.

Ngamsiriudom

(2017). Consumers’ attitude of endorser credibility, brand and intention with respect to celebrity endorsement of the airline sector. Journal of Air Transport Management, 60, 10–17.

64.

Wang

Walther

J. B.

Pingree

Hawkins

R. P.

(2008). Health information, credibility, homophily, and influence via the internet: Web sites versus discussion groups. Health Communication, 23(4), 358–368.

65.

Wiedmann

K.-P.

von Mettenheim

(2021). Attractiveness, trustworthiness and expertise – Social influencers’ winning formula? Journal of Product & Brand Management, 30(5), 707–725.

66.

Xiang

Zheng

Lee

M. K. O.

Zhao

(2016). Exploring consumers’ impulse buying behavior on social commerce platform: The role of parasocial interaction. International Journal of Information Management, 36(3), 333–347.

67.

Yang

Men

(2023). Recommendation content matters! Exploring the impact of the recommendation content on consumer decisions from the means-end chain perspective. International Journal of Information Management, 68, 102589.

68.

Yuan

Moon

Wang

Kim

K. H.

(2021). Study on the influencing of B2B parasocial relationship on repeat purchase intention in the online purchasing environment: An empirical study of B2B E-commerce platform. Industrial Marketing Management, 92, 101–110.

69.

Zafar

A. U.

Qiu

Shahzad

(2020). Do digital celebrities’ relationships and social climate matter? Impulse buying in f-commerce. Internet Research, 30(6), 1731–1762.

70.

Zhao

Shen

Zhang

(2022). The study on the impact of short video tourism vloggers at social media platform on online sharing intention. Frontiers in Psychology, 13, 905002.

71.

Zhao

Wagner

(2023). How TikTok leads users to flow experience: Investigating the effects of technology affordances with user experience level and video length as moderators. Internet Research, 33(2), 820–849.