Abstract
Purpose
This study embraces the call for exploring the determinants of continuous intention in TikTok. Taking the perspective of social influence, this study not only tries to explore the contextual sources of two types of social influence but also aims to unveil the influence mechanism of how social influence affects TikTok viewers’ continuous intention.
Design/methodology/approach
This study empirically analyzes how TikToker attractiveness, co-viewer participation, platform reputation and content appeal affect informative and normative social influence and then lead to the continuous intention of TikTok. Based on 547 valid survey data, this study adopts a mixed analytical approach for data analysis by integrating structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA).
Findings
SEM results unveil that content appeal is the most critical antecedent of informational social influence, while the TikToker attractiveness and platform reputation have no effect on it. Differently, all four external sources positively lead to normative social influence. Among them, content appeal and co-viewer participation influence the most. The influences of both two types of social influence on continuous intention are demonstrated. FsQCA results reveal seven alternative configurations that are sufficient for influencing continuance intention and further complement and reinforce the SEM findings.
Originality/value
Addressing the critical contextual elements of TikTok, this study explores and confirms the sources which may engender social influence. The authors also demonstrate the critical role of social influence in affecting TikTok viewers’ continuous intentions by the hybrid analytical approach, which contributes to existing academic literature and practitioners.
Keywords
Citation
Jia, Q., Xu, X., Zhou, M., Liu, H. and Chang, F. (2023), "Exploring the determinants of continuous intention in TikTok from the perspective of social influence: a mixed approach of SEM and fsQCA", Journal of Electronic Business & Digital Economics, Vol. 2 No. 1, pp. 45-68. https://doi.org/10.1108/JEBDE-07-2022-0016
Publisher
:Emerald Publishing Limited
Copyright © 2023, Qingdan Jia, Xiaoyu Xu, Minhong Zhou, Haodong Liu and Fangkai Chang
License
Published in International Hospitality Review. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
The torrents of TikTok have swept across the globe in recent years. The ascent of TikTok has made the number of its users double worldwide between 2019 and 2021 (291.4 million to 655.9 million) (Insider intelligence, 2022). As a short-form video-sharing social media app, TikTok penetrated social media users on this planet to record, edit and share videos. Among the 4.48 billion active social media users, 22.32% of them are regular TikTok users (Blind, 2022). TikTok not only increased its first-time installs by almost 400% year-over-year but also consolidated its global users (Statista, 2022). It has one billion monthly active users and has become one of the most successful social media apps currently worldwide (CNBC, 2022; Wang, 2020).
In essence, the TikTok usage environment is composed of various social-related components that influence users’ social experience. As the producer of short video content, TikToker uploads the video and interacts with viewers. The attractive TikToker with a charming appearance, friendly personality and enriched knowledge promotes viewers to indulge in watching, interacting and participating in TikTok activities (Lee & Watkins, 2016). With the advanced functions of commenting, clicking likes and sharing videos, viewers can interact with other co-viewers to exchange and share their opinions, information and knowledge actively (Tsai & Pai, 2013). Provided with various, attractive and exquisite video content (Cuesta-Valino, Gutierrez-Rodriguez, & Duran-Alamo, 2022), TikTok viewers can obtain pleasure and entertainment, acquire useful knowledge from others and get to know other people's lives, thoughts and opinions. An increasing number of viewers are inspired by this novel, reputable and interactive platform to seek information and interact with others (Scherr & Wang, 2021).
In such an interactive environment with abundant social cues, information exchanges and group association are both highly encouraged and facilitated in TikTok (Huang & Benyoucef, 2013). As a result, users’ preferences, decisions and behaviors are likely to be influenced during their usage of TikTok (Amblee & Bui, 2011). Social influence is the result of an individual’s interaction and refers to how an individual is influenced by others (Hu, Chen & Davidson, 2019). The various components of the social environment can be regarded as the external sources of social influence, which will influence individuals' beliefs during their interaction with the social environment (Li, 2013). Then, the individuals' beliefs will impact attitudes and lead to behavioral intention. In other words, individuals' behaviors are probably determined by influences generated by external sources (McFarland & Hamilton, 2006). Thus, the determinants and sources of influence can be interpreted by the particular components of TikTok (Liang & Turban, 2011).
In addition, the long-term value is more likely to be generated by the continuance usage of users, such as better customer relationships, more active social interaction and participation and higher business profits (Yang, 2021). Thus, the rapid development momentum of TikTok prompts researchers to explore TikTok users’ continuous intentions (Song, Zhao, Yao, Ba, & Zhu, 2021). Considering the effect of social influence on users’ knowledge, perception and behavior intention (Fu, Lu, Chen, & Farn, 2020), employing social influence theory to explore the external sources of social influence and its influence mechanism on TikTok viewers’ decision-making process is a very enlightening research topic.
The development of TikTok is still in its infancy. It is generally considered that the investigation into TikTok began in 2020 (Cuesta-Valino et al., 2022). Only a few studies attempt to adopt limited theoretical models to explore individuals’ behavioral intentions in TikTok (e.g. purchase and adoption), including the uses and gratification (U&G) theory (Cuesta-Valino et al., 2022; Meng & Leung, 2021; Scherr & Wang, 2021), affordance theory (Song et al., 2021) and human–computer interaction theory (Wang, 2020), which mainly focus on the features of TikTok. For example, several researchers proposed TikTok affords several technological features such as live streaming, searching, meta-voicing (Song et al., 2021), personalized recommendation algorithms and easy-to-use editing (Du, Liechty, Santos, & Park, 2020). Most prior studies investigated the social attributes of TikTok, such as interactivity (Zhang, Xu, & Ye, 2022), entertainment (Meng & Leung, 2021; Zhang et al., 2022) and socially rewarding self-presentation (Scherr & Wang, 2021). These pioneering studies proposed that a social streaming app (Xu & Tayyab, 2021), such as TikTok, can exert a strong influence on individual perception and motives for their usage (Song et al., 2021), attachment (Wang, Yang, & Zhang, 2021), stickiness (Cuesta-Valino et al., 2022) and purchase intention (Zhao & Wang, 2020). However, there is still a paucity of scientifically empirical studies to uncover the rationale behind users’ perceptions, attitudes and behaviors towards TikTok. The enlightening role that social influence theory plays in determining viewers’ behaviors has been ignored in existing studies investigating TikTok. It is still unclear what external sources of social influence and the underlying mechanisms may influence individuals' continuance use intention towards TikTok.
In addition, individuals’ perceptions and behaviors are formed by integrating distinct factors into a whole. These factors are perceived in a holistic, integrated way, with complex interactions (Maier, Laumer, Joseph, Mattke, & Weitzel, 2021). Thus, TikTok viewers’ perceptions and continuance usage intention may be driven by complex interactions of antecedents. However, there is a lack of knowledge regarding how critical external factors and distinct types of social influence can be combined differently to predict consequential behavioral decisions.
Employing the social influence theory, a research model is proposed to address the following research questions: (1) What are the sources of social influence in TikTok? (2) What is the mechanism of social influence affecting users’ continuous usage of TikTok? and (3) What configurations of antecedents effectively determine viewers’ continuance intention toward TikTok? In an effort to fill the research gaps, leveraging social influence theory, we developed a conceptual model that encompasses the sources of social influence in TikTok, the distinct types of social influence and TikTok users’ continuous intention. Utilizing a mixed analytical approach, we conducted the data analysis by employing both structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to verify the influence process of TikTok and further explore more different configurations based on the principle of set-theoretic configurational to validate and complement the SEM results.
This study contributes to both existing literature and practice. First, this study represents an early attempt to adopt the perspective of social influence to explore how TikTok influences users’ behavioral intentions. Considering that the context of TikTok creates a social environment that enables users’ social interaction, employing social influence theory can not only address the social nature of TikTok but also expand the application of social influence theory in a novel research context. Second, this study carefully clarifies the contextual sources of social influence in TikTok. Addressing the key elements that form the TikTok usage environment, this study comprehensively incorporates and examines the sources which may engender social influence in TikTok. It provides a holistic perspective to deepen the understanding of TikTok's influence on users. Third, by dissecting social influence into normative and informational aspects, this study extends the research on social influence and contributes to the investigation of social influence mechanisms in TikTok by exploring, examining and interpreting each of them in detail. A more detailed exploration of social influence provides useful knowledge of the influence mechanisms, routes and processes of TikTok. Fourth, this study provides methodological contributions to existing research by applying the hybrid analytical approach of partial least square structural equation modeling (PLS-SEM) and fsQCA, which unveils the viewers’ continuance intention in TikTok from the perspective of complex, nonlinear and synergistic analysis. Moreover, the results provide valuable suggestions, information and opportunities for TikTok and other social streaming practitioners to investigate and develop strategies for attracting, maintaining and retaining users.
This study is unfolded as follows. First, it begins with a literature review related to TikTok and social influence theory to establish a theoretical foundation in Section 2. Then, in Section 3 and Section 4, this study constructs a research model, develops the measurement and collects empirical data for analysis. In Section 5, the mixed analytical approach including SEM and fsQCA analysis was conducted for data analysis. Subsequently, the discussion of the results is presented in Section 6. Finally, this study concludes with both theoretical and practical implications and limitations in the remaining sections.
2. Research background
2.1 Related work in TikTok
As one of the most popular short-form video apps, TikTok provides a user-focused platform that enables users to record, edit and share videos. It not only offers more entertaining viewing experiences but also supports viewers’ high levels of interaction through keeping in touch with old friends and making new ones (Du et al., 2020). Various social features are embedded in TikTok, creating a diversified environment that enables viewers’ social interaction, which exerts a great extant influence on viewers' perceptions and decision-making process. For example, TikTok focuses on short-form videos which are more vivid and easier to approach, eliciting a stronger influence on users’ attitudes and behavioral intentions (Song et al., 2021). Dynamic videos also provide viewers’ with more intuitive understanding of the TikTokers’ appearance, personality and personal style, attracting them more to engaging in the interaction with TikTokers.
The environment of TikTok is composed of a variety of contextual components (Scherr & Wang, 2021). In general, four unique and critical aspects are involved in the TikTok usage environment: the TikToker, the co-viewer, the video content and the TikTok platform (Lee & Watkins, 2016; Wu & Tian, 2021; Wu & Chen, 2017). These four indispensable aspects of TikTok form an interactive social environment, address the fundamental nature of TikTok usage as a social experience and act as the driving force for influencing users’ thoughts, attitudes and behaviors (Tsai & Pai, 2013).
A TikToker is a person who produces and uploads videos for the platform (Meng & Leung, 2021). Most TikTokers have appealing features such as attractive physical appearances, friendly personalities, fantastic shooting styles and rich knowledge (McCroskey, Hamilton, & Weiner, 2010). Through watching, following and interacting with attractive TikTokers, viewers will acquire, accept and be influenced by the advice, recommendations and other related information conveyed by these appealing TikTokers (Meng & Leung, 2021).
The social interaction process with co-viewers is an indispensable part of TikTok usage. It allows people to connect and interact with other viewers by commenting, sending messages, clicking likes and sharing videos (Wang et al., 2021). The instant responses, attentiveness and mutual focus of attention of co-viewers enhance users’ sense of belonging and create intimate group interaction (Kim, Kim, Oh, & Ryu, 2010). Viewers are likely to gain social support and be influenced by active co-viewer participation in TikTok activities (Xu, Luo, Wu, & Zhao, 2021; Tsai & Pai, 2013).
Next in line is the content of the TikTok video. TikTok is specialized in offering short videos and provides content in various modalities such as pictures, text, audio and videos. The content of TikTok videos is rich, personalized and eye-catching, which focuses on recording peoples’ lives, showing talents and sharing knowledge by incorporating special effects, fancy video clips and diverse pop songs (Montag, Yang, & Elhai, 2021). These attractive, exquisite and well-organized contents aim to capture and attract the attention of users and influence consumers’ watching experience (Scherr & Wang, 2021).
Finally, the innovation of the TikTok platform amplifies the characteristics of traditional social media. It not only gathers a large number of short videos with their comprehensive and valuable content but also reforms individuals’ recreational activities and transforms the traditional way of social interaction. Promoted by the increasing user groups and widespread videos, TikTok has become one of the most influential and reputable platforms that influence users’ perceptions, beliefs and decisions (Meng & Leung, 2021).
The above four critical components of TikTok have offered its users a social environment with rich social cues which enhances diverse social interaction (Scherr & Wang, 2021). Engaging in the interaction in TikTok, viewers’ perceptions, decision-making and behaviors will be influenced by these various components embedded in TikTok. Thus, they can be regarded as the important drivers of users' continuance usage, leading to the significance of exploring how TikTok users are influenced socially in using TikTok continually (Wu & Chen, 2017).
To better understand how prior studies explore TikTok, this study endeavors to scrutinize the existing literature following the guidelines proposed by Okoli and Schabram (2010). We searched the related work in the core database of the “Web of Science” with the topic of “TikTok” and “Douyin”. The searching process identifies only 17 qualified and relevant papers about TikTok in the recent five years (2018–2022). Given the relative novelty of TikTok, little research has been undertaken on this research context to date, and few studies scientifically develop a research model or theoretical framework to explore the emerging context. Of the limited literature, most studies investigated why people use TikTok, drawing upon the U&G theory to explore what features of TikTok were related, gratified and motivated to individuals' behavioral intentions (Meng & Leung, 2021; Scherr & Wang, 2021; Cuesta-Valino et al., 2022). For example, applying the U&G theory, Scherr and Wang (2021) took the gratification approach and explored how distinct gratifications could motivate TikTok usages, such as socially rewarding self-presentation and escapist addiction. Meng and Leung (2021) extended the U&G theory to explore the unique technological gratifications of TikTok in navigability, interactivity and modality. Cuesta-Valino et al. (2022) regarded users as heavily motivated to stick to TikTok by the gratifications inherited in TikTok such as video sharing and creation capabilities. Moreover, employing the lens of the affordance theory, Song et al. (2021) identified live streaming, searching, meta-voicing and recommendation as four technological features afforded by TikTok and exerting influence on user experience. Based on the human–computer interaction (HCI) theories, Wang (2020) also demonstrated the influence of TikTok videos’ humor and camera view on improving user experience and technology-adoption intention.
Notably, the evidence arising from both academic research and industrial practices indicates that TikTok is a niche social streaming app with rich social elements to influence viewers' usage experience (Wang, 2020; Scherr & Wang, 2021; Song et al., 2021). It is natural to assume that social influence may exert significant impacts on viewers’ perception and decision-making processes in the TikTok context. However, to the best of our knowledge, existing studies merely focus on the features of TikTok to explore what and how these features influence users’ experience, decisions and behaviors. Importantly, researchers have indicated that users’ continuance intention is critical in the field of information systems (IS) research for its success and sustainability. Bhattacherjee (2001a) defined IS continuance intention as an individual’s intention to continue using an information system. Similarly, in our research context, we defined the continuance intention as TikTok viewers' intention to continue using TikTok. Though social influence theory has provided an enlightening perspective to explore the users’ continuance use intention of TikTok, little research has adopted it to provide contributions and meaningful extensions to the academic literature on TikTok.
2.2 Social influence theory
Social influence occurs when an individual’s actions, feelings or attitudes are affected by the behaviors or words of others in the social network (Venkatesh & Brown, 2001). Social influence theory explains how social influences are developed and translated into action during people’s interactions with others. As one of the dominant streams, social influences are divided into two types by Deutsch and Gerard (1955). The first type is informational social influence, which refers to the “influence to accept information obtained from another as evidence about reality”. The other type is the normative social influence which refers to “the influence to conform to the expectations of another person to group”. These two types of social influence are conceptually different and used to interpret distinguished situations (Hu et al., 2019). Informational social influence is related to an individual’s desire to make informed decisions based on their acceptance of the information, knowledge, experience and evidence from others (Kaplan & Miller, 1987), while normative social influence occurs when a person adjusts his\her action to conform to the expectations of others to achieve desirable results (Fu et al., 2020).
In social streaming apps, such as TikTok, several external sources may cause the occurrence of informational social influences. For instance, viewers may absorb and digest the information during their interaction with TikToker and understand the information generated in the process as proof of reality and thus develop their understandings based on the obtained information. As an example, individual viewers may choose a travel destination based on recommendations from TikTokers. In addition, video content can be the most prominent influencer. By watching the person explaining the life tips, product experience and professional interpretations of policy and laws presented in the video, viewers tend to accept the information. Informational social influence plays a critical role especially when individuals are faced with decision-making difficulties, perceive high uncertainty of the decision results or are unable to make decisions independently (Lee, Shi, Cheung, Lim, & Sia, 2011).
As refer to normative social influence, it is suggested that normative social influence can perform a far-reaching impact in the context of social media, since social media platform enables large and influential social associations (Kwahk & Kim, 2017). In the broad social circle created by TikTok, TikTokers and co-viewers are all important members of this virtual social community. It is natural that individuals comply with the opinions, values and commonly accepted social behaviors in such a community, and he\she may also perceive whether the group members approve of certain actions. TikTok is an influential platform attracting tens of millions of users. As more and more users are engaged, the TikTok community can convey more mainstream views. Consequently, individual users may be more inclined to comply with others’ opinions and make behavioral decisions based on them. For example, an individual may imitate the behaviors in popular activities, such as the ice bucket challenge or night running.
Social influence theory has been examined in a variety of contexts, such as users’ behavior changes (Chung & Han, 2017), satisfaction (Sedera, Lokuge, Atapattu, & Gretzel, 2017), continuous content contribution (Liu, Min, & Han, 2020) and purchase intention (Fu et al., 2020; Hu et al., 2019; Filieri, McLeay, Tsui, & Lin, 2018). Though researchers have applied social influence theory while investigating social network, social streaming apps has many unique features that are different from general social network sites, such as the elements of live streaming, short videos and simultaneous response in live videos. To the best of our knowledge, the effect of how the different social influences may impact social streaming app usage has not been tested. Importantly, prior studies have indicated that different sources of social influence, and combinations of these sources, will affect users’ choices and decisions (Li, 2013). Thus, it is critical to identify the contextual sources of social influence based on the unique features of the specific context of TikTok and explore how the different sources and their combination may influence different types of social influence and the following behavioral intentions.
3. Research model and hypothesis development
This study develops a research model to explore how TikTok usage is impacted by two distinct types of social influences which are determined by four sources (i.e. TikToker attractiveness, co-viewer participation, platform reputation and content appeal). The research model is presented in Figure 1.
The concept of attractiveness was originally proposed by interpersonal communication scholars to measure the “tendency of one person to evaluate another person or his symbol in a positive (or negative) way”. In this study, we define TikToker attractiveness as the personality, appearance and talent the viewers perceive regarding the TikToker while watching TikTok videos (Xu, Wu, & Li, 2020). Viewers may find a TikToker attractive when he\she is physically attractive (McCroskey et al., 2010) and has task-related abilities (e.g. the ability to make impressive videos) or social abilities (e.g. the ability to conduct positive interactions with viewers) (Fang, 2014; McCroskey et al., 2010).
Attractiveness is adopted frequently to explain why people initiate, develop and maintain public relations with other entities and is believed, by many researchers, to predict people's attitudes and behaviors (Dwyer, Schurr, & Oh, 1987; Fang, 2014). When viewers perceive a TikToker as attractive, they may watch and follow more videos made by the TikToker and engage in more intensive interaction with the TikToker. Moreover, when the viewers find the TikToker’s video useful and helpful for them, such as life tips and professional suggestions (e.g. interpretation of laws and photographic skills), they are more inclined to believe that the information and viewpoints offered by the TikToker are trustworthy (McCroskey et al., 2010). As viewers develop positive affection and emotion towards the TikToker, they may obey the TikToker’s expectations naturally and are more inclined to accept the TikToker’s suggestions subconsciously (Lee & Watkins, 2016; Sokolova & Kefi, 2020). In this way, the attractive TikToker are more likely to exert informational and normative social influence on their viewers, since they can develop social connections with their viewers more easily and convince them of their reliability. The following hypotheses are developed:
Perceived TikToker attractiveness is associated with informational social influence positively.
Perceived TikToker attractiveness is associated with normative social influence positively.
TikTok is a social streaming app; thus, the co-viewers are the cardinal member of this virtual social community (Xu & Tayyab, 2021). Member participation often presents the extent to which the members are engaged in the interaction in the social community (Burgoon et al., 1999) and suggests that the community members have devoted time, effort and other resources to initiating, developing and maintaining group relationships (Tsai & Pai, 2013). For example, when the viewers leave comments or ask questions, they may receive quick responses from other co-viewers. The participation of other co-viewers also creates an enthusiastic atmosphere and strong social presence. It has been well acknowledged that others in a social community can exert significant influences on the individual members (Coker & Burgoon, 1987; Tsai & Pai, 2013; Xu et al., 2021) because individuals believe in the “wisdom of crowds” and that community members provide valuable information highly related to them. What is more, due to the social interactions, trustworthy interpersonal connections and common value perceptions in a TikTok community, such as the follower community of a TikToker (e.g. KOL of fashion) or specific channel (e.g. China geography), individuals are also more inclined to comply to certain values collectively held in a community (Huang & Chen, 2006), such as the concept of environment and animal protection and the understanding of fashion and trend. Hence, the co-viewers participation is a critical source generating informational and normative social influence. The following hypotheses are developed:
Co-viewer participation is associated with informational social influence positively.
Co-viewer participation is associated with normative social influence positively.
Reputation refers to the overall evaluation of a company or a brand as time goes on (Foroudi, Melewar, & Gupta, 2014). If the reputation of a company is perceived as successful and famous by customers, their recognition of the said company will also be greatly enhanced (Foroudi, 2017). Thus, the platform's reputation plays a vital role in determining users’ attitudes toward the platform and their potential behaviors (Wu & Chen, 2017). In this study, the platform's reputation reflects the viewers' evaluation of TikTok. A good platform reputation not only implies the high quality and diversity of videos but also suggests the popularity and large user base. Therefore, viewers are more inclined to trust the platform and consider the information distributed on TikTok reliable. In addition, since a large user base of a reputable platform may also represent the mainstream value and viewpoints, the said platform may further make the audience accept expectations or hints to cater to the social community. The following hypotheses are developed:
Platform reputation is associated with informational social influence positively.
Platform reputation is associated with normative social influence positively.
The attractiveness of the content represents the core competitiveness of the platform relying on user-generated content (Lai & Liu, 2020). The content appeal in this study refers to the degree of attractiveness the viewers perceive towards the TikTok videos. When the viewers perceive the video content as attractive, it is natural for them to spend more time watching the videos, thereby absorbing and accepting the information presented in the video more easily. Moreover, with the advantage of appealing content, TikTok can attract a large number of users and thus reflect the mainstream, core values and viewpoints of society. To conclude, the content appeal can not only attract the users to engage in TikTok wherein they become influenced by the information provided but also increase the probability of the audience accepting the values, suggestions and expectations conveyed in the videos. The following hypotheses are developed:
Content appeal is associated with informational social influence positively.
Content appeal is associated with normative social influence positively.
Continuous intention refers to viewers’ intention to continuously use TikTok after they have adopted TikTok for the first time (Bhattacherjee, 2001b). Regardless of their previous or current attitude or perceptions towards a specific behavior, the individuals affected by normative social influence are more likely to feel the social pressure towards adopting or not adopting this behavior (Henningsen & Henningsen, 2003). In other words, if viewers experience more normative social influence towards their continuous use of TikTok, they are more likely to submit to the forces and continuously use TikTok. Such submission may bring benefits to individual users, such as having more chances to involve in the interaction with other TikTok members (Li, 2013).
In addition, informational social influence often exerts significant effects when the individual desire to make a better decision (Henningsen & Henningsen, 2003). The more information an individual obtains, the greater confidence he or she will acquire in future performance and behaviors (Chou, Wang, & Tang, 2015; Lee, Cheung, Sia, & Lim, 2006). Hence, the viewers’ tendency to actively seek, acquire and accept the information provided by TikTok are likely to be enhanced when they are under strong informational social influence. Further, they will estimate the value and benefits of obtaining the information and eventually develop a strong intention to use TikTok continuously. The following hypotheses are developed:
Informational social influence positively is associated with continuous intention.
Normative social influence positively is associated with continuous intention.
4. Methodology
4.1 Measurement development
The scales measuring the constructs were developed based on existing literature. TikToker attractiveness (TA) was mainly adopted from the study of Xu et al. (2021) and Ha and Lam (2017). The measurements of co-viewer participation (CP) and platform reputation (PR) were extracted from Xu et al. (2021) and Tsai and Pai (2013), along with Zhou (2020). Content appeal (CA) was measured using the scale from Lai and Liu (2020). In addition, the measurements of informative social influence (ISI) and normative social influence (NSI) were adopted from Henningsen and Henningsen (2003) and Li (2013). The scales of continuous intention (CI) were obtained from the works of Chen (2014). Based on our research context, we modified the items slightly to better suit our research scope. This study incorporated the variables of gender, age and educational background as the control variables. Five-point Likert scale was adopted, ranging from (1) strongly disagree to (5) strongly agree. All of the constructs used in this research are reflective constructs. The measurement items and references for each construct are presented in Table A1.
To ensure the accuracy of the content, guarantee linguistic equivalence and improve the readability of the questionnaire, we conducted expert interviews and a small-scale pilot test. Based on their feedback and the advice from the experts, we modified the wording, expression and understandability of the questions to ensure the quality of our questionnaire. In addition, we conducted the pretest by inviting 68 TikTok users to answer the questions. Several modifications of wording and phrases were conducted to ensure the clarity and suitability of the questions. At last, the formal questionnaire consists of three main parts, including a motivation letter, a confidentiality statement regarding the respondents’ information and the items measuring the constructs and background information.
4.2 Data collection
We applied a leading web-survey website (https://www.sojump.com/) to distribute questionnaires randomly and collect the empirical data from February 10th to 20th, 2022. To ensure the quality and validity of the answers, we set a pre-screening question at the beginning of the questionnaire to ensure the respondents are TikTok users. Only the respondents who provided affirmative answers to the question “Did you have used TikTok?” can be permitted to answer the following questionnaire. Respondents who failed to pass the attention check questions were excluded. Through the detailed and careful screening process, we finally collected 537 valid responses for the formal empirical data analysis.
The demographic information of the respondents is shown in Table 1. As for the distribution of gender, female accounts for 79.33% and 20.67% are male (Du et al., 2020). Over 95% of the respondents are between 19 and 40 years old. Notably, TikTok has introduced age restrictions to protect minors using the platform, which limits their usage. Our sample with only three minors also reflects this policy. In addition, most of the respondents have a higher education degree. In terms of usage frequency, about 40.60% of the respondents are daily users of TikTok and use it many times a day.
5. Data analysis
5.1 A mixed analytical approach
Individuals’ decision-making process is inherently complex since their intention is driven by integrating different factors into a whole. The various external factors embedded in the environment and distinct social influences will interact and configure dynamically to influence behavioral decisions (Maier et al., 2021).
SEM analysis focuses on additive, linear and nonfinality effects. It regards the independent variables as competing to explain the variation of the dependent variables and estimates the net effect of predictors on dependent variables (Woodside, 2013). This assumption overly simplifies the underlying correlation and integration of antecedents driving the individual’s decision-making mechanism (Pappas & Woodside, 2021). To overcome the limitation of SEM analysis and address the complexity of individuals’ decision-making process, several pioneers endeavored to further employ fsQCA analysis as a complementary analytical technique to SEM analysis (Afonso, Silva, Goncalves, & Duarte, 2018; Kaya, Abubakar, Behravesh, Yildiz, & Mert, 2020).
As a comparative case-oriented analytical approach, fsQCA focuses on the set-theoretic associations among variables and highlights the combinatorial effects rather than net effects (Ragin, Strand, & Rubinson, 2008). Importantly, fsQCA addresses the complexity of the real-life consumption environment based on the asymmetric assumptions between independent and dependent variables. Thus, it captures multiple solutions that lead to the same outcome that cannot be identified through the main effects analysis (Pappas & Woodside, 2021). The combination of SEM and fsQCA draws a scientific, robust and complemented conclusion and better unveils the underlying influence mechanisms of TikTok on viewers’ continuance intention.
Combining SEM and fsQCA analysis, this study employs a two-phase process in data analysis with a mixed analytical approach. Employing Smart PLS 3.0, this study first validates the measurement model and examines the hypotheses in the structural model using SEM. Second, we analyze the data using the fsQCA 3.0 to generate insights into more alternative configurations of continuance intention. SEM and fsQCA results enrich the understanding of how the key contextual elements of TikTok may engender social influence in TikTok and the antecedents of users’ continuance intention.
5.2 PLS-SEM analysis
5.2.1 Measurement model analysis
First, we examined the quality of the proposed measurement model by testing both the reliability and validity of the data. We ensure reliability by examining the value of Cronbach's alpha and composite reliability (CR). As shown in Table 2, all of the values exceed 0.7, which indicated a satisfactory reliability level.
In addition, we examine both convergent validity and discriminant validity to ensure validity. The factor loadings of each construct should exceed the thresholds of 0.7 (Rajalahti & Kvalheim, 2011), and the values of AVE should be above 0.50 (Chin, 1998). The results of our study show satisfactory convergent validity, which is presented in Table 2
The discriminant validity of this study is texted by both the Fornell–Larcker criterion (Fornell & Larcker, 1981). The value of the Fornell–Larcker criterion is presented in Table 3, which shows all the inter-construct correlations are lower than the square root of AVE values. The results demonstrate satisfactory discriminant validity in our study.
5.2.2 Common method bias
Considering that this study has collected the empirical data with a cross-sectional, self-reported and single-source approach (Spector, 2006), common method bias (CMB) is likely to be a concern that influences the validity of data. We applied the Harmon one-factor test to examine CMB in our study. Employing SPSS, we conducted the principal components factor analysis of all the constructs. The result showed that the first factor explained 28.98% of the total variance, which is below the acceptable threshold of 50% and suggests that CMB is not a critical issue (Mackenzie & Podsakoff, 2012).
5.2.3 Structural model analysis
We tested the explanatory power of our structural model by examining the R2 values and structural paths of constructs. The analytical results of the structural model are shown in Figure 2. As for the R2 values, informative social influence, normative social influence and continuous intention are explained by variances of 31.21, 33.73 and 46.25%, respectively, which shows a substantial proportion of the variance. The results of R2 indicate that the formation of the dependent variables is well explained and verify the explanatory power of our proposed research model. In addition, the analytical results indicate the control variables including gender, age and educational background did not statistically influence the independent variables significantly.
In addition, as shown in the results of path analysis (see Table 4), except for H1 (TikToker attractiveness → Informative social influence) and H5 (Platform reputation → Informative social influence), other hypothesized paths were significant. It indicates that perceived co-viewer participation and content appeal are both positively associated with informative social influence, while all of the four influence sources, namely, TikToker attractiveness, perceived co-viewer participation, content appeal and platform reputation, exert positive influences on normative social influence. Moreover, both types of social influence (informative social influence and normative social influence) are found to positively affect users’ continuous intention of TikTok.
5.2.4 Post-hoc analysis: multiple group analysis (MGA)
To explore differences in perception and behavior among users with different usage experiences, we tried to provide empirical evidence regarding the moderating role of user experience in the TikTok influencing mechanism.
We divided all respondents into two groups, namely, experienced users and users with little experience. Multiple group analysis (MGA) was conducted by Smart PLS 3.0 to investigate and compare the differences between all path relationships in the two groups. The results of MGA, as presented in Table 5, unveil that there may exist a significant difference between the two groups in the impact of informative social influence and normative social influence on users’ continuance intention.
The path coefficient between informative social influence and continuance intention is significantly larger in the experienced users than in the users with little experience (β-experienced user = 0.464, β-little experience = 0.269 and p-value = 0.012). As for the path coefficient between normative social influence and continuance intention, the user group with little experience is significantly larger than the experienced user group (β-experienced user = 0.269, β-little experience = 0.558 and p-value = 0.000). The empirical results revealed that new users are more inclined to comply with others’ opinions and make continuance decisions based on them, while experienced users are more focused on the informative role of TikTok and continually use TikTok due to the rich information obtained from TikTok.
5.3 Fuzzy-set qualitative comparative analysis (FsQCA)
5.3.1 Analysis process
To run the fsQCA analysis, data calibration is required to transform all variables into fuzzy sets, with a value range of 0 to 1 (Ragin et al., 2008). Based on the percentile of each construct, three qualitative thresholds of 95, 50 and 5th percentiles were applied to calibrate all the values to full membership, intermediate membership and full non-membership (Xie & Tsai, 2021; Ragin et al., 2008). In addition, following the principles of Fiss (2011), we endeavored to avoid the cases exact on 0.5 by adding 0.001 to the values that are exact 0.50 after calibration, which is useful to overcome the difficulties of analyzing the conditions in the cases exactly on 0.5.
After calibrating the data into fuzzy sets successfully, fsQCA analysis requires performing the necessity analysis. This study conducted the necessary conditions analysis for the presence and negation of continuance intention with a consistency benchmark of 0.90 (Ragin, 2008). As indicated in Table 6, all of the consistency values of each condition are below 0.90, which indicates that none of the variables can be considered the necessary condition to influence users’ continuance intention when considered solely.
Then, this study analyzed sufficient conditions based on the truth table algorithm. Based on the instruction of Fiss (2011), the frequency threshold was set as 3 and the consistency threshold as 0.8. In addition, the proportional reduction in inconsistency (PRI) was applied with the score threshold of 0.7 to ensure the configuration does not simultaneously occur both in the cases of the presence of the outcome and the negation of the outcome (Pappas & Woodside, 2021). Finally, seven various configurations are generated. Following the prior notions, we presented the intermediate solution which is an optimal trade-off between the presentation of the complex and parsimonious solution (Fiss, 2011). Applying the conventional notations, we applied ● to indicate a condition is present and ⊗ to indicate the absence of a condition.
5.3.2 Analysis results
Table 7 illustrates seven configurations sufficient for continuance intention. Based on the consistency with the threshold of 0.75, all the solutions presented in Table 6 are consistent, which indicates theses seven solutions can explain TikTok viewers’ continuance intention at a high level (Ragin, 2008). In addition, the results show an overall solution coverage of 0.759, which indicates that the seven configurations cover a substantial proportion of the paths that lead to continuous use intention. In addition, the overall solution consistency of 1 confirmed the high explanatory power of the results.
Solution 1–4 have similar features, which all include the factors in external sources of social influence and social influence. These four paths indicate that only external sources or social influence are insufficient to trigger viewers’ continuance intention. These two categories of factors should coexist. It is worth noting that co-viewer participation is a necessary factor in these four configurations, which unveils the significant role of co-viewer on viewers’ experience and its effects on their attitudes and continuance usage intention.
Except for the role of co-viewer participation in all solutions, the first configuration revealed that continuance intention results from the content appeal and informative social influence, which highlights the role of information on viewers’ continuance intention in TikTok. The second configuration focuses on the influence of TikToker, the co-viewer and the normative influence, which unveils the influence of the social community in social media on viewers’ continuance intention. The third configuration includes all of the antecedents except informative social influence. This configuration weakens the role of informative social influence when other factors exist. The fourth configuration advised that the influence of two types of social influence and co-viewer are sufficient to cause users’ continuance intention of TikTok.
Different from solutions 1–4, solutions 5–7 unveil the underlying configurations when two types of social influence are absent. Solution 5 indicates that the mere influence of co-viewer participation is sufficient to cause continuance intention. Solution 6 revealed that the combined influence of the TikToker, the co-viewer and the platform can lead to continuance intention while solution 7 weakened the role of the TikToker and indicated only the role of co-viewer and platform are sufficient. However, compared to solution 5, the raw coverage of solution 6 and solution 7 is higher than solution 5, which indicated that when the influence of other external sources exists except co-viewer, the influence and explanatory power of the solutions will become stronger.
6. Discussion and implications
6.1 Discussion
Employing a mixed analytical approach that combines SEM and fsQCA, we empirically examined the hypothesized influencing mechanism proposed in the research model and investigated how all antecedents may form a variety of configurations to interpret viewers’ continuous intention in the context of TikTok.
Employing SEM analysis, we explored the impact of unique external sources in TikTok on two types of social influences including informational social influence and normative social influence. This study examined eight hypotheses to unveil the relationships between external influencing sources and different social influences and their associations with continuous intention in TikTok. Six of the hypotheses are supported by empirical data.
With regard to the four external sources of informational social influences, two of the four constructs are positively associated with informational social influence. Co-viewer participation and content appeal both exert statistically significant effects on informative social influence (H2a, H4a). The results of the supported hypotheses are in accordance with the conclusion in prior literature (Cuesta-Valino et al., 2022; Wu & Chen, 2017). It can be implied that when other co-viewers devote more energy and effort to contribute to the TikTok community and interaction, individual viewers can obtain more information easily, thus enhancing the information influences. Importantly, the content appeal is the strongest antecedent of informational social influences in the context of TikTok. The result is in line with our observation of the phenomenon. Since the quality of the content and the perceived attractiveness of the videos play the most critical role in the social streaming platform (McCroskey et al., 2010; Xu & Tayyab, 2021), if the content is not attractive, users may not watch the video at all, let alone receiving the informational social influence conveyed in the video.
In addition, the effects of TikToker's attractiveness and platform reputation on informational social influence are not supported by the empirical data (H1a and H3a). Informational social influence represents an individual’s cognitive response towards a phenomenon or reality, thus helping confirm and strengthen their thinking (Li, 2013). The attractiveness of TikToker often shows the attributes of the TikToker, such as his\her physical, task-related or social attractiveness (Fang, 2014). Information social influence is related to individuals’ cognition. Influenced by the specific context, viewers will generate different perceptions and cognitions in different situations, such as watching different videos or comments. However, the TikToker attractiveness and platform reputation only represent the general image of the TikToker and Tiktok platform, respectively. The general image reflects the overall evaluation of the viewers, which is unlikely to change in different specific contexts. Thus, the general image of TikToker and the Tiktok platform exert limited influence statistically on viewers’ perceptions of informativeness. Compared with the TikToker attractiveness and platform reputation, video content and co-viewer are both context-related and represent the main way users obtain information, which has a great impact on the audience's cognition and perceived information when they use TikTok.
Four external sources, namely, TikToker attractiveness, co-viewer participation, content appeal and platform reputation, all exert significant impacts on normative social influence. The significance of these external sources has been supported by prior studies (Wang et al., 2021). Content appeal (β = 0.27***) and co-viewer participation (β = 0.26***) exert the strongest effects. The impact of TikToker attractiveness (β = 0.20***) is also statistically significant, while platform reputation only exerts a marginally significant effect (β = 0.08*). The results suggest that the appealing content is most likely to exert normative social influence. The result is in line with the observation of social streaming platforms. In addition, under normative influence, people are more inclined to maintain the harmonious relations and obtain a positive evaluation from the social community (Kaplan & Miller, 1987). Hence, it is a natural result suggesting that other co-viewers as important TikTok community members can exert the most significant effect on normative social influence.
As for the influence of continuous intention, rich results were provided by both the SEM and fsQCA. On the one hand, SEM analysis unveils that both informational (β = 0.35***) and normative social influence (β = 0.41***) exert a very strong effect on the continuous intention of TikTok (H5 and H6). Prior studies have indicated that social influences generated from interaction on social media can significantly enhance users’ positive attitudes toward the platform and change their previous negative impressions (Wu & Chen, 2017). The persuasive social influences message can increase the viewers’ interest in engaging in a variety of behaviors on the social media platform (Chou et al., 2015; Lee et al., 2006). As for the relative importance, the results suggest that normative social influence can have a stronger impact on continuous intention. This might be attributed to the nature of social media. The reason why people continue to use social media is not limited to obtaining useful information but also includes maintaining social relationships developed in the virtual community (Tsai & Pai, 2013).
On the other hand, based on the combination of all of the antecedents of continuance intention, fsQCA results unveil the seven alternative solutions that are equally capable of explaining TikTok users’ continuance intention. Several findings can be drawn from the results. First, the results indicate that there is no single factor that is itself necessary to explain continuance intention, which also confirms the theoretical model that no single external source itself is a necessary or sufficient factor to achieve the outcome. It reveals that users’ continuance intention of TikTok results from a mix of several factors. Second, as indicated by the fsQCA results, co-viewer participation assumes particular importance as it is present in all configurations. While it is interesting to notice that the effects of content appeal on both informative and normative social influence are stronger than co-viewer participation in the SEM net effect analysis, exclusively positive or negative relations between single variables concluded by SEM results provide an incomplete picture of the phenomenon (Woodside, 2013). The results of fsQCA further complement SEM results. Third, solutions 5–7 of the fsQCA results indicated that social influence is not necessary in leading to users’ continuance intention. However, although solutions 5–7 indicate only external sources of social influence are sufficient to lead to continuance intention, compared to solutions 1–4, the raw coverage and the unique coverage value of solutions 1–4 are higher than solutions 5–7. The differences indicate the solutions with the social influence factors are stronger than those without such factors. The results not only validated the research model that external factors further affect the users’ continuance intention through their social influence but also highlighted the role of social influence in users’ decision-making process.
6.2 Theoretical implications
This study endeavors to offer several implications to the relevant literature. This research is one of the early attempts to extend social influence theory in the domain of TikTok study. Although researchers have called for investigating viewers’ decision-making process of TikTok users (Cuesta-Valino et al., 2022), only a few studies have addressed this topic to date (Meng & Leung, 2021). This study confirms the explanatory power of social influence theory in interpreting TikTok viewers’ continuous intention and sheds light on the two types of social influences, including informational and normative social influences with empirical evidence. Hence, on the one hand, this study adds new insight into the literature on viewers’ behavior on social streaming platforms such as TikTok and on the other hand, extends the scope of social influence theory in a novel context and thus contributing to the literature of social influence theory.
This study endeavors to explore the different sources of social influences in a novel context, and examines how the combinations of these sources will eventually affect viewers’ behavioral decisions in TikTok. This study comprehensively explored four different external sources of social influence to represent the unique elements of this specific context. The research results unveil that the different external sources can generate distinct effects on two different social influences. Specifically speaking, only two sources related to content and co-viewer may lead to informational social influence, while all four sources may predict normative social influences in TikTok. Hence, this study offers useful knowledge to the literature focusing on the antecedents of social influences in an emerging context.
Finally, the application of a mixed analytical method offered a holistic and comprehensive view to understand viewers’ decision-making mechanisms in more detail. The SEM allowed examination of the correlations proposed in the research model, which revealed a possible underlying influencing mechanism on viewers’ continuance usage intentions in TikTok. Configuring the antecedents identified based on the social influence, the results of fsQCA offered seven alternative solutions for interpreting viewers’ continuance intention toward TikTok, confirming the critical role of social influence in viewers’ continuance intention and validating the influence mechanism of TikTok.
This approach revealed new insights (such as mediating effects) and offered in-depth interpretations of the SEM empirical results toward the research model. The fsQCA also verified and compensated the SEM results in the previous stages with a comprehensive list of solutions for determining coping behaviors. Moreover, this analytical approach indicated the complexity of determinants and solutions for predicting viewers' continuance intention in TikTok.
6.3 Practical implications
This study offers several practical implications to practitioners, managers and platform developers. First, the research results show that TikToker attractiveness has a significant impact on the continuous intention of TikTok users through normative social influence. Thus, the TikTok platform should arrange more resources to support the development of the TikTokers. The platform can officially launch a series of comprehensive tutorials to help TikTokers to improve their attractiveness, such as task-related skills (shooting skills, video editing and material selection), social interaction skills, etc. The platform can also launch a survey to better understand their viewers’ preferences towards TikTokers, thus training them to better satisfy their viewers.
The results show that the perceived co-viewer participation has a significant impact on the continuous intention of TikTok through both informative and normative social influence. Therefore, the platform should enhance social functions, such as the functions of publishing updates, viewing friends' updates, posting comments and interacting with friends. In addition, the platform should increase the opportunities for the viewers to interact with other co-viewers sharing a similar interest with them, such as adopting effective recommendation algorithms, artificial intelligence and other advanced technologies to design more effective mechanisms. In addition, the platform should also dedicate to developing a high-quality virtual community to attract more users and motivate meaningful communications among co-viewers.
At last, the content appeal has a significant impact on the continuous intention of TikTok through the informative and normative social influence. Thus, the platform should design an effective mechanism to encourage the users to create more high-quality and attractive videos, such as reward points, grades and fans' rewards. In addition, the platform may optimize the mechanism to collect high-quality videos while recommending them to their viewers, thus enabling the users to obtain useful information and beneficial viewpoints by watching these videos.
6.4 Limitations and future research
This study has to acknowledge the limitations, which offer opportunities for future research. First, this study employed a cross-sectional survey to collect empirical data to investigate behavioral intention at the one-time point. Thus, future studies may employ longitudinal research methods and data capture the actual behavior to offer more reliable results. Second, since the sample data were collected in mainland China only, researchers should be cautious while applying the results to other cultures or regions. Third, though this study endeavors to offer a comprehensive result on the external sources of social influences, it does not offer a complete list of the possible factors. Future studies may explore other factors from different perspectives, such as the internal factors related to individual features. In addition, since informational social influence is distinct from normative social influence, it is reasonable to consider the different influencing factors of these two types of social influence. Thus, the future study may further explore the different antecedents of distinct social influences to provide a more underlying understanding of the users’ decision-making mechanism.
Figures
Demographics of respondents
| Items | Frequency | Percentage (%) | |
|---|---|---|---|
| Gender | Male | 111 | 20.67 |
| Female | 426 | 79.33 | |
| Age | Under 18 | 3 | 0.56 |
| 19–25 | 458 | 85.29 | |
| 26–30 | 44 | 8.19 | |
| 31–40 | 24 | 4.47 | |
| Above 40 | 8 | 1.49 | |
| Education level | Junior high school and below | 9 | 1.68 |
| Senior High school | 15 | 2.79 | |
| Undergraduate or College | 401 | 74.67 | |
| Graduate and above | 112 | 20.86 | |
| Frequency of using TikTok | Several times a day | 218 | 40.60 |
| Once a day | 76 | 14.15 | |
| Several times a week | 90 | 16.76 | |
| Once a week | 29 | 5.04 | |
| Less than once a week | 124 | 23.09 |
Reliability and convergent validity analysis
| Construct | Items | Factor loading | Cronbach's α | CR | AVE |
|---|---|---|---|---|---|
| TA | TA1 | 0.783 | 0.703 | 0.834 | 0.626 |
| TA2 | 0.761 | ||||
| TA3 | 0.828 | ||||
| CP | CP1 | 0.819 | 0.817 | 0.875 | 0.636 |
| CP2 | 0.782 | ||||
| CP3 | 0.804 | ||||
| CP4 | 0.786 | ||||
| PR | PR1 | 0.831 | 0.924 | 0.942 | 0.766 |
| PR2 | 0.888 | ||||
| PR3 | 0.861 | ||||
| PR4 | 0.900 | ||||
| PR5 | 0.892 | ||||
| CA | CA1 | 0.810 | 0.845 | 0.896 | 0.682 |
| CA2 | 0.832 | ||||
| CA3 | 0.819 | ||||
| CA4 | 0.842 | ||||
| ISI | ISI1 | 0.828 | 0.849 | 0.898 | 0.689 |
| ISI2 | 0.837 | ||||
| ISI3 | 0.872 | ||||
| ISI4 | 0.781 | ||||
| NSI | NSI1 | 0.789 | 0.850 | 0.893 | 0.624 |
| NSI2 | 0.783 | ||||
| NSI3 | 0.800 | ||||
| NSI4 | 0.808 | ||||
| NSI5 | 0.770 | ||||
| CI | CI1 | 0.907 | 0.935 | 0.950 | 0.793 |
| CI2 | 0.904 | ||||
| CI3 | 0.875 | ||||
| CI4 | 0.894 | ||||
| CI5 | 0.873 |
Discriminant validity analysis
| Construct | TA | CP | PR | CA | ISI | NSI | CI |
|---|---|---|---|---|---|---|---|
| TA | 0.626 | ||||||
| CP | 0.129 | 0.637 | |||||
| PR | 0.118 | 0.069 | 0.766 | ||||
| CA | 0.110 | 0.088 | 0.222 | 0.682 | |||
| ISI NSI | 0.077 | 0.124 | 0.112 | 0.267 | 0.689 | ||
| 0.168 | 0.184 | 0.119 | 0.204 | 0.327 | 0.624 | ||
| CI | 0.095 | 0.116 | 0.079 | 0.271 | 0.349 | 0.380 | 0.793 |
Note(s): Diagonal elements represent the square root of AVE
Hypotheses testing results
| Hypothesis | Coefficients | T-values | p values | Results | |
|---|---|---|---|---|---|
| H1a | TikToker attractiveness → Informative social influence | 0.046 | 0.988 | n.s. | Not support |
| H1b | TikToker attractiveness → Normative social influence | 0.201 | 4.536 | p < 0.001 | Supported |
| H2a | Co-viewer participation → Informative social influence | 0.194 | 4.541 | p < 0.001 | Supported |
| H2b | Co-viewer participation → Normative social influence | 0.255 | 6.347 | p < 0.001 | Supported |
| H3a | Platform reputation → Informative social influence | 0.075 | 1.506 | n.s. | Not support |
| H3b | Platform reputation → Normative social influence | 0.080 | 1.863 | p < 0.05 | Supported |
| H4a | Content appeal → Informative social influence | 0.409 | 8.559 | p < 0.001 | Supported |
| H4b | Content appeal → Normative social influence | 0.271 | 6.449 | p < 0.001 | Supported |
| H5 | Informative social influence → Continuous intention | 0.354 | 7.502 | p < 0.001 | Supported |
| H6 | Normative social influence → Continuous intention | 0.414 | 9.065 | p < 0.001 | Supported |
| Constructs | R2 (%) |
|---|---|
| Informative social influence | 31.21 |
| Normative social influence | 33.73 |
| Continuous intention | 46.25 |
Note(s): ***p < 0.001, **p < 0.01 and *p < 0.05. n.s.: not significant
MGA analysis
| Path coefficients | Difference of path coefficients | ||
|---|---|---|---|
| Experienced users | Users with little experience | ||
| Content appeal → Informative social influence | 0.439*** | 0.363*** | 0.076 |
| Content appeal → Normative social influence | 0.276*** | 0.264*** | 0.012 |
| Co-viewer participation → Informative social influence | 0.204*** | 0.193*** | 0.011 |
| Co-viewer participation → Normative social influence | 0.255*** | 0.262*** | −0.007 |
| Informative social influence → Continuous intention | 0.464*** | 0.247*** | 0.217*** |
| Normative social influence → Continuous intention | 0.269*** | 0.558*** | −0.29*** |
| Platform reputation → Informative social influence | −0.022 | 0.152 | −0.173 |
| Platform reputation → Normative social influence | 0.057 | 0.100 | −0.043 |
| TikToker attractiveness → Informative social influence | 0.016 | 0.086 | −0.070 |
| TikToker attractiveness → Normative social influence | 0.149** | 0.252*** | −0.103 |
Note(s): *** is significant at p < 0.001
Necessary condition analysis
| Configurational elements | Continuance intention | ∼ Continuance intention | ||
|---|---|---|---|---|
| Consistency | Coverage | Consistency | Coverage | |
| TikToker attractiveness | 0.774 | 0.760 | −0.508 | 3482382.25 |
| ∼ TikToker attractiveness | 0.534 | 0.655 | 0.726 | 0.331 |
| Co-viewer participation | 0.893 | 1.000 | −0.670 | −729909.06 |
| ∼ Co-viewer participation | 0.492 | 0.522 | 0.859 | 0.391 |
| Platform reputation | 0.762 | 0.712 | −0.464 | −3389677.00 |
| ∼ Platform reputation | 0.510 | 0.667 | 0.725 | 0.329 |
| Content appeal | 0.708 | 0.789 | −0.478 | 837527.37 |
| ∼ Content appeal | 0.637 | 0.680 | 0.702 | 0.319 |
| Informative social influence | 0.748 | 0.776 | −0.452 | −835116.93 |
| ∼ Informative social influence | 0.585 | 0.671 | 0.756 | 0.344 |
| Normative social influence | 0.776 | 0.762 | −0.483 | −4417389.50 |
| ∼ Normative social influence | 0.524 | 0.641 | 0.746 | 0.339 |
Configurations that lead to continuance intention
| Configuration | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| TikToker attractiveness | ● | ● | ⊗ | ⊗ | ● | ||
| Co-viewer participation | ● | ● | ● | ● | ● | ● | ● |
| Platform reputation | ⊗ | ● | ⊗ | ● | ● | ||
| Content appeal | ● | ● | ⊗ | ||||
| Informative social influence | ● | ● | ⊗ | ⊗ | ⊗ | ||
| Normative social influence | ● | ● | ● | ⊗ | ⊗ | ⊗ | |
| Raw coverage | 0.58 | 0.35 | 0.52 | 0.25 | 0.37 | 0.33 | 0.33 |
| Unique coverage | 0.03 | 0.04 | 0.018 | 0.02 | 0.02 | 0.003 | 0.004 |
| Consistency | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Solution coverage | 0.759 | ||||||
| Solution consistency | 1 | ||||||
Measurement items
| Construct | Measurement item | References |
|---|---|---|
| TikToker attractiveness (TA) | I think the TikToker has an attractive style I think the personality of the TikToker is attractive to me I think the TikToker has a very attractive appearance | Xu et al. (2021) Ha and Lam (2017) |
| Co-viewer participation (CP) | Other co-viewers of TikTok seem to find the conversation stimulating The community of TikTok showed enthusiasm while talking The community of TikTok is intensively involved in video watching I was very impressed by the community’s high responsiveness in replying to comments | Xu et al. (2021) Tsai and Pai (2013) |
| Platform reputation (PR) | TikTok platform is famous TikTok platform is well-known TikTok has been widely used by many people TikTok has been recognized widely compared to other short-video platforms TikTok has a reputation for providing interesting short videos | Zhou (2020) |
| Content appeal (CA) | I think the video content on TikTok is attractive I think the video content on TikTok is valuable I think the video content on TikTok is wonderful I think the video content on TikTok is impressive | Lai and Liu (2020) Wang and Li (2014) |
| Informational social influence (ISI) | I think there has some information to sway my decisions on TikTok I think there has some information to change my mind on TikTok I may ask the community for suggestions to solve my problems I often consult others to help me make the best decisions | Henningsen and Henningsen (2003) |
| Normative social influence (NSI) | People’s opinions in TikTok may change my behaviors or thinking My behavior may be guided by a desire of the TikTok community It is important what the TikTok community think about how I think What the TikTok community considers important matters are also important to me I watch some videos to fulfill the expectations of the TikTok community | Li (2013) |
| Continuous intention (CI) | Using TikTok is something I would like to do I intend to continue using TikTok If I could, I would like to continue using TikTok as much as possible I will maintain my TikTok use frequency in the future I tend to use TikTok rather than other short video apps in the future | Chen (2014) |
References
Afonso, C., Silva, G.M., Goncalves, H.M., & Duarte, M. (2018). The role of motivations and involvement in wine tourists' intention to return: SEM and fsQCA findings. Journal of Business Research, 89, 313–321.
Amblee, N., & Bui, T. (2011). Harnessing the influence of social proof in online shopping: The effect of electronic word of mouth on sales of digital microproducts. International Journal of Electronic Commerce, 16, 91–113.
Bhattacherjee, A. (2001a). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370.
Bhattacherjee, A. (2001b). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32, 201–214.
Blind (2022). TikTok user statistics, (2022). Available from: www.backlinko.com
Burgoon, J.K., Bonito, J.A., Bengtsson, B., Ramirez, A., Dunbar, N.E., & Miczo, N. (1999). Testing the interactivity model: Communication processes, partner assessments, and the quality of collaborative work. Journal of Management Information Systems, 16, 33–56.
Chen, Y.F. (2014). See you on Facebook: Exploring influences on Facebook continuous usage. Behaviour & Information Technology, 33, 1208–1218.
Chin, W.W. (1998). Issues and opinion on structural equation modeling. MIS Quarterly, 22, VII–XVI.
Chou, C.H., Wang, Y.S., & Tang, T.I. (2015). Exploring the determinants of knowledge adoption in virtual communities: A social influence perspective. International Journal of Information Management, 35, 364–376.
Chung, N., & Han, H. (2017). The relationship among tourists' persuasion, attachment and behavioral changes in social media. Technological Forecasting and Social Change, 123, 370–380.
CNBC (2022). TikTok reaches 1 billion monthly users (cnbc.com).
Coker, D.A., & Burgoon, J.K. (1987). The nature of conversational involvement and nonverbal encoding patterns. Human Communication Research, 13, 463–494.
Cuesta-Valino, P., Gutierrez-Rodriguez, P., & Duran-Alamo, P. (2022). Why do people return to video platforms? Millennials and centennials on TikTok. Media and Communication, 10, 198–207.
Deutsch, M., & Gerard, H.B. (1955). A study of normative and informational social influences upon individual judgement. Journal of Abnormal Psychology, 51, 629–36.
Du, X., Liechty, T., Santos, C.A. and Park, J. (2020), “I want to record and share my wonderful journey’: Chinese millennials’ production and sharing of short-form travel videos on TikTok or Douyin”, Current Issues in Tourism, Vol. 25 No. 21, pp. 3412-3424.
Dwyer, F.R., Schurr, P.H., & Oh, S. (1987). Developing buyer-seller relationships. Journal of Marketing, 51, 11–27.
Fang, Y.H. (2014). Beyond the credibility of electronic word of mouth: Exploring eWOM adoption on social networking sites from affective and curiosity perspectives. International Journal of Electronic Commerce, 18, 67–101.
Filieri, R., McLeay, F., Tsui, B., & Lin, Z.B. (2018). Consumer perceptions of information helpfulness and determinants of purchase intention in online consumer reviews of services. Information & Management, 55, 956–970.
Fiss, P.C. (2011). Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of Management Journal, 54, 393–420.
Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50.
Foroudi, P., Melewar, T.C., & Gupta, S. (2014). Linking corporate logo, corporate image, and reputation: An examination of consumer perceptions in the financial setting. Journal of Business Research, 67, 2269–2281.
Foroudi, P., Dinnie, K., Kitchen, P.J., Melewar, T.C.,& Foroudi, M.M. (2017), IMC antecedents and the consequences of planned brand identity in higher education. European Journal of Marketing, 51, 528-550.
Fu, J.R., Lu, I.W., Chen, J.H.F., & Farn, C.K. (2020). Investigating consumers' online social shopping intention: An information processing perspective. International Journal of Information Management, 54.
Ha, N.M., & Lam, N.H. (2017). The effects of celebrity endorsement on customer’s attitude toward brand and purchase intention. International Journal of Economics and Finance, 9(1), 64–77.
Henningsen, D.D., & Henningsen, M.L.M. (2003). Examining social influence in information-sharing contexts. Small Group Research, 34, 391–412.
Hu, X., Chen, X.Y., & Davidson, R. (2019). Social support, source credibility, social influence, and impulsive purchase behavior in social commerce. International Journal of Electronic Commerce, 23, 297–327.
Huang, Z., & Benyoucef, M. (2013). From e-commerce to social commerce: A close look at design features. Electronic Commerce Research and Applications, 12, 246–259.
Huang, J.H., & Chen, Y.F. (2006). Herding in online product choice. Psychology & Marketing, 23, 413–428.
Insider intelligence (2022). TikTok users worldwide & growth forecast (2021-2025). Available from: https://www.insiderintelligence.com/charts/tiktok-users-worldwide-forecast
Kaplan, M.F., & Miller, C.E. (1987). Group decision-making and normative versus informational influence - effects of type of issue and assigned decision rule. Journal of Personality and Social Psychology, 53, 306–313.
Kaya, B., Abubakar, A.M., Behravesh, E., Yildiz, H., & Mert, I.S. (2020). Antecedents of innovative performance: Findings from PLS-SEM and fuzzy sets (fsQCA). Journal of Business Research, 114, 278–289.
Kim, J.K., Kim, H.K., Oh, H.Y., & Ryu, Y.U. (2010). A group recommendation system for online communities. International Journal of Information Management, 30, 212–219.
Kwahk, K.Y., & Kim, B. (2017). Effects of social media on consumers' purchase decisions: Evidence from Taobao. Service Business, 11, 803–829.
Lai, I.K.W., & Liu, Y.D. (2020). The effects of content likeability, content credibility, and social media engagement on users' acceptance of product placement in mobile social networks. Journal of Theoretical and Applied Electronic Commerce Research, 15, 1–19.
Lee, J.E., & Watkins, B. (2016). YouTube vloggers' influence on consumer luxury brand perceptions and intentions. Journal of Business Research, 69, 5753–5760.
Lee, M.K.O., Cheung, C.M.K., Sia, C.L., & Lim, K.H. (2006). How positive informational social influence affects consumers’ decision of internet shopping?. In Proceedings of the 39th Hawaii International Conference on System Sciences (Vol. 115).
Lee, M.K.O., Shi, N., Cheung, C.M.K., Lim, K.H., & Sia, C.L. (2011). Consumer's decision to shop online: The moderating role of positive informational social influence. Information & Management, 48, 185–191.
Li, C.Y. (2013). Persuasive messages on information system acceptance: A theoretical extension of elaboration likelihood model and social influence theory. Computers in Human Behavior, 29, 264–275.
Liang, T.P., & Turban, E. (2011). Introduction to the special issue social commerce: A research framework for social commerce. International Journal of Electronic Commerce, 16, 5–13.
Liu, X.D., Min, Q.F., & Han, S.N. (2020). Understanding users' continuous content contribution behaviours on microblogs: An integrated perspective of uses and gratification theory and social influence theory. Behaviour & Information Technology, 39, 525–543.
Mackenzie, S.B., & Podsakoff, P.M. (2012). Common method bias in marketing: Causes, mechanisms, and procedural remedies. Journal of Retailing, 88, 542–555.
Maier, C., Laumer, S., Joseph, D., Mattke, J., & Weitzel, T. (2021). Turnback intention: An analysis of the drivers of it professionals' intentions to return to a former employer. MIS Quarterly, 45, 1777–1806.
McCroskey, J.C., Hamilton, P.R., & Weiner, A.N. (2010). The effect of interaction behavior on source credibility, homophily, and interpersonal attraction. Human Communication Research, 1(1), 42–52.
McFarland, D.J., & Hamilton, D. (2006). Adding contextual specificity to the technology acceptance model. Computers in Human Behavior, 22, 427–447.
Meng, K.S. and Leung, L. (2021), “Factors influencing TikTok engagement behaviors in China: An examination of gratifications sought, narcissism, and the Big Five personality traits”, Telecommunications Policy, Vol. 45 No. 7.
Montag, C., Yang, H.B. and Elhai, J.D. (2021), “On the psychology of TikTok use: A first glimpse from empirical findings”, Frontiers in Public Health, Vol. 9.
Okoli, C., & Schabram, K. (2010). A guide to conducting a systematic literature review of information systems research. Sprouts: Working Papers on Information Systems, 10(26).
Pappas, I.O., & Woodside, A.G. (2021). Fuzzy-set qualitative comparative analysis (fsQCA): Guidelines for research practice in information systems and marketing. International Journal of Information Management, 58.
Ragin, C.C. (2008), Redesigning social inquiry: Fuzzy sets and beyond, University of Chicago Press, Chicago, IL.
Ragin, C.C., Strand, S.I. and Rubinson, C. (2008), User’s guide to fuzzy-set/qualitative comparative analysis, University of Arizona Press, Arizona, Tucson, Vol. 87, pp. 1-87.
Rajalahti, T., & Kvalheim, O.M. (2011). Multivariate data analysis in pharmaceutics: A tutorial review. International Journal of Pharmaceutics, 417, 280–290.
Scherr, S. and Wang, K.X. (2021), “Explaining the success of social media with gratification niches: Motivations behind daytime, nighttime, and active use of TikTok in China”, Computers in Human Behavior, Vol. 124.
Sedera, D., Lokuge, S., Atapattu, M., & Gretzel, U. (2017). Likes-The key to my happiness: The moderating effect of social influence on travel experience. Information & Management, 54, 825–836.
Sokolova, K., & Kefi, H. (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.
Song, S.J., Zhao, Y.X.C., Yao, X.L., Ba, Z.C., & Zhu, Q.H. (2021). Short video apps as a health information source: An investigation of affordances, user experience and users' intention to continue the use of TikTok. Internet Research, 31, 2120–2142.
Spector, P.E. (2006). Method variance in organizational research - truth or urban legend?. Organizational Research Methods, 9, 221–232.
Statista (2022). TikTok- statistics & facts | statista. Available from: https://www.statista.com/topics/6077/tiktok/
Tsai, H.T., & Pai, P.Y. (2013). Explaining members' proactive participation in virtual communities. International Journal of Human-Computer Studies, 71, 475–491.
Venkatesh, V., & Brown, S.A. (2001). A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges. MIS Quarterly, 25, 71–102.
Wang, Y.W. (2020). Humor and camera view on mobile short-form video apps influence user experience and technology-adoption intent, an example of TikTok (DouYin). Computers in Human Behavior, 110.
Wang, X.Q., & Li, Y.B. (2014). Trust, psychological need, and motivation to produce user-generated content: A self-determination perspective. Journal of Electronic Commerce Research, 15, 241–253.
Wang, Q.Q., Yang, M.S., & Zhang, W.S. (2021). Accessing the influence of perceived value on social attachment: Developing country perspective. Frontiers in Psychology, 12.
Woodside, A.G. (2013). Moving beyond multiple regression analysis to algorithms: Calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory. Journal of Business Research, 66, 463–472.
Wu, B., & Chen, X.H. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221–232.
Wu, R.Z. and Tian, X.F. (2021), “Investigating the impact of critical factors on continuous usage intention towards enterprise social networks: An integrated model of is success and TTF”, Sustainability, Vol. 13 No. 14, pp. 7619-7639.
Xie, X.Z. and Tsai, N.C. (2021), “The effects of negative information-related incidents on social media discontinuance intention: Evidence from SEM and fsQCA”, Telematics and Informatics, Vol. 56.
Xu, X.Y., & Tayyab, S.M.U. (2021). How the intensity of the immersive experience impels the extent of social streaming app dependency? An empirical assessment with mediation and moderation effects. Telematics and Informatics, 63.
Xu, X.Y., Wu, J.H., & Li, Q. (2020). What drives consumer shopping behavior in live streaming commerce?. Journal of Electronic Commerce Research, 21, 144–167.
Xu, X.Y., Luo, X., Wu, K., & Zhao, W.J. (2021). Exploring viewer participation in online video game streaming: A mixed-methods approach. International Journal of Information Management, 58.
Yang, X. (2021). Determinants of consumers ? Continuance intention to use social recommender systems: A self-regulation perspective. Technology in Society, 64.
Zhang, M.M., Xu, P., & Ye, Y.J. (2022). Trust in social media brands and perceived media values: A survey study in China. Computers in Human Behavior, 127.
Zhao, J. and Wang, J.F. (2020), “Health advertising on short-video social media: A study on user attitudes based on the extended technology acceptance model”, International Journal of Environmental Research and Public Health, Vol. 17 No. 5, pp. 1501-1522.
Zhou, T. (2020). The effect of information privacy concern on users' social shopping intention. Online Information Review, 44, 1119–1133.