Determinants of continued use of social media: the perspectives of uses and gratifications theory and perceived interactivity
Meng-Hsiang Hsu, Department of Information Management, National Kaohsiung First University of Science and Technology, Taiwan
Chun-Ming Chang, Department of Tourism Information, Aletheia University, Taiwan
Hsien-Cheng Lin, Director, Department of Planning, Fooyin University Hospital, Taiwan
Yi-Wan Lin, Department of Information Management, National Kaohsiung First University of Science and Technology, Taiwan
The rapid innovation of information communication technologies have helped social media, such as Facebook, Twitter and MySpace, to become important social platforms that attracts numerous users around the globe to carry out computer-mediated communications (Kim, Sohn and Choi, 2011; Lin and Lu, 2011). According to a recent survey conducted by e-Marketer in 2013, more than 22.8% of people in the globe have used social media (iThome, 2013). This survey also reports that the penetration rate of social media in some counties (e.g., the Netherlands, Denmark, Norway, Sweden and United States) is greater than 50%, indicating that the popularity of social media has enabled people to use social media to build and maintain their personal relationships.
The popularity of social media has motivated many scholars to investigate why people use social media (Kim et al., 2011). Generally speaking, research for examining motives of social media usage can be classified into two perspectives. Based on the uses and gratifications theory, the first perspective asserts that psychological needs and gratifications are the determinants of social media usage (Lee and Ma 2012; Lu, Lin, Hsiao and Cheng, 2010). Prior studies from this perspective have found that entertainment, information seeking, socialisation and self-presentation are the key factors affecting social media usage (Dunne, Lawlor and Rowley, 2010; Li, 2011; Park, Kee and Valenzuela, 2009). On the other hand, the second perspective posits that social interactions affect social media usage (Li, 2011); Pempek, Yermolayeva and Calvert, 2009). Research from this perspective has found that perceived interactivity will impact users’ satisfaction, which in turn affects social media usage (Lu et al., 2010; Zhao and Lu, 2012).
Although many studies have examined the motives for using social media, most of them focus on the factors determining information systems adoption, which may limit the understanding about the antecedents of users’ post-adoption behaviour. Since continued usage is considered as a power indicator for measuring the success of an information system, including social media (Bhattacherjee, 2001), the focus of this study is users’ post-adoption behaviour. In addition, while these two streams of research have provided useful insights about the determinants of social media use, few studies have been done to integrate these two distinct perspectives in order to examine the antecedents of continued use of social media. Uses and gratifications theory suggests a variety of needs will enable users to use an information system to fulfill their needs and that their own use experience will impact satisfaction and choice for continual use of an information system. The authors of this study believe that integrating uses and gratifications theory and perceived interactivity to examine the antecedents of continued usage of social media may provide insight into what can motivate users to continue social media use after their initial acceptance.
The purpose of this study is to answer the following questions: (1) Do motivations (entertainment, socialisation, information-seeking and self-presentation) impact perceived interactivity (human-human interaction, human-message interaction and human-community interaction)? (2) Does perceived interactivity influence satisfaction and continued use of social media? This study is organized as follows. First, we review the existing literature of uses and gratifications theory and perceived interactivity to propose our conceptual model, research and hypotheses. Then research methodology and data analysis are described. Finally, we discuss the implications of research findings for theory and practice.
Uses and gratifications theory
Uses and gratifications theory asserts that users of media are active and goal-oriented (Sangwang, 2005) therefore they will be motivated to select a medium that best gratifies their needs (Luo, Chea and Chen, 2011; Sangwang, 2005). That is, the primary function of uses and gratifications theory is to explain how psychological needs can shape peoples’ reasons to use a medium to fulfill their needs (Roy, 2009; Rubin, 1994). Gratifications of these needs, on the other hand, lead to continuance of media use (Sangwang, 2005). Thus, based on the uses and gratifications theory, satisfaction is treated as a vital antecedent of continued use of social media.
Uses and gratifications theory has been widely used to examine the users’ motivations for using certain media, including social media (Luo et al., 2011; Nguyen and Western, 2006; Thelwall, Byrne and Goody, 2007; Wang, Woo, Quek, Yang and Lin, 2012). For example, Shao (2009) reports that users use social media to satisfy their information, entertainment and mood management needs. Chua, Goh and Lee (2012) found that the motivations that bring people use social media are leisure/entertainment, easy access, information resources/services and obtaining high quality information. Other scholars have found that the motivations for using social media are entertainment, information seeking, socialisation, passing time, escapism and professional advancement (Chen, Yang and Tang, 2013; Papacharissi and Mendelson, 2011). Additionally, researchers have found that a purpose to which users use social media is present themselves online, to develop and maintain their social networks and relationships (Boyle and Johnson, 2010; Ellison, Steinfeld and Lampe, 2007). Similarly, Lenhart (2009) posits that approximately 90% of social media participants create a profile to get in touch with friends or meet new friends. By synthesizing the above standpoints, we recognize that the motivations for using social media can be divided into four types, namely entertainment, socialisation, information seeking and self-presentation.
Entertainment refers to the pleasure or fun derived from social media use (Lee and Ma, 2012), while socialisation is the need for an individual to connect with others for building and maintaining relationships (Andrew, Nicole, Cliff and Donghee, 2011). Information seeking is the desire to obtain information through social media (Lee and Ma, 2012), while self-presentation is the need to disclose personal information to enhance identity (Schlenker and Wowra, 2003).
Social media have been considered as useful platforms that allow people to interact with others to strengthen relationships (Ko, Cho and Roberts, 2005). From the viewpoint of uses and gratifications theory, motivations may enable users to interact with others through social media. In this sense, we may propose that motivations will impact upon users’ perceptions about interactivities when using social media. However, few studies on uses and gratifications theory have been done to test the influences of motivations on perceived interactivity, which in turn impact satisfaction and continued use. Therefore, this study aims to examine the effects of motivations on perceived interactivity, satisfaction and continued use.
Perceived interactivity and its classification
Perceived interactivity is a difficult construct to define because it has different meanings in different contexts (Lowry, Romano Jr., Jenkins and Guthrie, 2009). In general, perceived interactivity can be defined from three different perspectives: as a feature of technology, as a process of message exchange and as a user’s perception after using technology or going through a process (Lowry et al., 2009; Zhao and Lu, 2012). In this study, we use the perspective of users’ perception and define it as the extent to which users perceive their experiences as a simulation of interpersonal interaction and sense they are in the presence of a social other ( Zhao and Lu, 2012f).
Many researchers agree that interactivity is a complex and multidimensional concept ( Burgoon, Bonito, Bengtsson, Ramirez, Dunbar and Miczo, 2000; Zhao and Lu, 2012). Generally speaking, interactivity can be divided into two types, human-human interaction and human-message interaction (Ko et al., 2005). Human-human interaction refers to the extent to which users believe that they can communicate with others via the functions of social media (Lu et al., 2010). That is, human-human interaction denotes the reciprocal communication from senders to receivers and vice versa via social media (Ko et al., 2005). Human-message interaction, on the other hand, refers to participants’ interactions with messages (browsing and sharing messages) through the functions of social media (Lu et al., 2010). In general, a higher level of human-message interaction can improve the effectiveness of information searching and feedback. This kind of interactivity has been considered as an important dimension of the social interaction (Bettman, Luce and Payne, 1998; Lu et al., 2010).
Using social media could enable users to invite others with common interests to build their own communities. That is, users may use social media to conduct social interactions with community, since they tend to connect themselves to the community and obtain a sense of belonging (Hsu, Chang and Yen, 2011; Li, Browne and Wetherbe, 2006). Thus, in this study, we consider the human-community interaction as an additional type of perceived interactivity in the context of social media and define it as one’s feelings of being close to or connected to a community via the functions of social media (Zhao and Lu, 2012).
Research model and hypotheses
Based on the theoretical background presented, the research model is shown in Figure 1. The rationale of the model is explained below.
Motivations and perceived interactivity
As mentioned earlier, a key value social media can provide to users is allowing them to escape from reality and obtain enjoyment (Lee and Ma, 2012). Papacharissi and Mendelson (2011) also posit that entertainment is an important factor driving users’ decision for the use of social media. Previous research suggests that the key motivations for using social media are social interactions and peer identity (Luo et al., 2011). In addition, recent studies suggest that entertainment needs will motivate users of social media to share information with others within online communities (Lee and Ma, 2012; Nov, Naaman and Ye, 2010) and to read news in the Internet (Diddi and LaRose, 2006; Lee and Ma, 2012). Taken together, we may propose that entertainment will exert positive influences on human-human interaction, human-message interaction and human-community interaction.
H1a: Entertainment is positively related to human-human interaction.
H1b: Entertainment is positively related to human-message interaction.
H1c: Entertainment is positively related to human-community interaction.
Previous research also suggests that users typically use social media to communicate with friends (Lee and Ma, 2012; Papacharissi and Rubin, 2000) to fulfill their socialisation needs (Park et al., 2009). Some researchers have also found that social media could facilitate interpersonal communication (Cemalcilar, Falbo and Stapleton, 2005; Ellison et al., 2007; Viswanath, Mislove, Cha and Gummadi, 2009) and achieve sense of belonging (Lee and Ma, 2012; Rubin, 1994). By synthesizing their arguments, we may recognize that social media can be considered as interactive mechanisms by which users could exchange information with others to strengthen social ties with others in their communities when they want to fulfill their socialisation needs. This leads to the following hypotheses.
H2a: Socialisation is positively related to human-human interaction.
H2b Socialization is positively related to human-human interaction.
Generally speaking, a major reason for people to use social media is to access and share information. According to Wang et al. (2012), users tend to use social media to acquire and share information with others. Similarly, Park et al. (2009) suggest that knowledge acquisition and sharing is one of the needs met by students using social media. Lee and Ma (2012), on the other hand, find that information seeking is a significant factor affecting users’ intention to share information. Thus, we may recognize that users are more likely to search and share information by using the social media, if they have information seeking needs. This leads to the following hypothesis.
H3a: Information seeking is positively related to human-message interaction.
Previous researchers have argued that regular communication will facilitate the development of social relationships among people (Panteli and Sockalingam, 2005). In the online context, users can use social media to provide others with timely and relevant information to satisfy their information needs (Lee and Ma, 2012). By seeking information posted by users, shared values and understanding can be enhanced. Thus, users’ sense of belonging to the group can be increased as well (Panteli and Sockalingam, 2005). In this sense, we propose that information seeking is a vital antecedent that can give rise to human-community interaction.
H3b: Information seeking is positively related to human-community interaction.
In general, social media provide attractive places for users to present themselves to connect to others (Cheung and Lee, 2010; Lin and Lu, 2011). Similarly, other researchers have found that self-presentation is an important factor that will facilitate interpersonal communications (Laurenceau, Barrett and Pietromonaco, 1998). Ma and Agarwal (2007) find that self-presentation will increase perceived identity verification, which in turn affects knowledge contribution. Hence, in this study, we may expect that self-presentation will facilitate information exchange among users. This leads to the following hypothesis.
H4a: Self-presentation is positively related to human-human interaction.
In addition, previous studies (e.g., Hew and Hara, 2007; Kaiser and Müller-Seitz, 2008) postulate that people generally present themselves to attain status among peers and to be recognized by members in a social system. Researchers also argue that social media may enable users to present themselves to connect to their personal network and thus strengthen the structure of their social systems (Ellison et al., 2007; Kane, Fichman, Gallaugher and Glaser, 2009; Lin and Lu, 2011). Therefore, we may recognize that self-presentation will impact the development of human-community interaction. This leads to the following hypothesis:
H4b: Self-presentation is positively related to human-community interaction.
Perceived interactivity and satisfaction
Satisfaction refers to an individual’s evaluation of and his/her psychological state resulting from his/her overall experience with a product or service (Kim 2008; Oliver 1980; Zhao and Lu, 2012). From the standpoint of uses and gratifications theory, users are motivated to use social media to meet their needs, resulting in a changed level of gratification and satisfaction (Lu et al., 2010). In general, gratification derived from use of the internet can be divided into three types: process gratification, content gratification and social gratification (Lu et al.; 2010; Stafford, Stafford and Schkade, 2004). When a user uses social media to interact with others in the social system, human-human interaction and human-community interaction will increase process gratification and social gratification, since website design will determine users’ attitude (i.e., satisfaction) toward website use. The fulfillment of socialization needs will influence users’ pleasure and flow experience. At the same time, human-message interaction will impact content gratification, similar to the assertion of Kim, Xu and Koh (2004) that content quality will impact on users’ satisfaction. Previous research has also provided empirical evidence to the link between perceived interactivity and satisfaction (e.g., Lu et al., 2010; Zhao and Lu, 2012). Thus,
H5: Human-human interaction is positively related to satisfaction.
H6: Human-message interaction is positively related to satisfaction.
H7: Human-community interaction is positively related to satisfaction.
Satisfaction and continued use of social media
Satisfaction has been considered an important factor determining continued use of information systems (Lin and Wang, 2006). If a user revalues his or her use experience of an information system positively, it is likely that his or her willingness to use such information system again will increase. Researchers find that people tend to use information systems more frequently when they have high level of satisfaction (e.g., Wang et al., 2012; Kwon, 2005). Bhattacherjee (2001) and Zhao and Lu (2012) also provide empirical support for the link between satisfaction and continued use of information system. Therefore,
H8: Satisfaction is positively related to continued use of social media.
Human-human interaction, human-community interaction and continued use of social media
Social media provide places for users to carry out human-human interactions in the virtual setting. Past literature has found that the interpersonal interactions can facilitate users’ loyalty to social media (Shen, Cheung, Lee and Chen, 2011). From the viewpoint of motivation theory (Deci, 1975), when users’ needs for interpersonal communications can be fulfilled, their behaviour can be motivated. Therefore, we may argue that human-human interaction will have a significant influence on continued use of social media.
H9: Human-human interaction is positively related to continued use of social media.
On the other hand, Tamborini and Skalski (2006) describe that a stable environment of social media will enable users to undertake activities in social media. Likewise, Marlow (2006) suggests that the group harmony of social media will directly influence their use intention. In addition, previous research also finds that a strong sense of belonging to a group is a key motivator for the use of social media (e.g., Cheung and Lee, 2010). By synthesizing their statements, we may expect that human-community interaction will exert positive influence on continued use of social media.
H10: Human-community interaction is positively related to continued use of social media.
We collected data from the users of Facebook in Taiwan to examine the proposed research model of this study. Facebook was selected because it is a well-known social medium in use around the globe, including Taiwan. Other researchers have also used the users of Facebook as their respondents (e.g., Lin and Lu, 2011). In order to target the users of Facebook, a Web-based survey questionnaire was employed. Following Lin and Lu (2011) and Hsu et al. (2011), this study posted the messages about questionnaires on the websites of Facebook in March 2013. Active users of Facebook were invited to participate in this survey. In order to encourage participation, one hundred randomly selected participants were offered an incentive in the form of a gift certificate at a convenient store amounting to USD$3.30, following the recommendation of Hsu et al.. When the questionnaires were received, respondents’ e-mail addresses were checked to avoid replications (Lin and Lu, 2011). By the time this survey was concluded, 306 questionnaires were collected. The exclusion of ten invalid questionnaires (incomplete or e-mail with similar address) resulted in a total of 296 complete and valid questionnaires for data analysis. Table 1 lists the demographic characteristics of the respondents.
|Hours of Facebook use per week||More than 15||28||9.5|
|Less than 1||75||25.3|
Generally, the demographics of respondents are in line with the recent surveys that found more people on social media were males than females in Taiwan (ACI-FIND, 2014), but both samples were nearly equal in number (Lin and Lu, 2011). The demographics of respondents are also similar to the survey conducted by Pew Research Center which found that the years of age for the greatest percentage of Facebook users ranged from 18 to 29 (TechHive, 2013). Accordingly, we recognize that the sample in the study represents overall Facebook users in Taiwan. In addition, Table 1 also shows 15.2% of participants are young people between the ages of 10 to 19. In line with Limayem, Hirt and Cheung (2007), this indicates that young people (e.g., students) generally use social media (e.g., Facebook) to keep in touch with their friends. Several researchers report that no systematical difference occurred between young users (i.e., student subjects) and adult users (e.g., organizational users) in the previous studies (Bhattacherjee and Premkumar, 2004; Hsu, Yen, Chiu and Chang, 2006). Thus, we believe that young users can be the representative of Facebook users in this study.
The measurement items of the constructs were developed based on the existing literature wherever possible. Items for measuring entertainment and socialization were adapted from Andrew et al. (2011) and Wu, Wang and Tsai (2010), while scales used to measure information seeking were adapted from Andrew et al. (2011) and Lee and Ma (2012). Social presentation measures were adapted from Andrew et al. (2011) and Pempek et al. (2009), whereas items for measuring human-human interaction were adapted from Lu et al. (2010). Items used to measure human-message interaction were adapted from Lu et al. and Taylor, Cool, Belkin and Amadio (2007), while human-community interaction were measured using items developed based on Yang and Lai (2011). Items for measuring satisfaction were adapted from Ko et al. (2005) and Lu et al., whereas continued use was measured using items adapted from Wu et al. (2010). The survey questionnaire was measured using a 5-point Likert scale with anchors ranging from strongly agree (5) to strongly disagree (1). The items are presented in Table 1 in the Appendix.
In this study, we employed the partial least squares regresson to carry out data analysis. The method was chosen because it places minimum restrictions on sample size, measurement scales and residual distributions (Chin, Marcolin and Newsted, 2003). This study used a two-step approach recommended by Anderson and Gerbing (1988). The first step was to examine the measurement model and then we investigated the structural relationships among the latent constructs. In this study, we used SmartPLS 2.0 M3 (Ringle, Wende and Will, 2005) to test our research model.
Measurement model test
To test the measurement model, confirmatory factor analysis was conducted. We assessed the reliability, convergent validity and discriminant validity of the constructs, following Kang, Hong and Lee (2009).
|Information seeking (SI)||0.88||0.60||0.55||0.56||0.77|
|Human-human interaction (HH)||0.90||0.65||0.63||0.65||0.55||0.60||0.81|
|Human-message interaction (HM)||0.91||0.67||0.48||0.48||0.68||0.53||0.39||0.82|
|Human-community interaction (HC)||0.90||0.61||0.63||0.56||0.66||0.64||0.59||0.60||0.78|
|Continuous use (CU)||0.85||0.66||0.60||0.49||0.48||0.51||0.54||0.39||0.59||0.52||0.81|
|Diagonal elements in the correlation of constructs matrix are the square root of the average variance extracted. For adequate discriminant validity, diagonal elements should be greater than corresponding off-diagonal elements.|
As shown in Table 2, all the values of composite reliability (CR) are above the threshold of 0.7 and the values of average variance extracted (AVE) are greater than 0.5 as well, indicating the adequate reliability (Fornell and Larcker, 1981). In addition, the results show that loadings of indicators are much higher on their hypothesised factor than on other factor (see Appendix Table 2) and the square root of average variance extracted are larger than all other cross correlations, indicating the acceptable convergent validity and discriminant validity.
Structure model test
The hypothesized relationships were estimated using 500 interactions of bootstrapping technique in SmartPLS. Figure 2 shows the results of analysis.
The results demonstrate that all of the hypotheses described earlier are supported. As expected, entertainment has a positive influence on human-human interaction, human-message interaction and human-community interaction (β=0.295; 0.153; 0.258, t-value=5.055; 2.747; 3.936, respectively). Thus, H1a, H1b and H1c are supported. Socialization has significant impacts on human-human interaction and human-community interaction (β=0.378; 0.138, t-value=5.821; 2.738, respectively), thus, H2a and H2b are supported. Information seeking has positive influences on human-message and human-community interaction (β= 0.600; 0.311, t-value= 13.119; 5.495, respectively), so H3a and H3b are supported. Self-presentation also has significant positive impacts on human-human interaction and human-community interaction (β=0.209; 0.207, t-value= 2.936; 3.342, respectively). Therefore, H4a and H4b are supported.
The results also show that human-human interaction, human-message interaction and human-community interaction have significant influence on satisfaction (β= 0.247; 0.342; 0.218, t-value= 3.963; 5.229; 4.139, respectively). Thus, H5, H6 and H7 were supported. In addition, satisfaction, human-human interaction and human-community interaction exert significant influences on continued use of social media (β= 0.218; 0.270; 0.263, t-value= 3.420; 3.696; 4.113, respectively). Therefore, H8, H9 and H10 are supported. The proportions of variances explained were 56% for human-human interaction, 49% for human-message interaction, 58% for human-community interaction, 47% for satisfaction and 40% for continued use of social media.
Discussion and conclusions
In order to understand the determinants of continued use of social media, this study proposes a theoretical model by integrating uses and gratifications theory and perceived interactivity. Our findings show that entertainment exerts positive influence on human-human, human-message interaction and human-community interaction. Our findings are consistent with the assertions of previous studies that entertainment and social interactions are important and complementary and can influence users’ decision to use social media (Ko et al., 2005; Lee and Ma, 2012; Luo et al., 2011). Similar to previous literature (Dunne et al., 2010; Park et al., 2009), our results also indicate that the socialization will have positive impact on human-human interaction and human-community interaction. This finding reveals that people will use social media to communicate with friends or to interact with their peers when they tend to extend and maintain their interpersonal relationships. On the other hand, users’ motivation of information seeking in positively associated with human-message interaction and human-community interaction. The findings indicate that users will use social media more frequently to find or browse information and to seek the sense of belonging to the online community if they have high levels of information seeking needs. In addition, our results reveal that self-presentation has significant influence on human-human interaction and human-environment interaction. The findings are similar to previous research which found that people will attempt to maintain their self-relevant images to increase social identity through social interactions (Laurenceau et al., 1998; Schlenker and Wowra, 2003).
In the study, we also found that human-human interaction, human-message interaction and human-community interaction exert strong influences on satisfaction, which in turn impacts continued use of social media. These findings provide additional evidence to support the standpoint ofprevious research (e.g., Lu et al., 2010; Zhao and Lu, 2012) that satisfaction is an important antecedent of continued use of an information system and that perceived interactivities will influence the formation of satisfaction. Finally, our results also indicate that human-human interaction and human-community interaction have positive influences on continued use of social media. The findings provide empirical evidence to support the link between perceived interactivity and users’ post-adoptive behaviour.
Our findings suggest several important implications for theory. First, although past studies have used uses and gratifications theory to identify the motivations of social media use (Lee and Ma, 2012; Luo et al., 2011) and employed the aspect of perceived interactivity to examine the factors affecting social media use (Lu et al., 2010; Zhao and Lu, 2012). Few studies have been conducted to integrate perceived interactivity to examine the determinants of continued use of social media. To the best of our knowledge, this study is one of the earliest studies to examine the link between motivations and perceived interactivity and is a contribution of this study to the literature. Second, while prior literature has tested the influences of human-human interaction and human-message interaction on an individual behaviour (e.g., Ko et al., 2005), it has been incomplete in that it has only identified two possible types of interaction. In this study, we propose a new construct, human-community interaction. Our study demonstrates the effect of human-community interaction on satisfaction and continued use of social media and its complementary relationship to the two previously identified types of interaction in the literature.
From a practical perspective, our results also provide implications for social media service providers who want to improve their service quality and promote their competitive advantage. Theresults show that motivations will facilitate users’ interactions in social media, which in turn impacts satisfaction and continued use of social media. We suggest that managers of social media should deploy some strategic elements to satisfy the users’ needs such as entertainment, socialization, information seeking and self-presentation to facilitate social interactions via social media. In this way users’ intention to use social media continuously can be increased. First, management of social media are advised to provide some interesting games on social media with which users can invite others to use the games together to increase their perception of enjoyment. In addition, in order to satisfy users’ information-seeking needs, managers of social media can provide a mechanism to highlight users who contribute information actively or award users based on the amount the amount of information sharing (Lee and Ma, 2012). In order to fulfill users’ needs for self-presentation, management of social media could develop new features to allow users to upload video clips to introduce themselves or generate user profiles automatically by using their past activities (Ma and Agarwal, 2007). Finally, management of social media should improve some tools or functions (e.g., editing, organizing and uploading) to increase the quality of user interface for direct and indirect interactions among users to meet the needs of socialization (Lu et al., 2010).
Despite the contribution of this study, there are some limitations. First, the data of this research were mainly collected from Facebook users in Taiwan. However, every country or region has its unique cultural background which will influence the thoughts and behaviours of its residents (Fang and Prybutok, 2013). Further studies should be conducted to test the results of this study in different countries. Second, this study may suffer from selection bias because our participants are active online users only. These users who cease to use Facebook may present different perceptions about their motivations, perceived interactivity, satisfaction and continued use behaviour. Finally, this study mainly focuses on the effects of motivations, perceived interactivity and satisfaction. Other factors (e.g., social influence) that may impact continued use of social media should also be tested by future studies.
As Chen (2012) describes, users’ loyalty is a critical factor related to the survival and development of a firm. Hence, understanding what factors will impact users’ decision to use social media continuously is a key issue for organizations that own and operate social media. In this study, we propose a theoretical model by integrating the perspectives of uses and gratifications theory and perceived interactivity. The results show that entertainment impacts human-human interaction, human-message interaction and human-community interaction significantly, while socialization has positive influence on human-human interaction and human-community interaction. The results also report that information seeking influences human-message interaction and human-community interaction positively, whereas self-presentation exerts significant effects on human-human interaction and human-community interaction. Moreover, the three types of perceived interactivity have positive effects on satisfaction, while satisfaction, human-human interaction and human-community interaction are the powerful predictor of continued use of social media.
About the authors
Meng-Hsiang Hsu holds a PhD degree from National Sun-Yat-Sen University, Taiwan. He is currently a distinguished professor at the Department of Information Management, National Kaohsiung First University of Science and Technology. His research interests include knowledge management, information ethics and electronic commerce.
Chun-Ming Chang is currently an associate professor at the Department of Tourism Information, Aletheia University, Taiwan. He holds a PhD degree in management from National Kaohsiung First University of Science and Technology, Taiwan. His current research interests include electronic commerce and knowledge management.
Hsien-Cheng Lin is currently an assistant professor at the Department of Health-Business and Administration, Fooyin University and the chief at Department of Planning, Fooyin University Hospital, Taiwan. He holds a PhD degree in management from National Kaohsiung First University of Science and Technology, Taiwan. His current research interests include knowledge management and medical informatics. He is the corresponding author for the article and can be contacted at email@example.com
Yi-Wan Lin holds a Master degree in information management from National Kaohsiung First University of Science and Technology, Taiwan.