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The comparison study on employees’ adoption of public and enterprise social networks

Abstract

BACKGROUND:

The social network services (SNSs), such as Facebook, Twitter, Yammer and Slack etc., allow users to post short messages on topics ranging from personal hobbies and interests to working messages and knowledge. However, the answers to how use context and participation behavior pattern influence individual’s engagement in social network services still remain vague.

OBJECTIVE:

This study aims to find out the adoption mechanism in two contexts – Enterprise social network services (ESNs) and Public social network services (PSNs).

METHODS:

This paper adopts the methods of literature research, questionnaire survey and statistical analysis. The research model was established by structural equation and analyzed by AMOS software.

RESULTS:

Our empirical results show that the use of social network platforms is to a certain extent embedded both in the use context (public or enterprise) and in different kinds of participation modes (original participation or secondary participation). For both ESNs and PSNs, perceived playfulness is the most important adoption factor in both participations, while perceived usefulness just works for the original participation.

CONCLUSIONS:

Compared with ESNs that are weighted in favor of the utilitarian-oriented perspective, PSNs are inclined to be more hedonic-oriented. The findings offer us novel insights on understanding and applying social network services.

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Jiayin Qi (Ph.D., Xi’an Jiaotong University) is a professor at the Institute of Artificial Intelligence and Change Management, Shanghai University of International Business and Economics (SUIBE). She serves as the Dean of Institute of Artificial Intelligence and Change Management and the director of Key Lab Data Science and Management Decision. She is also a part-time professor of Beijing University of Posts and Telecommunications (BUPT). Her research interests are social network analysis, big data driven decision making and artificial intelligence. Email address: .

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Lianren Wu (Ph.D., Beijing University of Posts and Telecommunications) is an assistant professor at the Institute of Artificial Intelligence and Change Management, Shanghai University of International Business and Economics (SUIBE). His research interests are social network analysis and social communication. Email address: .

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Miaomiao Xiong is a graduate with a master’s degree in Management Science and Engineering, Beijing University of Posts and Telecommunications (BUPT). She serves as a data analyst of Cyber User Management and Service Center, China Merchants Bank. Email address: .

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Shuaibo Hu is a master’s degree candidate in Business Management, College of Business Administration, Shanghai University of International Business and Economics (SUIBE). His research interests are data science and management decision, and his master’s thesis is mainly about human and robot interaction. Email address: .

1Introduction

The social network services (SNSs), such as Facebook, Twitter, Yammer and Slack etc., allow users to post short messages on topics ranging from personal hobbies and interests to working messages and knowledge. In general, they have become well-accepted technology in daily lives and working places. These services have profoundly changed the way people acquire knowledge, share information and interact with one another on a societal scale [1].

Public social network services (PSNs) are the SNSs using social network (e.g. Facebook and Twitter) in public daily life, which are mainly used for social interaction among friends or other acquaintances [2]. Enterprise social network services (ESNs), such as Yammer and Slack, are increasingly attracting companies since they promise to offer enormous potentials to enhance organizational collaboration, innovation, and performance [3].

PSNs nowadays are almost daily partners that everyone cannot live without. According to Statista website and Facebook, two of the biggest social network sites worldwide, the number of monthly active users as of the July 2019 has reached 2.38 billion, accounting for 31.6% of the global population. Compared with PSNs, the adoption of ESNs is rather unexpected. Many companies attempting to keep employees engaged by creating ESNs struggle to reach a wider adoption of their ESNs by their employees, but fail the expectations for participation, which is partly due to a sharp drop in interest and usage after initial enthusiasm among employees [4].

Therefore, how to maintain the enthusiasm and motivation within ESNs and encourage employees to use this service are important issues. Compared to the intensive focus on the adoption mechanism of PSNs, that of ESNs for employees seems to have attracted much less scholarly attention. Is the adoption mechanism of ESNs the same as that PSNs? If the answer is yes, then companies can borrow the experiences from PSNs to enhance employees’ ESNs adoption. If the answer is no, then companies have to develop other specific strategies for employees’ ESNs adoption.

The research question in this paper has both theoretical and practical implications. Theoretically speaking, the answer of this question not only provides the adoption mechanism of ESNs, but also detects how use context influences the adoption of SNSs: if the answer is yes, it means that use context has no influence on an individual’s adoption of social network sites; if the answer is no, it indicates that the former has a real influence on the latter. Practically speaking, the answer can provide proper guidance to companies’ ESNs adoption strategies.

This research is aiming to conduct a comparative study on user’s adoption on enterprise and public social networks. To achieve this, a rational research model on the basis of the technology acceptance model is proposed, in an attempt to explain why people use the social networking tools under both enterprise and public contexts. To further explore the differences in details, employees’ social network services participation behaviors are divided into original participation and secondary participation. Thus, the respective adoption mechanisms of the two different participations under two different use contexts are studied accordingly. With the exclusive data support from China Mobile, certain valuable results are generated.

2Definitions and literature review

SNSs are evolving concept because the supporting technologies are developing continually. Boyd and Ellison define SNSs as web-based services that allow individuals to construct a public or semi-public profile within a bounded system. They articulate a list of other users with whom they share a connection, and view and traverse their own list of connections and those made by others within the system [5]. Carr and Tayes define social media as Internet-based channels that allow users to be opportunistically interactive and selectively self-present, either in real-time or asynchronously, with both broad and narrow audiences to derive value from user-generated content and the perception of interaction with others [6]. According to Hu and Shi, a social networking site is defined as “a social aggregation that emerges from the Internet when sufficient numbers of individuals continue a public discussion for a certain amount of time, with sufficient human feeling, to form webs or connections of personal relationships in cyberspace” [7]. Yu define SNSs as the capacity to develop connections, post a profile, and give users the ability to access and interact with diverse streams of other users [8]. Erfani and Abedin define the term as networked communication platforms in which users can create profiles and content, establish connections, develop audio and video interactions with their connections, and exchange user-generated content [9]. Huang and Shih promote this notion as online services, platforms, or websites that enable the construction and reflection of social networks or social relations among individuals [10]. We can see that there are many different understandings of social media. Just as Carr and Tayes have mentioned, there is no commonly-accepted definition of what social media is, both functionally and theoretically. They hope that their definition of social media is precise enough to embody the development of technologies, and robust enough to remain applicable up to 2035. Therefore, the present study is based on Carr and Tayes ’s version, which is considered to be more sustainable [6].

2.1Definition of PSNs

PSNs are platforms to build social network/ relations among people who share interests, activities, background or real-life connections [11, 12]. The classical ones include Facebook, Twitter, Myspace, Sina Weibo, WhatsApp, Instagram, Skype, Viber, China’s WeChat, Japan’s Line etc [13]. PSNs allow users to express themselves, connect to a social network, while develop and keep good relations with others. Compared with ESNs, PSNs are normally used by individuals on public SNSs platforms, while ESNs are just used by employees on company-boundary SNSs platforms.

2.2Definition of ESNs

What are exactly ESNs? Ref. [14] define them as: “Web-based platforms that allow workers to (1) communicate messages with specific coworkers or broadcast messages to everyone in the organization; (2) explicitly indicate or implicitly reveal particular coworkers as communication partners; (3) post, edit, and sort text and files linked to themselves or others; and (4) view the messages, connections, text, and files communicated, posted, edited and sorted by anyone else in the organization at any time of their choosing”. However, according to Carr and Tayes’s notion for social media [6], our definition of ESNs is internet-based, which involves web-based applications and other applications of the internet, file transfer protocols (FTP), and media streaming to facilitate communication by circumventing the web all together.

Leonardi and his colleagues believe that there are four affordances provided by ESNs that are made distinct from other communication technologies commonly used in organizations: visibility, persistence, editability and association [15, 16]. More specifically, the new set of tools provides people an unprecedented level of visibility into the knowledge, activities, and work behaviors of others; the possibility of preserving and analyzing the traces of communicative actions over time; the ability to carefully craft a communicative act for designated audience; and the opportunity to connect with new people and information. As a result, these new technologies have been poised to greatly improve the efficiency of knowledge workers and unlock a tremendous amount of business value.

2.3Literature review

Table 1 shows an overall of previous studies on enterprise and public social networks’ user adoption. In general, it can be found that most of the current researches have only focused on PSNs rather than ESNs. Fewer academics have been using the adoption theory to test the use of SNSs in enterprises. Take a study of Yammer conducted in one Fortune 500 company as an example. An employees’ ESNs behavior analysis is performed in this study, the end of which talks about the barriers to adopt ESNs, such as the noise-to-value ratio paradoxes. Sledgianowski studies the micro-blogging adoption in enterprise and discovers that perceived usefulness and privacy concerns will influence employees’ usage [17]. Engelstätter and Sarbu explore the adoption of ESNs, but their work is merely conducted on the company-level, without considering the viewpoint of individual employees. More importantly, few studies have done a comparative analysis on individual’s adoption mechanism between these two social network sites [18]. Although Engelstätter and Sarbu discuss about this topic, they only have a content analysis of the 5000 public posts from 34 employees on Twitter and their ESNs, yet fail to involve adoption mechanism. Xiong compares human behaviors and motivations behind behaviors toward using enterprise and public social networks, without involving the adoption mechanism difference between the ESNs and PSNs [1]. Therefore, we believe it will be meaningful to study why employees use ESNs at an individual level by comparing the adoption of PSNs and ESNs. Table 1 shows the summary of our literature review.

Table 1

Reviews of Historical Literature on ESNs and PSNs

Research Topics
AuthorsPSNsESNsComparison
Günther et al. (2009)
Kang et al. (2009)
Pelling & White (2009)
Zhao and Rosson (2009)
Pelling and White (2009)
Barnes and Böhringer (2009)
Zhang et al (2010)
Kwon and Wen (2010)
Ehrlich and Shami (2010)
Baker and White (2010)
Barnes & Böhringer (2011)
Chang & Zhu (2011)
Maier et al. (2011)
Schöndienst & Krasnova (2011)
Lin, Chiu & Lim (2011)
Lin and Lu (2011)
Leng et al. (2011)
Engelstätter & Sarbu (2011)
Chang & Zhu (2012)
Riemer & Scifleet (2012)
Agrifoglio (2012)
Zhang and Pentina (2012)
Gao et al. (2012)
Lin et al. (2012)
Moore and McElroy (2012)
Engelstätter and Sarbu (2013)
Chiang (2013)
Pentina et al. (2013)
Pentina, Zhang, & Basmanova (2013)
Ku, Chen & Zhang (2013)
Zhu & Chang (2013)
Berger et al. (2014)
Amos & Zhang (2014)
Feng & Xie (2014)
Lin & Lu (2014)
Pentina, Basmanova & Zhang (2014)
Nikou & Bouwman (2014)
Park (2014)
Kruse & Baumöel (2016)
Ulmer & Pallud (2014)
Kügler & Smolnik (2014)
Zhou & Li (2014)
Xiong, Chen & Zhao (2014)
Mark et al. (2014)
Hsu Yu & Wu (2014)
Men & Tsai (2014)
Gao & Bai (2014)
Kwon, Park & Kim (2014)
Kwon, Stefanone & Barnett (2014)
Sun et al. (2014)
Huang Hsieh & Wu (2014)
Lorenzo-Romero & Chiappa (2014)
Wu, Tao, Li, Wang & Chiu (2014)
Zhu et al. (2014)
Baran & Stock (2015)
Buettner (2015)
Pentina (2015)
Wang, Xu & Chan (2015)
Li, Lin & Wang (2015)
Chiu & Huang (2015)
Antonius, Xu, & Gao (2015)
Chen & Sharma (2015)
Maier et al. (2015)
Wong et al. (2015)
Chang, Hung, Cheng & Wu (2015)
Salahshour et al. (2015)
Yoon & Rolland (2015)
Syn & Oh (2015)
Chin, Evans & Choo (2015)
Chin, Choo & Evans (2015)
Chin, Evans, Choo & Tan (2015)
Mouakket (2015)
Bristy (2016)
Gu, Oh & Wang (2016)
Chaouali (2016)
Mäntymäki & Riemer (2016)
Zemaitaitiene et al. (2016)
Lacka & Chong (2016)
Stei, Sprenger & Rossmann (2016)
Richter et al. (2016)
Lu & Gallupe (2016)
Ng (2016)
Ifinedo (2016)
Chang, Shen & Liu (2016)
Martins et al. (2016)
Zhang, Zhao, Lu & Yang (2016)
Coelho & Duarte (2016)
Chan et al. (2016)
Jukic & Merlak (2017)
Althoff et al. (2017)
Chang, Liu & Shen (2017)
Hacker, Bernsmann & Riemer (2017)
Lee & Kim (2017)
Lin, Featherman & Sarker (2017)
Lin, Wu & Kim (2017)
Hsu & Lin (2017)
Lüders & Brandtzæg (2017)
Verduyn et al. (2017)
Ameen, Almari & Isaac (2018)
Giermindl, Strich, & Fiedler (2018)
Salehan, Kim & Koo (2018)
Erfani & Abedin (2018)
Kim et al. (2018)
Dixit & Prakash (2018)
Leong et al. (2018)
Yushi et al. (2018)
Lei et al. (2018)
Kim (2018)
Aboelmaged (2018)
Weerasinghe & Hindagolla (2018)
Huang & shih (2019)
Kim, Lee & Contractor (2019)
Barnes, Pressey & Scornavacca (2019)
Zhou (2019)
Duarte & Coelho (2019)
Noguti, Singh & Waller (2019)
Mohd Suki et al. (2019)
Chin et al. (2019)
Estell & Davidson (2019)
Samad et al. (2019)
Sum87(72.5%)31(25.8%)2(1.7%)
Total120

3Hypotheses development

3.1Perceived usefulness and perceived playfulness

The Theory of Reasoned Action (TRA) is often used in social psychology research that assumes intention as the main predictor of behavior. The theory was derived from previous research on the theory of attitude, which led to the study of attitude and behavior. The theory posits that behavior intentions, as immediate antecedents to behavior, are a function of salient information or beliefs about the likelihood that performing a particular behavior will lead to a specific outcome.

Generally, TRA assumes that: (1) the more favorable the attitude of an individual toward a behavior, the stronger the individual’s intention to engage in such behavior will be; (2) the greater the subjective norm is, the stronger the intention of the individual to perform the behavior will be; and (3) the stronger the intention of the individual to engage in a behavior, the more likely the individual will be to perform it.

TRA has been widely used in social psychology research to explain a variety of people’s behaviors. Davis et al. adapt the theory into their development of the Technology Acceptance Model (TAM)[19]. TAM suggests that two factors — perceived ease of use and perceived usefulness — are two significant determinants of behavioral intention to use computer systems. Meanwhile, this notion omits subjective norm, which is also considered in TRA as a determinant of behavioral intention. TAM considers a determinant of behavioral intention and its validated measurement scales have facilitated research into IT acceptance, therefore, TAM has gained tremendous acceptance within the IS research community [20].

A lot of research effort has been devoted to the factors of the perceived usefulness and the perceived ease of use, while partial study of those factors in TAM cannot help us to thoroughly understand the mechanism of accepting certain technology and how it can be accepted by the end users, especially the process and the essence of this acceptance. Moreover, it seems that the period of TAM introduced coincides with the time when computers are only used for professional technicians rather than widely spread among general users. Thus we could raise a question: are the factors of perceived usefulness and perceived ease of use still valid to test the current technology world? Just as Padilla-Mel e´ Ndez et al. has said, more intrinsic motivators such as playfulness, enjoyment and flow etc. should be taken into account together with the extrinsic motivators of perceived ease of use and perceived usefulness [21].

Although there are several alternative intrinsic motivators, playfulness has been employed as cognitive absorption [22]. Moon and Kim then adopt playfulness to explain behavioral intention to use the World Wide Web (WWW) and name it ‘perceived playfulness’ [23]. They define perceived playfulness as the extent to which the individual perceives that his or her attention is focused on the interaction with the World Wide Web; is curious during the interaction; and finds the interaction intrinsically enjoyable or interesting. As a result, they show that perceived playfulness is an important factor in encouraging more people to use WWW. Lin finds perceived of playfulness as an important determinant for user’s continuance visiting of a web site [24]. Agrifoglio (2012) et al. investigate user continuance of using Twitter [25]. Based on TAM model, they also discover that playfulness would affect user’s intention. Further, Oum and Han study the intention to participate in user-created contents services, in which the effect of perceived playfulness on intention to use is shown to be highly significant, suggesting users’ need in something else: playfulness [26].

Since most of the social network tools emphasize very much on user experience, ease of use is an essential requirement for any SNS product to enter into market. Therefore, in this paper, we select perceived usefulness (PU) as the extrinsic motivator, and perceive playfulness (PP) as an intrinsic motivator for employees to adopt SNSs.

3.2Employees’ ESNs and PSNs’ participation behavior

There are four options for individual users to take actions when using social network services. Here listed our understanding of these definitions:

  • Action to Post (AtP): defines the behavior that an individual uses the social network to post information content.

  • Action to Repost (AtrP): defines the behavior that an individual uses the social network to repost information content. This implies an action of willingness to share the same information of others.

  • Action to Follow (AtF): defines the behavior that an individual uses the social network to retrieve information and read through others’ postings.

  • Action to Comment (AtC): defines the behavior that as individual read others’ posting and takes the action of responding the post with meaningful feedback and suggestion.

As can be seen, the difference among the above four kinds of individual participation of social network is that users contribute their original contents in AtP, whereas they contribute secondary contents in other cases. So we further classify the user’s participation on social network into original participation and secondary participation.

  • Original Participation: means AtP.

  • Secondary Participation: includes AtrP, AtF and AtC.

3.3Conceptual model and hypotheses

The study aims to compare the adoption mechanisms of ESNs and PSNs from companies’ employees. As discussed above, we choose perceived of usefulness and perceived playfulness as two motivators, and select AtP, AtrP, AtF, and AtC as four kinds of employees’ usage behaviors to ESNs and PSNs, in the hope of finding out how the public use context and organizational use context to affect employees’ usage of social network services. Thus, our conceptual research model can be shown in Fig. 1.

Fig.1

Conceptual research model.

Conceptual research model.

3.4PU and PP in the adoption of ESNs and PSNs

Prior studies have widely used the motivation theory to explain individual’s behavior of accepting information technology. Deci et al. divide the motivations of underlying individual’s behavior into extrinsic motivation and intrinsic motivation [27]. Extrinsic motivation refers to the commitment of an action due to its perceived helpfulness in achieving values (e.g. the performance of improvement), while intrinsic motivation refers to the engagement of an action due to an interest in the action itself, rather than external reinforcement [19].

Furthermore, from a psychological perspective, Self-Determination Theory distinguishes between different types of motivation based on the different reasons or goals that give rise to an action. It regards intrinsic motivation as a motivation of doing something because it is inherently interesting or enjoyable. While extrinsic motivation refers to a motivation of doing something because it leads to a separable outcome. Thus, in accordance with most of the adoption models in the previous research, in this study, perceived usefulness is deemed as an extrinsic motivator while perceived playfulness an intrinsic motivator.

Since motivators always have positive influence on a person’s behavior of taking actions, it is believed that when a person uses different social networks, the perceived feelings that this person would largely affect his/her perceived attitudes toward usage could leads to different behavioral actions during his/her use of social networks. Thus, the following hypotheses can be generated:

  • H1: PP is positively related to employees’ participation of ESNs.

  • H1a: PP is positively related to employees’ original participation of ESNs.

  • H1b: PP is positively related to employees’ secondary participation of ESNs.

  • H2: PU is positively related to employees’ participation of ESNs.

  • H2a: PU is positively related to employees’ original participation of ESNs.

  • H2b: PU is positively related to employees’ secondary participation of ESNs.

  • H3: PP is positively related to employees’ participation of PSNs.

  • H3a: PP is positively related to employees’ original participation of PSNs.

  • H3b: PP is positively related to employees’ secondary participation of PSNs.

  • H4: PU is positively related to employees’ participation of PSNs.

  • H4a: PU is positively related to employees’ original participation of PSNs.

  • H4b: PU is positively related to employees’ secondary participation of PSNs.

Generally, motivation is conceptualized as either intrinsic or extrinsic. The relationships between intrinsic motivators and extrinsic motivators may be complex. Deci et al suggest that extrinsic motivation for a task can be changed into intrinsic motivation, and vice versa [27]. But in the IS adoption research area, intrinsic motivators mostly impose positive influence on extrinsic motivators. In a systematic review of TAM (Technology Acceptance Model), Legris et al. generalize that in most cases of the classical TAM model and the later extended models, intrinsic motivators have a significant and positive effect on intrinsic motivators [20]. Faganet al. further prove that there is a positive relationship between intrinsic motivation and extrinsic motivation, and that intrinsic motivation can increase extrinsic motivation perception [28]. Thus, the following hypotheses are developed:

  • H5: PP is positively related to PU in employees’ participation of ESNs.

  • H6: PP is positively related to PU in employees’ participation of PSNs.

3.5The relative influence of PP and PU in ESNs adoption

Despite a growing trend of research concerning the use of micro-blogging services like Twitter, few attempts have been made to investigate micro-blogging in a work environment. Stuart et al. conduct a content analysis and compare the messages of 34 employees posted publicly on Twitter with the ones they posted on an intra-firm micro-blogging platform [29]. They find significant differences in individuals’ use of the two systems. Employees use the company-internal system mostly to engage in “Q&A” and personal, directed interaction, whereas Twitter is mostly used to share information with a larger community. Semi-structured interviews shed more light on the underlying motivation of employees in using micro-blogging. In particular, the interviewees mention the benefits of communication, such as the ability to share information in real-time and to be aware of what colleagues are working on. At the same time, employees see micro-blogging as a way to enhance their reputation.

Davis et al. interview 11 active Twitter users and build theories on interpersonal communication and social psychology to explore potential benefits micro-blogging that can bring to informal communication at work [30]. Their results hint at relational benefits such as building common ground, managing people perceptions, and creating a sense of contentedness, as well as personal benefits such as the acquisition of valuable information.

Similarly, Stuart et al. use an interpretive approach to investigate the case of a corporation use of an in-house social software development that shows close similarities to micro-blogging [29]. They find that employees see micro-blogging as a good information source and an efficient tool to stay aware of activities in the company. On the flipside, their finding suggests that employees have concerns regarding privacy-related aspects such as the monitoring of their work.

Based on the above research, it seems that, in the working context, extrinsic motivator is more importance that intrinsic motivator for employees’ adoption of ESNs, which means the effect of perceived usefulness on employees’ ESNs participation is bigger than that of perceived playfulness. Thus, the following hypotheses are generated.

  • H7: In terms of ESNs, the influence of PU is always higher than that of PP.

  • H7a: In terms of employees’ original participation of ESNs, the influence of PU is higher than that of PP.

  • H7b: In terms of employees’ secondary participation of PSNs, the influence of PU is higher than that of PP.

3.6The relative influence of PP and PU in PSNs adoption

Taking Facebook, Friendster and Myspace as their research background, Sledgianowski and Kulviwat introduce the Social Network Site Adoption model to examine the effect of perceptions of normative pressure, playfulness, critical mass, trust, usefulness, and ease of use on usage intention and actual usage of these sites [17]. They figure out that all the hypothesized determinants have a significant direct effect on intent to use, with perceived playfulness and perceived critical mass the strongest indicators. The intension to use and perceived playfulness have a significant direct effect on actual usage.

Built upon the social capital perspective, Xiao et al. provide a general social networking site experience model regarding the impact of the interaction between user-perceived competence and the user identification on user satisfaction through playfulness in the context of public social networking sites. Their research proves the mediating role of playfulness through which social network site users’ competency and identification factors are transmitted and predict user satisfaction [32].

It seems that, compared with ESNs, in public social platforms, intrinsic motivation is more important than extrinsic motivations. Thus, the following hypotheses are produced:

  • H8: In terms of PSNs, the influence of PP is always higher than that of PU.

  • H8a: In terms of employees’ original participation of PSNs, the influence of PP is higher than that of PU.

  • H8b: In terms of employees’ secondary participation of PSNs, the influence of PP is higher than that of PU.

3.7Public use context versus enterprise use context

ESNs are not simply a Facebook behind a firewall. Every enterprise has its distinct needs and nuances that require a reframing of a social network. The main tasks of ESNs are communication, information dissemination and sharing, management activities and problem solving, training and learning, and collaboration and innovation, etc. [32]. ESNs provide a new platform for informal networks to emerge and function. Studies of IBM’s enterprise social networks have shown that increased use of the tool can lead to increased social capital among both new and existing relationships and workers’ perceived closeness to their co-workers. Other studies have shown that blogging is an effective communication and collaboration tool within large companies to promote collaboration across departments [33, 34]. Hence, the specific functions of ESNs developed by companies are all aiming at improving firms’ operation efficiency. This determines that employees’ perceived usefulness should be a very important factor for companies to design their ESNs.

For PSNs, enlarging individual’s social network is one of the main functions. Zhang and Pentina conduct a study of motivations and usage patterns on Weibo [4]. The authors design a list of motivations with an online survey asking respondents to rate all 40 Weibo related motivations. Afterwards, they identify the most relevant eight factors, namely: 1) professional development; 2) emotional release; 3) information seeking; 4) citizenship behavior; 5) social connection; 6) visibility; 7) self-expression; and 8) interaction with Weibo. As a result, the similar motivations to promote the use of Twitter and Weibo prove to be informational and social. And Weibo users also join the service to facilitate their professional development, fulfill emotional needs, reciprocate by helping other users with advice and information, enhance their social status, express themselves, and interact with the site and other users. More importantly, the authors find that Weibo facilitates both self-expression and interactivity, leading to more frequent contributions and longer stay on the site. Some of the eight factors can be considered as the antecedent variables of perceived playfulness [35].

Further, Turban et al, conduct a content analysis and compare the messages of 34 employees posted publicly on Twitter with the ones they posted on an intra-company micro-blogging platform [32]. They find significant differences in individuals’ use of the two systems. Employees use the company-internal system mostly to engage in “Q&A” and personal, directed interaction, whereas Twitter is mostly used to share information with a larger community.

Via the comparison, it seems that the adoption of employees’ ENSs is more utilitarian oriented, while the employees’ adoption of PSNs is more hedoni-oriented. Thus, we speculate that the role of perceived usefulness is stronger in ESNs than that in PSNs, while perceived playfulness is stronger in PSNs than that in ESNs. Here listed two groups of hypotheses:

  • H9: In terms of employees’ participation, the effect of PU in ESNs is higher than that of PU in PSNs.

  • H9a: In terms of employees’ original participation, the effect of PU in ESNs is higher than that of PU in PSNs.

  • H9b: In terms of employees’ secondary participation, the effect of PU in ESNs is higher than that of PU in PSNs.

  • H10: In terms of employees’ participation, the effect of PP in PSNs is higher than that of PP in ESNs.

  • H10a: In terms of employees’ original participation, the effect of PP in PSNs is higher than that of PP in ESNs.

  • H10b: In terms of employees’ secondary participation, the effect of PP in PSNs is higher than that of PP in ESNs.

4Research setting

4.1Enterprise social network (Happy work)

In 2010, China Mobile Beijing introduced their first Enterprise Social Network (ESN, hereafter) – Happy Work site. It is incorporated with the internal Industry and Information Technology department and maintained by IBM Corporation. The average age of employees in Beijing Mobile is about 28, with 81% of them aged under 30. Due to this reason, the design of Happy Work is oriented toward the most IT popular trend - Social Network Site (SNS) and the easy-of-use notion. Here, the site is based on a group level, instead of an individual level.

The site is aimed to build a newly work environment towards the idea of “Team Building”. It focuses on improving internal employee communication between different groups and departments, between different departments and regions, as well as bottom-top interactions. Thereby employee could experience a new way of corporate culture in a virtual corporate society.

Happy Work site is set on the background of an ancient city with some classical Chinese elements. The content includes different topics such as:

  • General Information: Daily updated news on events or employees within organization;

  • Number 1 Team: A specific section for top management team;

  • Discussion Teahouse: The latest topics and discussions within or outside the company;

  • Blog Gather: A platform where employees (incl. top managers) can share articles, which can either be their own or from external sources, either work-concerned or for life-related.

  • Post Hostel: An online station where employees can share their work and life with their co-workers. (Similar to Twitter tweet/ Weibo post)

  • Voting Bar: A section where teams can design the relevant issues asked across the whole organization in democracy.

  • Hero Board: A board where team / departmental honors are posted.

  • Ask / Answer Farm: An area where questions are frequently asked regarding the site and the system.

  • Sutra Depository: A list of suggested books shared to every employee. Most of times employees are requested to read amount of books in certain period.

  • Lecture Hall: A place where knowledge is shared to every employee in organization.

Furthermore, there are two special areas – Events and Employee Happy House – where employees are currently encouraged to actively participate. Events area is the places where department can post their events and attract qualify employee to join. However the events are normally mid-term or long-term that are held throughout the year, organized by administration. It should be noted that some of the events can be highly related to team performance evaluations. On the other hand, Employee Happy House is designed for promoting employee social life, and each of its department has their own plant in the country farm. There are specific farmers taking care of their vegetables in the plant. When the harvest season comes, employees can order vegetables that grow in their own plant, ask the farm deliver to the company and pick up after work.

4.2Public social network (Sina Weibo)

The banning of Twitter, Facebook, and YouTube in China has created an opportunity for domestic providers such as social networking sites Renren and Kaixin001, micro-blogging service Weibo, and video sharing service like Youkou. One of most successful micro-blogging services is Weibo operated by Sina Corporation – the largest Chinese-language infotainment web portal. Much like Twitter, Sina Weibo enables users to post short (140 characters or less) messages that are displayed on a user’s Weibo page. A user’s Weibo page is open to anyone, where followers (known as fans) can exchange private messages. However, Weibo recently enables the classification of followers for message display and the closure of friends pages.

Weibo involve games, voting, radio, music, and file sharing. It promotes interactivity and engagement by offering Weibo medals for participating in various Weibo activities, tweeting for several consecutive days, re-tweeting brand event announcements, etc. However, unlike members of SNSs, Weibo users are more oriented toward the exposure of strangers, including field experts, stars, and social celebrities, with less attention paid to the messages from friends and acquaintances. The loose social network on Weibo establishes user relationship through rich information flow. Since the launch of Weibo, many SNSs users have joined the platforms and now spend more time on Weibo than on SNSs [4].

Over the past five years, academic researchers have studied the use of social networking whereas most of them are focused on investigating (external) public social networks (PSNs, hereafter), such as Facebook and Twitter. However, few are paying attention to internal use of social networks in the organization, known as enterprise social networks (ESNs). Prior researchers on the utility of social networks have explained that, these social networks can be used for sharing information, following updated news, and directly and indirectly informing and communicating with others [36, 37]. This leads to the first research purpose of understanding the benefits of using social networks in public, and this case concerns with the use of Sina Weibo.

On the other hand, prior researches have proven that enterprise use of social networks differ from that of (external) public social networks [33, 38]. However, studies have barely used a comparative analysis, therefore our research purpose is to investigate what differ in motivations behind of people use public social networks (e.g. Sina Weibo) and enterprise social networks (e.g. Blue Twit).

5Research methodology

5.1Measurement

5.1.1Independent variables—perceived usefulness and perceived playfulness

Perceived Usefulness. Davis et al. define the perceived usefulness as a “perception that using system leads to enhanced personal performance”[30]. Later, in UTAUT model [39], the perceived usefulness has been further redefined as “perception that using system will help user attain gains in job performance”. Our research includes the study of two social network sites where we aim to discover which motivation could affect on users’ attitudes of system use. Thus, we further define our understanding of the perceived usefulness as “the degree to which a person believes that using particular social network helps user enhance relevant job/learning/life performance”. Referring to the four commonly used PU measurement items [20], we develop our measurement of PU for PSNs and ESNs respectively, as shown in the Appendix.

Perceived Playfulness. Computer playfulness has been defined as “the degree of cognitive spontaneity in microcomputer interactions”. Here, for SNSs use, we define perceived playfulness as the degree of cognitive spontaneity in SNS interactions. In Moon and Kim’s study, three dimensions of perceived playfulness are described as: concentration (the extent to which a user perceives that his or her attention is focused), curiosity (the extent to which the user is inquisitive about the interaction), and enjoyment (the extent to which the user finds the interaction fun or interesting) [23]. In this study, we also classify perceived playfulness into three dimensions: the extent to which the individual 1) perceives that his or her attention is focused on the interaction with the SNSs; 2) is curious during the interaction; and 3) finds the interaction intrinsically enjoyable or interesting. Referring to the measurement of perceived playfulness (PP) provided by Moon and Kim, the measurement items of for PSNs and ESNs are generated respectively, as shown in the Appendix.

5.1.2Dependent variables—employees’ SNSs participation

To measure employees’ SNSs participation, we have asked employees in the survey to self-report their use frequency of PSNs and ESNs, as shown in the Appendix 3.

5.2Survey design and sampling

Our research is aimed to investigate the motivation behind each user’s behaviors, therefore, we follow the approaches of most relevant studies and conducted a survey among staff users in Beijing Mobile. We design sampling quota by gender, age, and occupation in accordance with the demographics of this target population. We measure perceived attitudes and participation behaviors on a five point Likert scale (1 = strongly disagree, while 5 = strongly agree). All items measurement are mixed up and re-categorized into different topics of questions. The research instrument is developed on the basis of prior studies in the west. For the convenience of our Chinese sample group, the English questionnaire is translated into Chinese. We conducted 15 in-depth interviews with random samples in China Mobile to ensure the face validity of the measures. For details, please see the Appendix 3.

Invitation to participate for online survey is spread through Labor Union within Beijing Mobile, and posted on internal Survey System. The Labor Union post an announcement to ask each department to fill out the survey (the quota sampling is requested by the research team beforehand). The duration is about one month. After that, the Labor Union collects the survey results and sends them to the research team.

5.3Construct reliability and validity

5.3.1Construct reliability

To test the reliability of these variables, we adopt confirmatory factor analysis (CFA) to assess the scale properties of the measurement model. The CFA results indicate that the four measures about perceived playfulness can be merged into a single one, with the Cronbach’s Alphas coefficient (0.841 in ESNs and 0.817 in PSNs, see Table 2-a) being sufficiently high to allow further analysis. Similarly, the measures of perceived usefulness, action to post, action to repost, action to follow and action to comment converge can all be merged into a single factor with Cronbach’s Alpha values of 0.7, 0.92, and 0.84, respectively (see Table 2-b). In addition, average variance extracted (AVE) values for the constructs also exceed the cut-off point of 0.5, indicating that the constructs have captured a sufficiently high level of variance [40].

Table 2-a

Reliability, means and standard deviations of research variables in ESNs

VariableCAAVE
Perceived playfulness0.8410.762
Perceived usefulness0.9470.811
Action to post0.8570.778
Action to repost0.9050.842
Action to follow0.9370.801
Action to comment0.9150.798

Note: CA = Cronbach’s alpha; AVE = average variance extracted.

Table 2-b

Reliability, means and standard deviations of research variables in PSNs

VariableCAAVE
Perceived playfulness0.8170.737
Perceived usefulness0.8830.701
Action to post0.8550.779
Action to repost0.9310.879
Action to follow0.9340.795
Action to comment0.8990.768

Note: CA = Cronbach’s alpha; AVE = average variance extracted.

5.3.2Convergent and discriminant validity

We assess convergent validity by examining the factor loadings through the exploratory factor analysis. The criteria for an acceptable level of convergent validity are: 1) individual item loadings greater than 0.5, and 2) cumulative variance contribution greater than 40%. The results of all item loadings are reported in Table 3-a, which support the dimensionality of the constructs. One additional guideline for discriminant validity is that the square root of AVE for each construct should be greater than the correlation values of the construct with other constructs [40]. As reported in Table 3-b, all constructs across the samples meet with the guideline. Therefore, the discriminant validity criterion is also satisfied.

Table 3-a

Factor loading analysis for the variables in ESNs

VariableItemsFactor loadingOverall explanation degree
Perceived playfulnessPP1_a0.80976.176%
PP2_a0.905
PP3_a0.901
Perceived usefulnessPU1_a0.91482.313%
PU2_a0.930
PU3_a0.926
PU4_a0.913
PU5_a0.851
Action to postAtP1_a0.88377.846%
AtP2_a0.925
AtP3_a0.840
Action to repostAtRp1_a0.87884.174%
AtRp2_a0.931
AtRp3_a0.944
Action to followAtF1_a0.86580.085%
AtF2_a0.895
AtF3_a0.906
AtF4_a0.922
AtF5_a0.888
Action to commentAtC1_a0.86779.788%
AtC2_a0.912
AtC3_a0.927
AtC4_a0.865
Table 3-b

Factor loading analysis for the variables in PSNs

VariableItemsFactor loadingOverall explanation degree
Perceived playfulnessPP1_b0.79773.655%
PP2_b0.914
PP3_b0.861
Perceived usefulnessPU1_b0.81370.232%
PU2_b0.838
PU3_b0.865
PU4_b0.874
PU5_b0.794
Action to postAtP1_b0.87677.579%
AtP2_b0.919
AtP3_b0.852
Action to repostAtRp1_b0.91687.883%
AtRp2_b0.941
AtRp3_b0.956
Action to followAtF1_b0.86379.476%
AtF2_b0.914
AtF3_b0.873
AtF4_b0.935
AtF5_b0.871
Action to commentAtC1_b0.87476.821%
AtC2_b0.897
AtC3_b0.895
AtC4_b0.837

5.4Common method bias

Self-reported data collection and logical constructs of items lead naturally to variance in the measurement method [41]. Whereas this variance is commonly acknowledged, very few papers actually address it, despite the popularity of self-report survey method [42].

Given the measurement of perceived playfulness, perceived usefulness, action to post, action to repost, action to follow and action to comment with information gathered from the same respondents, the issue of common method variance must be addressed. We conduct a Harmon single-factor test, which reveal that the common method variance unlikely to be a concern. Table 4 shows the results of the adjustments, with zero-order correlations below the diagonal and adjusted correlations above it. Our results do not show a substantial bias due to common method variance. All relevant correlations remain significant after the correction.

Table 4-a

Results of discriminant validity and construction of correlations in ESNs

123456
Perceived playfulness0.820.57*0.17*0.38*0.42*0.12*
Perceived usefulness0.57*0.990.20*0.40*0.63*0.15
Action to post0.17*0.17*0.940.29*0.160.09*
Action to repost0.41*0.41*0.35*0.920.35*0.13
Action to follow0.45*0.68*0.160.31*0.890.10*
Action to comment0.09*0.140.15*0.100.10*0.90
Table 4-b

Results of discriminant validity and construction of correlations in PSNs

123456
Perceived playfulness0.880.56*0.17*0.38*0.44*0.39*
Perceived usefulness0.56*0.990.20*0.40*0.63*0.34
Action to post0.17*0.17*0.930.29*0.160.33*
Action to repost0.43*0.41*0.35*0.940.35*0.32
Action to follow0.42*0.68*0.160.31*0.950.31*
Action to comment0.37*0.350.33*0.300.30*0.89

Note: Square roots of AVEs are presented on the diagonal. Construct correlations are below the diagonal. Construct correlations corrected for common method bias are above the diagonal. *Marks the significance levels. In summary, the measures of the proposed constructs achieve high reliability as well as convergent and discriminant validity.

6Empirical results

6.1Assessment of model fit

To test the model’s effectiveness, we apply it to the ESNs context and the PSNs context, respectively. Tests are performed using the AMOS 17.0 software. For the chi-square is very sensitive to the sample size, item numbers and factor numbers in the model, as well as other fit indices, such as goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), root mean square of approximation (RMSEA), and Comparative Fit Index (CFI) are used to assess overall model fit. These results are reported in Table 5. In general, all these results suggest that our model fits the data well (See Table 5).

Table 5

Evaluation indicators of the model

IndicatorsReference valueReferencesAtPAtrPAtFAtC
GFI>0.9Hooper et al.0.9430.9580.9340.952
AGFI>0.90.8040.9280.9000.923
CFI>0.90.9730.9830.9730.980
RMSEA<0.080.0560.0450.0520.045

6.2Hypotheses testing

We use structural equations to analyze the relations between perceived playfulness, perceived usefulness and their impacts on employees’ participation, according to the theoretical conceptual model in Fig. 1. The AMOS 17.0 software is used.

Table 6 presents the analysis of the relationships among different variables in the conceptual models. The three models are quite consistent: all the relations are significant (p < .001), except for those between perceived usefulness and employees’ secondary participation of PSNs.

Table 6

Correlation analysis of the variables in the conceptual model

HypothesisRelations between variablesCorrelationP valueSignificance
ESNSPSNS
H1a/H3aPP and AtPPositive******Significant
H2a/H4aPU and AtPPositive******Significant
H1b/H3bPP and AtrPPositive******Significant
PP and AtFPositive******Significant
PP and AtCPositive******Significant
H2b/H4bPU and AtrPPositive.886.092Not significant
PU and AtFPositive.644.866Not significant
PU and AtCPositive.315***Not significant
H5/H6PP and PUPositive******Significant

Based on this observation, we modify the model as shown in Fig. 2.

Fig.2

Modified Conceptual Research Model.

Modified Conceptual Research Model.

After the modified model in Fig. 2 is analyzed, we present the path coefficients in the structural equation models in Table 7.

Table 7

Path coefficient for the two contexts

ParticipationHypothesisPathCorrelationESNs ModelPSNs ModelSignificance
Original participationH1a/H3aPP⟶ AtPPositive.372.443Significant
H2a/H4aPU⟶ AtPPositive.432.329Significant
Secondary participationH1b/H3bPP⟶ AtrPPositive.572.621Significant
PP⟶ AtFPositive.754.687Significant
PP⟶ AtCPositive.497.506Significant
H2b/H4bPU⟶ AtrPPositive–.020.081Not significant
PU⟶ AtFPositive–.059.008Not significant
PU⟶ AtCPositive.133.230Not significant
H5/H6PU⟶ PPPositive.910.525Significant

6.2.1Perceived playfulness and perceived usefulness is positively related to employees’ participation of ESNs (H1, H2)

On the one hand, the path coefficients in ESNS model—PP to AtP:.372, PP to AtrP:.572, PP to AtF:.754, PP to AtC:.497 (see the third row in Table 7)—show that the relationship between perceived playfulness and employees’ original participation and secondary participation is significantly positive. On the other hand, the data analysis of the path coefficients in ESNs model—PU to AtP:.432, PU to AtrP: –.020, PU to AtF: –.059, PU to AtC:.133 (see the third row in Table 7)—shows that the relationship between perceived usefulness and employees’ original participation is significantly positive, but the relationship between perceived usefulness and employees’ secondary participation is slightly (but not significantly) negative. Therefore, we can conclude that in ESNs, perceived playfulness is positively related to employees’ participation, while perceived usefulness is only positively related to employees’ original participation, without significant correlation to employees’ secondary participation.

6.2.2Perceived playfulness and perceived usefulness is positively related to employees’ participation of PSNs (H3, H4)

On the one hand, the path coefficients in PSNs model—PP to AtP:.443, PP to AtrP:.621, PP to AtF:.687, PP to AtC:.506 (see the fourth row in Table 7)—show that the relationship between perceived usefulness and employees’ original participation and secondary participation is significantly positive. On the other hand, the data analysis of the path coefficients in PSNs model—PU to AtP:.329, PU to AtrP:.081, PU to AtF:.008, PU to AtC:.230 (see the fourth row in Table 7)—shows that the relationship between perceived usefulness and employees’ original participation is significantly positive, but the relationship between perceived usefulness and employees’ secondary participation is slightly (but not significantly) positive. Therefore, we can conclude that in PSNs, perceived playfulness is positively related to employees’ participation, while perceived usefulness is only positively related to employees’ original participation, without significant correlation to to employees’ secondary participation.

6.2.3Perceived playfulness is positively related to perceived usefulness in employees’ participation of PSNs (H5, H6)

The path coefficients of the relationship from perceived usefulness to perceived playfulness in ESNs model:.910, PSNs model:.525, General model:.706 (see the last line in Table 7)—show that the relationship between perceived usefulness to perceived playfulness is significantly positive. It is supported that perceived playfulness is positively related to perceived usefulness in employees’ participation of ESNs and PSNs. H5 and H6 are confirmed.

6.2.4In terms of ESNs, the influence of PU is always higher than that of PP. (H7)

In ESNs model, on the one hand, the path coefficients of original participation—PP to AtP:.372, PU to AtP:.432 (see the third row in Table 7)—show that in terms of employees’ original participation, the influence of perceived usefulness is higher than that of perceived playfulness. On the other hand, the data analysis of the path coefficients of secondary participation—PP to AtrP:.572, PU to AtrP: –.020 (not significant); PP to AtF:.754, PU to AtF: –.059 (not significant); PP to AtC:.497, PU to AtC:.133 (not significant) (see the third row in Table 7)—shows that the influence of perceived usefulness is not higher than that of perceived playfulness. Therefore, it can be concluded that in terms of ESNs, the influence of PU is not always higher than that of PP. H7.

6.2.5In terms of PSNs, the influence of PP is always higher than that of PU. (H8)

In PSNs model, on the one hand, the path coefficients of original participation—PP to AtP:.443, PU to AtP:.329 (see the fourth row in Table 7)—show that the influence of perceived playfulness is higher than that of perceived usefulness. On the other hand, the data analysis of the path coefficients of secondary participation—PP to AtrP:.621, PU to AtrP:.081 (not significant); PP to AtF:.687, PU to AtF:.008 (not significant); PP to AtC:.506, PU to AtC:.230 (see the fourth row in Table 7)—shows that the influence of perceived playfulness is higher than that of perceived usefulness. Therefore, it can be concluded that in terms of PSNS, the influence of PP is confirmed to be always higher than that of PU. H8.

6.2.6The difference of the effect of perceived attitude between ESNs and PSNs. (H9, H10)

To test the moderating effect of use context (i.e. the H9 and H10), we establish a multi-group structure equation model (Multi-Group SEM). The objective of multi-group simultaneous path analysis is to determine whether the path coefficients between perceived attitudes and the employees’ participations are equal across different use contexts.

For H9 and H10, the sample is divided into two subgroups according to the context. We first constrain the paths to be invariant across the two contexts and then freely estimate these paths. If the chi-square change between the above constrained and unconstrained multi-group SEM is statistically significant, it means the path loadings in different industries are significantly changed. That is, context type is a significant moderator to the relationships between perceived attitudes and employees’ participations.

For the unconstrained multi-group analysis, we suppose that the corresponding path coefficients from perceived attitudes to employees’ participations in the two sample models (the ESNs model and the PSNs model) are statistically equivalent. Then we do a multi-group invariance test using the software AMOS 17.0. Table 8 shows that ▵χ= 69.603, 67.330, 66.388, 62.987, which is non-significant at the level of p < .05. That is to say, the study does not pass the multi-group invariance test. In other words, there are significant difference between the ESNs model and the PSNs model.

Table 8

Multi-group invariance test (assuming model unconstrained to be correct)

ModeldfCMINPNFI Delta-1IFI Delta-2RFI rho-1TLI rho-2
Measurement weightsAtP869.603.000.008.008.003.003
AtrP867.330.000.007.007.006.006
AtF1066.388.000.006.006.003.003
AtC962.987.000.006.006.005.005

Table 9 shows the results of Multi-group SEM analysis. As can be seen, for H9a, the chi-square change (×2 with 4df) is 19.114, which is significant at the.05 level, so H9a is supported. That is, the relationship between perceived usefulness and employees’ original participation differs between ESNs and PSNs. H9b is tested using the same method. We divide the data into two subgroups, Chi-square change (×2 with 6 df) of AtrP is 38.706, Chi-square change (×2 with 7 df) of AtF is 21.362, and Chi-square change (×2 with 6 df) of AtC is 18.902, which are significant at.05 level, so H9b is supported. Other hypothesis, H10a and H10b are tested by using the same method. Here, H10a is supported where the Chi-square change (×2 with 4 df) is 19.114 and the P-value is.001 which is significant at.05 level; H10b is supported where the Chi-square change (×2 with 4 df) of AtrP is 13.485, Chi-square change (×2 with 6 df) of AtF is 13.485, Chi-square change (×2 with 5 df) of AtC is 13.485, and the P-value is.009,.016 and.020, respectively. Therefore, all the relationships between perceived playfulness and employees’ participation differ between ESNs and PSNs, which means use context has a significantly moderating effect in this study.

Table 9

The results for multi-group SEM

HypothesisPathSignificance
χ2/dfGFICFIRMSEAdfχ2PSignificance
H9aPUAtP5.541.953.976.069634.957.000Significant
H9bPUAtrP3.910.968.986.055638.706.000Significant
PUAtF4.962.936.972.064721.362.003Significant
PUAtC3.658.959.982.053618.902.004Significant
H10aPPAtP6.256.968.973.074419.114.001Significant
H10bPPAtrP61.489.979.988.054413.485.009Significant
PPAtF4.993.952.974.065615.643.016Significant
PPAtC3.555.974.984.052513.328.020Significant
Table 10

Hypothesis test results

Hypothetical ContentResults
H1: PP is positively related to employees’ participation of ESNs.Support
H2: PU is positively related to employees’ participation of ESNs.Support
H3: PP is positively related to employees’ participation of PSNs.Support
H4: PU is positively related to employees’ participation of PSNs.Partly support
H5: PP is positively related to PU in employees’ participation of ESNs.Support
H6: PP is positively related to PU in employees’ participation of PSNs.Support
H7: In terms of ESNs, the influence of PU is always higher than that of PP.Partly support
H8: In terms of PSNs, the influence of PP is always higher than that of PU.Partly support
H9: In terms of employees’ participation, the effect of PU in ESNs is higher than that of PU in PSNs.Support
H10: In terms of employees’ participation, the effect of PP in PSNs is higher than that of PP in ESNs.Support

6.2.6.1. In terms of employees’ participation, the effect of PU in ESNs is higher than that of PU in PSNs. (H9). On the one hand, in terms of the effect of perceived usefulness on employees’ original participation, the path coefficients —ESNS model:.432, PSNs model:.329 (see the third line in Table 7)—show that the effect of perceived usefulness in ESNs is higher than that in PSNs. On the other hand, in terms of the effect of perceived usefulness on employees’ secondary participation, the data analysis of the path coefficients are significant in all (see the seventh-ninth lines in Table 7)—showing that it is unable to compare the effect of PU in ESNS and that in PSNS. Therefore, it can be concluded that in terms of employees’ original participation, the effect of PU in ESNs is higher than that of PU in PSNs. H9 is therefore supported.

6.2.6.2. In terms of employees’ participation, the effect of PP in PSNs is higher than that of PP in ESNs. (H10). On the one hand, in terms of the effect of perceived playfulness on employees’ original participation, the path coefficients —ESNS model:.327, PSNs model:.443 (see the second line in Table 7)—show that the effect of perceived playfulness in PSNs is higher than that in ESNs. On the other hand, in terms of the effect of perceived playfulness on employees’ secondary participation, the data analysis of the path coefficients - ESNS model:.572 (AtrP), 754 (AtF), 497 (AtC); PSNs model:.621(AtrP),.687(AtF),.506(AtC) (see the fourth-sixth lines in Table 7)—shows that the effect of perceived playfulness in PSNs is higher than that in ESNs except for the action to follow. Therefore, it can be concluded that in terms of employees’ original participation, the effect of PP in PSNs is higher than that of PP in ESNS. H10 is partly confirmed.

7General discussion and conclusion

7.1Theoretical implications

In the introduction section, we raise the question that we want to address in our research: “Is the adoption mechanism of ESNs the same as that of PSNs?” After the entire data analysis, we are now in a position to answer it. The hypothesis test results are present in the Table 10.

The answer is partly yes and partly no. As for the general adoption mechanisms, PSNs and ESNs are almost the same. However, as for the relative roles of PP and PU in PSNs and ESNs, there are obvious differences. These similarities and differences construct the main theoretical implications of this study.

Firstly, our study discovers that the general adoption mechanisms of PSNs and ESNs for both employees’ original participation and secondary participation are nearly the same: the original participation is a both utilitarian-oriented and recreational-oriented adoption, while the secondary SNS participation is a recreational-oriented adoption. Our study shows that both perceived usefulness and perceived playfulness are all the factors to influence employees’ original PSNs and ESNs participation. However, for employees’ secondary PSNs and ESNs participation, only perceived of playfulness is the influencing factor. According to the classification, we can deduce that employees’ original participation for PSNs and ESNs are dual-purpose (utilitarian-oriented and recreational-oriented) IT behavior, while the usefully perceived of playfulness and the uselessly perceived usefulness in the employees’ secondary participation of PSNs and ESNs indicate that the secondary SNS participation is a recreational-oriented IT behavior. Individual’s original SNS participation is dual-purpose and secondary SNS participation is recreational-oriented. This conclusion is suitable for both PSNs and ESNs. This is a very unique research finding of our study. Previously, people take it for granted that PSNs is recreational-oriented until Xu et al. find that utilitarian gratifications and hedonic gratifications together become the important factors for individual’s PSNs usage [43]. However during their work, user’s different SNS activities are dealt with as a whole although they measure the general SNS usage from posting, viewing, sharing, replying and playing etc. Therefore, we can see that our work deepens Xu et al. ’s study, and further detects which PSNs activities are utilitarian-oriented and which ones are recreational-oriented. As for ESNs, people are also likely to simply think that ESNs is a utilitarian-oriented adoption. At this point, our study firstly proves that the adoption of ESNs is also both utilitarian-oriented and recreational-oriented [44–46].

Secondly, although the general adoption mechanism of PSNs and ESNs is the same, the relative influences of PP and PU in the adoption of PSNs and ESNs are different. Our study finds that the influence of PP in PSNs is bigger than that of PU in PSNs, indicating that although the adoption of PSNs is found to be dual-purpose, it actually prefers recreational-oriented adoption to utilitarian-oriented adoption. As for ESNs, our study indicates that for individual’s original ESNs participation, the influence of PU is higher than that of PP, while the direction of the relationship is opposite to individual’s secondary ESNs participation. The results for ESNs mean that although individual’s first original participation is dual-purpose, it is more inclined to be utilitarian-oriented. To generalize, our findings show that the individual’s adoption of PSNs is more inclined to be recreational-oriented in individual’s original participation while individual’s adoption of ESNs is more inclined to be utilitarian-oriented, although both of them are proved to be dual-purpose. If our research results about the similar adoption mechanism between PSNs and ESNs are not in conformity with people’s common experience, here, the different roles of PP and PU in the adoption processes of PSNs and ESNs are in line with people’s common experience [47–49].

Thirdly, although PP and PU are both the influencing factors for individual’s ESNs and PSNs original participation, and as PP is the only influencing factor for individual’s ESNs and PSNs secondary participation, the comparative power is different. The role of PU in the original participation of ESNs is stronger than PU in that of PSNs, and the role of PP in PSNs is stronger than PP in that of ESNs for both original and almost all secondary participations. This means that PP is the most important factor affecting the behavior of individual’s PSNs adoption. This conclusion is supported by other related work. For example, the study of Ref. [50] shows that enjoyment is the most influential factor in people’s continued use of PSNs. Lin et al. find that appraisal factors (pleasure, awareness, connectedness, and system quality) are strong determinants of emotional reaction on PSNs continuance intention. Compared to PSNs, PU has higher influence in ESNs adoption [51].

7.2Managerial implications

Our research has implications for companies’ ESNs development. First, ESNs are not just for work. They are also for fun. Although most people resist that ESNs can bring values to companies, there are also some difference voices concerning the time wasted, juggling multiple personas, personal views affecting career progression and crossing organizational hierarchy, etc. of ESNs. Our research gives clues of disagreement of these voices. Our study shows that PP has positive relationship with PU, and employees’ adoption of ESNs is dual-purpose for original participation and recreational-oriented for secondary participation. This indicates when designing an internal social network, companies can take these both PU and PP into account, especially on system playfulness. From this implication, we believe the design of Happy Work Site in China Mobile has indeed provided a good example. Secondly, different employees’ ESNs participations have different roles. Companies should make some intensive policies to encourage all kinds of employees’ ESNs participation. The more utilitarian-oriented original employees’ participation may lead to higher working productivity, while the more recreational-oriented secondary employees’ participation may increase the connections between employees, and may reduce the employees’ turnover. Thirdly, companies should also carefully manage ESNs to balance the utilitarian function and recreation function to harvest their best of benefits. Although we pretest the forbidding opinions on ESNs, and also support that PP is much important for employees’ adoption of ESNs, we still suggest companies to balance the utilitarian functions and recreation functions of ESNs since too many recreation functions will let ESNs not ESNs, but more and more like PSNs, and make companies aim of ESNs further away.

Our research also has implications for PSNs development. First, PSNs are not just for fun, especially for people’s original participation. In particular at this moment, PSNs have developed to very high level, so we suggest the providers of PSNs strengthen the utilitarian functions of their platforms to attract more and more users to generate contents. Second, PSNs are mostly for fun. When the providers want to add more and more utilitarian functions to PSNs, they should never forget that the biggest value of PSNs for users is to enjoy happiness. Too many utilitarian functions to PSNs maybe make the platforms lose fun, and drive users away. Third, the providers of PSNs should also balance the utilitarian function and the recreation function to get the largest number of users, and enhance the activities of users. The providers of PSNs should give more weight to recreation functions than utilitarian functions, and make utilitarian functions full of fun if possible.

7.3Limitations and future research directions

Although our study provides meaningful implications to both enterprises and public social networks, it has a few inherent limitations.

Our study is a first empirical study on the comparison of ESNs and PSNs adoption mechanism based on the data from China Mobile. Concerning the characteristics of China Mobile, a state-owned big telecommunication operator, despite of the strong support given by the data to our empirical study, they also bring limitations to our study. Our conclusion may not be suitable for other companies different from China Mobile, such as the ones in West cultures, the ones with small size or middle size, the ones not in IT industry, etc.

Our study just uses micro-blogging services to be one example of SNS. Both Happy Work and Weibo are micro-blogging services. We do not use other SNS formats, such as Blogging service, Forum etc. to test our research results. Thus, we cannot make sure whether the research conclusions are still fit in other SNS formats.

In summary, future research is suggested to investigate the difference of using social networks between Eastern and Western countries, between big companies and small or middle size companies, between IT industry and non IT industries.

Finally, considering the purpose and functionality of establishing ESNs, it is suggested that later scholars can combine both organizational behaviors and leadership theories for developing a relevant research model to investigate how ESNs can be adopted by different companies.

Appendices

Appendix

Measurement items

ESNs

ConstructsItemsItem wording
Perceived playfulnessPP1_aWhen using Happy Work Site, I do not realize the elapse of time.
PP2_aI feel the contents of Happy Work Site are very attractive to me.
PP3_aHappy Work Site provides me with a lot of enjoyment.
Perceived usefulnessPU1_aHappy Work Site enables me to accomplish my work more quickly.
PU2_aHappy Work Site would enhance my work effectiveness.
PU3_aHappy Work Site enables me to solve my life troubles quickly.
PU4_aHappy Work Site would enhance my life effectiveness.
PU5_aOverall, I think Happy Work Site brings benefits to our work.
Action to postAtP1_aI express my thought on Happy Work Site.
AtP2_aI show my opinion to other colleagues on Happy Work Site.
AtP3_aI share my knowledge and information to other colleagues on Happy Work Site.
Action to repostAtRp1_aWhen I read and agree with a post, I repost to other colleagues on Happy Work Site.
AtRp2_aWhen I see useful information (work related) and am willing to share with other colleagues on Happy Work Site, I repost.
AtRp3_aWhen I see useful information (life related) and am willing to share with other colleagues on Happy Work Site, I repost.
Action to followAtF1_aI keep myself updated about company information through the Happy Work Site.
AtF2_aI check the postings of my team and department on Happy Work Site.
AtF3_aI check the postings of other teams and departments on Happy Work Site.
AtF4_aI follow the information related to my life on Happy Work Site.
AtF5_aI follow the information related to my work on Happy Work Site.
Action to commentAtC1_aI take an active part in the discussion of hot topics on Happy Work Site.
AtC2_aI comment on my team and colleagues’ postings.
AtC3_aI comment on other team and colleagues’ postings.
AtC4_aI provide useful content on Happy Work Site.

a All items measured on a five-scale ranging from 1 = “strongly disagree” to 5 = “strongly agree”.

PSNs

ConstructsItemsItem wording
Perceived playfulnessPP1_bWhen using Weibo, I do not realize the elapse of time.
PP2_bI feel the contents of Weibo are very attractive to me.
PP3_bWeibo provides me with a lot of enjoyment.
Perceived usefulnessPU1_bWeibo enables me to accomplish my work more quickly.
PU2_bWeibo would enhance my work effectiveness.
PU3_bWeibo enables me to solve my life troubles quickly.
PU4_bWeibo would enhance my life effectiveness.
PU5_bOverall, I think Weibo brings benefits to our work.
Action to postAtP1_bI express my thought on Weibo public.
AtP2_bI show my opinion to others on Weibo.
AtP3_bI share my knowledge and information to others on Weibo.
Action to repostAtRp1_bWhen I read and agree with a post, I repost to other colleagues on Weibo.
AtRp2_bWhen I see useful information (work related) and am willing to share with other colleagues on Weibo, I repost.
AtRp3_bWhen I see useful information (life related) and am willing to share with other colleagues on Weibo, I repost.
Action to followAtF1_bI keep myself updated about social information through Weibo.
AtF2_bI check the postings of my friends on Weibo.
AtF3_bI check the postings of others on Weibo.
AtF4_bI follow the information related to my life on Weibo.
AtF5_bI follow the information related to my work on Weibo,
Action to commentAtC1_bI take an active part in the discussion of hot topics on Weibo.
AtC2_bI comment on my following group (people, celebrities or company)’s postings on Weibo.
AtC3_bI comment on other (not in my following group) people/events posted on Weibo.
AtC4_bI provide useful content on Weibo.

a All items measured on a five-scale ranging from 1 = “strongly disagree” to 5 = “strongly agree”.

Acknowledgment

This work is jointly supported by NSFC project (72042004), (71601005), (71231002) and Joint project of MOE-China Mobile (MCM20123021).

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