Inf Syst Front DOI 10.1007/s10796-013-9473-2
The role of social media in supporting knowledge integration: A social capital analysis Xiongfei Cao & Xitong Guo & Hefu Liu & Jibao Gu
# Springer Science+Business Media New York 2013
Abstract Internet of things (IoT) is a current trend that reveals the next generation Internet-based information architecture, the convergence of social networks and IoT solutions is helpful to optimize relationships among objects. In order for IoT to take off in the IT sector, providers and other stakeholders must integrate knowledge successfully. In this study, we investigate the role of social media in supporting knowledge integration from a social capital perspective. Specifically, we propose that social media have the potential to facilitate the formation of employees’ social capital indicated by social networking, trust and shared language. These mediating variables will in turn positively affect knowledge integration. This research frame is validated with survey data collected from 262 Chinese working professionals. The results provide general empirical support for our hypotheses. In analogy with social media for human beings, the future direction of socialization among objects can be inspired by this study. Keywords Social media . Internet of things (IoT) . Social Internet of things (SIoT) . Social capital . Knowledge integration
1 Introduction Internet of things (IoT) is a current trend that reveals the next generation Internet-based information architecture, which can be used to facilitate information flow in supply chain network X. Cao : H. Liu : J. Gu University of Science and Technology of China; City University of HongKong, Hefei 230026, China X. Guo (*) Harbin Institute of Technology, The Hong Kong Polytechnic University, Harbin 150001, China e-mail:
[email protected]
(Fang, et al. 2013; Xu 2011). Based on the notion of social relationships among objects, recently a new paradigm Social Internet of things (SIoT) is proposed to describe a world where things can be intelligently sensed and networked. The concept of SIoT is motivated by the popular social networks over the Internet (e.g., Facebook and Twitter) and most of SIoT characteristics are similar to those observed in social networks of humans (Atzori et al. 2012). Drawing on the success of social media and applying the social networking principles to the IoT are helpful to optimize relationships among objects. However, as new concepts, many of the feathers, benefits, and challenges of IoT and SIoT are not well understood. In order for IoT and SIoT to take off in the IT sector, providers and other stakeholders must integrate knowledge successfully. The ubiquity of social media has resulted in extensive applications, with increasing number of companies incorporating social media inside their organizations to support communication and collaboration. According to the survey on the way organizations use social media undertaken by McKinsey global institute (2011), companies are enhancing their mastery of social media, utilizing them to improve operations and exploit new market opportunities. Meanwhile, academic research has provided some exploratory insights into social media’s influence on knowledge management (Fung and Hung 2013). Despite growing interests, the impact of social media on knowledge integration has been overlooked. In addition, the lack of conceptualization of social media as informal knowledge management systems reflects a literature gap. Knowledge management systems (KMS) are information technology (IT)-based systems developed to support organizational knowledge management processes (Alavi and Leidner 2001). The majority of KMS are designed for capture and retrieval of formal/explicit knowledge, ignoring the fact that knowledge management initiatives are increasingly directed towards informal activities. When sharing and transferring implicit knowledge, people tend to use personal social
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networks rather than electronic systems, since human beings are both the carrier and medium through which the knowledge is delivered (Huysman and Wulf 2005). Further, IT alone is not the main driver for knowledge management initiatives. The way people use it is what shapes the role of IT in supporting knowledge management (Law and Chang 2008). In virtual environments, forcing people to codify their knowledge is difficult. But when people are socializing, they are more likely to share their experiences and expertise, even in the workplace (Levin and Cross 2004). There exists evidence that knowledge management is inherently a social process where social capital plays a vital role (Wasko and Faraj 2005); however, KMS that cultivate the accumulation of social capital are scarce. Given the above situation, this research regards social media as informal KMS, and thus we investigate their influence on knowledge integration in the workplace. Two models of KMS have been recognized in information system studies: the repository model and network model (Alavi and Leidner 2001). The repository model of KMS, such as knowledge discovery system and enterprise search engine, emphasizes the codification and storage of knowledge to promote explicit knowledge reuse (Ganley and Lampe 2009). On the other hand, the network model of KMS is equivalent to the personalization approach to knowledge management, focusing primarily on the linkage among people for the purpose of knowledge exchange (Kankanhalli et al. 2005). Such KMS (e.g., electronic forum that enables users to interact within communities of practice) is less focused on mapping knowledge than it is on bringing the experts together so that knowledge is integrated and amplified (Alavi and Leidner 2001). Researchers and practitioners are increasingly aware of the significance of personal knowledge management. In 2005, Tsui proposed three development tendencies of knowledge management technologies in the next 5 years: 1) alignment of KM technologies with business process management (BPM) tools, 2) the emergence of personal networks, and 3) knowledge management becoming more and more “on-demand” with agile and adaptive IT tools playing more important roles. We believe that social media are representative of the latter two tendencies. The extensive availability, low cost, as well as the ease of use of social media have made KMS - once available only when hardwired into the company’s network - accessible from anywhere: “Recently, a new wave of smaller, lighter, and less expensive tools has started to go where the larger KMS often don’t, bringing corporate knowledge back out into daylight” (Spanbauer 2006). We argue that social media, functioning as social networking tools as well as informal KMS, reflects the relational, collaborative nature required by knowledge integration. In this context, the relationships among actors within a social media network become critical to achieving joint knowledge integration success. We propose to link social
media use and knowledge integration from the perspective of ongoing social interactions. Using this perspective, social capital—the resources embedded within a relational network—will influence knowledge integration. More specifically, grounded in social capital theory that address value cocreation embedded in relationships through its relational foundation, the following research questions are examined: 1) Is there an influence of social media use on employees’ social capital indicated by social networking, trust, and shared language? 2) Is there any impact of social capital on knowledge integration? 3) Is there a mediated impact of social capital on the relationship between social media use and knowledge integration? The remainder of this paper is organized as follows. We first develop a research framework based on the literature with hypotheses established. We then elaborate our methodology, followed by empirical results. We conclude the paper with a discussion and implications of this research.
2 Theoretical background and hypotheses 2.1 Knowledge integration Knowledge management involves four different but interdependent processes: knowledge creation, knowledge storage/ retrieval, knowledge transfer, and knowledge application (Alavi and Leidner 2001). Knowledge itself can not generate competitive advantages; it is the effective application of existing knowledge that creates values. Similarly, the processes of knowledge creation, storage/retrieval, and transfer do not necessarily result in improved organizational performance; effective knowledge application, however, does (Alavi and Leidner 2001). Although knowledge application has direct influence on organizational performance and value, it has received scant attention in the literature. Knowledge integration is a key aspect of knowledge application that refers to the synthesis of individuals’ specialized knowledge into situation-specific systemic knowledge (Mohan et al.2007). Knowledge integration that requires individually held knowledge to be shared or transferred to other people often creates new knowledge grounded on the synthesis of existing knowledge, thus suggesting that knowledge integration not only involves, but also exceeds knowledge sharing and creation. The interactivity of social media allows participants to freely produce, organize, locate, and share content. Social media’s open and participatory nature is best exemplified in their platforms (e.g., Facebook, twitter) where people work collaboratively to create, compile, and update knowledge. In this sense, the knowledge in virtual communities created by social media is a collective knowledge resulting from the integration of knowing among community members.
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Prior research has identified three primary mechanisms for knowledge integration: directives, routines, and self-contained task teams (Grant 1996). Directives are “the specific set of rules, standards, procedures, and instructions developed through the conversion of specialists’ tacit knowledge to explicit and integrated knowledge for efficient communication to non-specialists” (Alavi and Leidner 2001). Routines refer to process specifications, interaction protocols, and coordination patterns through which individually held knowledge can be integrated and applied without communicating it explicitly (Grant 1996). Task teams provide a platform for individual team members’ diverse knowledge and expertise to be assembled, integrated, and applied to complex organizational tasks. In addition, a large amount of knowledge integration does not follow formal organizational structure; rather, it depends on the interpersonal relationships developed through informal interactions (Nirmala and Vemuri 2009). For instance, Xerox technicians relied rarely on their official repair manuals, but instead heavily on their self-developed networks of informal knowledge sources (Cheng et al. 2009). This feature is particularly evident in the network age, since the distributed nature of work requires employees to communicate and collaborate across multiple networks rather than in a single task team. Therefore, we propose informal networking as the fourth knowledge integration mechanism to complement prior research which has primarily focused on formal organizational structures.
2.2 Social capital theory Social capital theory serves as the foundation of this study to investigate the impact of social media on knowledge integration. Social capital denotes the resources embedded within an individual’s or an organization’s network of relationships, including both interpersonal relationships and the resources rooted in the relationships (McFadyen and Cannella Jr 2004). Unlike the physical capital embodied in physical implements of production and the human capital lodged in humans themselves, social capital inheres in the relationships between actors within a social network (Coleman 1988). Social capital is a multidimensional concept, which can be divided into structural, relational, and cognitive dimensions (Nahapiet and Ghoshal 1998). The structural dimension means overall pattern of connections between people, i.e., whom you connect and how you connect them. The relational dimension describes resources embedded in the social relationship, such as trust, commitment, and reciprocity. The cognitive dimension refers to a common context which increases understanding among people represented by shared language, codes, and goals. For members of a network, the social capital benefits contain broader sources of information and opportunities that are otherwise unavailable.
In the era of knowledge economy, knowledge is an important strategic resource upon which competitive edge is established. Empirical studies have demonstrated that knowledge integration is inherently a social and situated process, which is strongly driven by social capital (Gao et al. 2013; Li et al. 2012). However, existing literature has not addressed the role of emerging IT in fostering social capital in the context of knowledge integration. Given that social capital stems from social interactions among individuals and that social media is the main medium through which people interact in the networked environment, we contend that social media can foster the development of social capital, which, in turn, facilitates knowledge integration. The multidimensional view of social capital offers a pliable theoretical perspective for explaining the knowledge integration process since each of these three dimensions, directly or indirectly, promotes through other dimensions the combination of knowledge (Tsai and Ghoshal 1998). Although the three dimensions each represent a distinct facet of social capital, they are closely interrelated. Specifically, structural capital impacts cognitive capital and relational capital, its effect on the combination of knowledge is thought to be obtained directly and indirectly through the development of the relational and cognitive dimensions of social capital (Nahapiet and Ghoshal 1998). In this study, we adopt social networking, trust, and shared language to represent, respectively, the structural, relational, and cognitive social capital. Previous research has recognized them as important instantiations of social capital and will facilitate voluntary behaviors in the virtual context. Figure 1 depicts the research model. 2.3 The effect of using social media at work As an effective social networking platform, social media are widely employed to maintain and strengthen interpersonal relations. Social networks refer to the structure of the direct and indirect relationships that people create which provide socioeconomic resources to the individual (Ganley and Lampe 2009). The structural features of social media induce new and ingenious forms of communication, which expand the breadth and depth of social networking. First, social media use at work can help discover potential ties and then convert them into weak ties. Potential ties denote social ties that are technically possible but not yet activated socially (Haythornthwaite 2002). They are only activated (i.e., converted from potential to weak) by some kind of social interaction between members (Haythornthwaite 2005). Social media provide personal profiles and visible social network in the open space, enabling employees to identify those who share the same specialty and expertise in order that potential ties can be activated easily. Unlike email and instant message, which must be targeted to specific recipients, social media allow employees to circumvent organizational
Inf Syst Front Fig. 1 Research model
Trust
H1a Social media use at work
H2a
H1b
H1c
Social networking H2b Shared language
H3a
H3b
Knowledge integration
H3c
Experience with social Control variable
hierarchies and connect with geographically or organizationally distant readers, offering a free social networking platform where otherwise disconnected people are bridged (Brzozowski et al. 2009). Second, social media are helpful in the accumulation of weak ties and the maintaining of strong ties in work environment. Social media can accumulate weak ties effectively, since these ties are cheaply and easily maintained. On the other hand, the maintaining of individuals’ strong ties usually depends on multiple channels, while online communities based on social media are beneficial for distributed employees who perform various forms of work across geographical boundaries. Overall, social media’s spontaneous and informal interaction has been regarded more effective in shaping working relationships than scheduled and formal communication (Harden 2012). Third, social media have a variety of synchronous (e.g., microblogging) and asynchronous (e.g., blogs, SNSs, wikis) communication channels, providing effective ways to communicate on an unprecedented scale where problems at work can easily be integrated into the interaction process (Farkas 2007). Social media in the workplace therefore complement traditional communication, especially when a large number of individuals are involved. For example, in a many-to-many communication setting, a network-generalized exchange (e.g., wiki) is found to be a more effective structure than group-generalized exchange (e.g., electronic bulletin) (Sohn and Leckenby 2007). The effective communication through social media can enlarge social networking. Therefore, we hypothesize the following: H1a: Social media use at work enhances social networking. Trust is not cultivated in a single information exchange event, but through repeated and frequent social interaction
(Gössling 2004). Contemporary distributed work requires working professionals to communicate across multiple networks and locations (Ou and Davison 2011). Deployment of social media in this distributed workspace may promote employees’ informal interaction and collaborative exchanges, permitting them to know extra information about their contacts, such as personal background and special talents. Deepening mutual understanding can reduce uncertainty about other people’s behaviors and intentions as a prerequisite of trust (Valenzuela et al. 2009). The more we recognize others, the more we may trust or distrust them (Newton 1999). Individuals who trust each other may further communicate through various channels, including social media. Similarly, employees are unlikely to employ social media to keep in touch with people whom they really distrust. In fact, social media use and mutual trust have a reciprocal relationship. This study focuses on the potential of social media to augment social capital. Therefore, we propose: H1b: Social media use at work enhances trust. In an online community, the wording, symbols, terms, jargon, and narrative forms commonly adopted by its members constitute their shared language (Law and Chang 2008). Shared language includes but goes beyond language to address acronyms, subtleties, and underlying assumptions that sustain everyday communication (Lesser and Storck 2001). Structuring and storing the collective memory within common repositories are helpful in building shared language (Lesser and Storck 2001). Social media possess high reprocessability, and support history maintaining. Previous work-related information and interactions can be easily reexamined. This media feature is useful in presenting a cognitive map to employees for developing shared language. We thus suggest: H1c: Social media use at work enhances shared language.
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2.4 Correlating the components of social capital Social networks created by social media are networks of loosely related participants, providing individuals with access to other people and their resources. Trusting relationships evolve from social interactions, since frequent and close social interactions enable participants to know one another, share resources, and create a common viewpoint (Tsai and Ghoshal 1998). People usually constitute their own social network based on their specific interests and needs; the potential of social networking is to facilitate closer relationships and more frequent interaction among people, which, in turn, are stimulated by their engagement in discussions and sharing of resources. Thus: H2a: Social networking is positively related to trust. Social networking is believed to promote shared language based on the premise that socially connected members can learn a common set of language, and create new languages with one another within the network. In a well-connected network, through the process of informal social interaction, participants realize and employ their communities’ languages. Meanwhile, they may also create new sets of language based on their common interests and shared understandings. Therefore: H2b: Social networking is positively related to shared language.
2.5 Facilitating knowledge integration with social capital In virtual communities, individuals’ contributions are difficult to measure, suggesting that trust is especially important in voluntary behaviors such as knowledge contribution and integration. Robert et al. (2008) purport that trust influences knowledge integration in two ways. First, trust allows individuals to justify their decision to contribute, and enables the exchange of useful information. Second, trust enables individuals to freely exchange information which is key to the successful collaboration, thus increasing the amount and varieties of information exchanged. Trust also promotes the use of information since it enhances the credibility of information. Thus: H3a: Trust is positively related to knowledge integration. Social networking not only connects collaboration partners for exchanging and combining knowledge, but also influences the anticipation of value through such exchange. In a collaborative environment, individuals often develop and rely on their own ego-centered networks when deciding with whom
to collaborate and how to collaborate (Reagans and McEvily 2003). Knowing who knows what and accessing others’ knowledge through one’s social networking have become increasingly important in knowledge management practice: the better you know another person, the more likely it is that you can acquire knowledge from them (Borgatti and Cross 2003). Considering social expectations of reciprocity, individuals who have built an extensive social network will actively engage in knowledge contribution and exchange activities (Chow and Chan 2008). Intimate personal relationships will also cultivate a virtual community member’s sense of belonging, reducing knowledge search cost, and ensuring reliability (Kim et al. 2012). Therefore: H3b: Social networking is positively related to knowledge integration. From the perspective of semantics, knowledge integration is concerned with conveying a consistent and common meaning across stakeholders with specialized knowledge. Such integration can be achieved through the use of shared language to transmit or retrieve information (Nahapiet and Ghoshal 1998). Shared language enhances people’s ability to access information and share ideas because it reduces both their encoding and decoding efforts (Szulanski 1996). Therefore, people are more willing to engage in knowledge integration with those sharing common sets of language and communicative pattern together. Shared language also represents the overlap in knowledge. Similar knowledge structures provide a cognitive map for individuals on where and how information should be organized, increasing the efficiency and effectiveness of different parties to combine the knowledge (Nahapiet and Ghoshal 1998). Thus: H3c: Shared language is positively related to knowledge integration.
2.6 Control variable We expect that individual experience with social media may have a positive effect on knowledge integration. People who have rich experience with social media are likely to better understand how their knowledge and expertise are relevant, increasing the extent to which knowledge is integrated with others. Therefore, we control for the effect of experience with social media on knowledge integration.
3 Methodology This study adopted a quantitative approach to validate the research model. After collecting data from a cross-sectional
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survey, we implemented 2 rounds of focus group discussions involving 13 working professionals. These participants came from different industries (insurance industry, software industry, banking), all of them had social media experiences and their companies had applied KMS. The focus group discussions, complementary to the quantitative approach, investigated employees’ social media use at work and their opinions on the influence of social media on knowledge integration. This mixed method approach can help further our understanding of social media’s potential.
instrument individually. Afterwards, they were requested to discuss their translation results together item by item to ensure consistency between the original English and Chinese version, until agreement was achieved. Over a period of 3 weeks, a total of 262 valid questionnaires were collected, yielding a valid response rate of 64 %. We assessed common method bias based on Harman’s singlefactor. No dominant factor emerging from the factor analyses was discovered, suggesting that common method bias was not a concern. Table 1 lists the demographic characteristics of the respondents.
3.1 Measures To enhance validity, we relied on existing measurement scales in the literature to develop the survey questions. The measurement for social media use at work was adapted from Kankanhalli et al. (2005) regarding the employ of knowledge management systems at work. The measures of social networking were adapted from Chow and Chan (2008). Trust was derived directly from the study of Levin and Cross (2004). Shared language was measured with items developed based on Nahapiet and Ghoshal (1998). Knowledge integration was measured with items adopted from Tiwana and Mclean (2003). The control variable, experience with social media, was adopted from Ma and Agarwal (2007) with the items modified to fit the current research. Unless otherwise specified, all items were on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). Appendix A lists the summary of construct operationalization. 3.2 Data collection Data for validating the research model were collected using a paper-based survey on a voluntary basis from working professionals in China, all of whom were also part-time postgraduate students at one of the following institutions of higher education: Harbin Institute of Technology (Harbin) and University of Science and Technology of China (Hefei). The questionnaires were distributed by two teachers. The Chinese culture can be described as high powered distance (Hofstede, Hofstede, and Minkov 2010), which motivated students to participate in the survey. Before filling out the questionnaires, we introduced social media, KMS, and knowledge integration to the participants, explained the research objectives, and ensured confidentiality. The survey was translated from English to Chinese according to the translation committee approach, which is suitable for linguistic and psychological equivalence through the sense-making process between committee members. Two native Chinese speakers fluent in English were involved in the committee. All were Ph.D. students majored in information systems familiar with the research area. They were asked to translate the English
4 Data analysis and results 4.1 Measurement model To evaluate the adequacy of the measurement model, the reliability, convergent validity, and discriminant validity were examined. Reliability was assessed by examining the Cronbach’s alpha and composite reliability for each construct. From Table 2, it can be observed that the Cronbach’s alpha ranges from 0.775 to 0.906, and the composite reliability ranges from 0.780 to 0.941, both of which exceeds the threshold value of 0.7, thus confirming their reliability. Convergent validity was evaluated by examining the average variance extracted (AVE) from the constructs. As shown in Table 2, all the AVE values are greater than the recommended level of 0.5, the convergent validity of the measurement model is verified. Discriminant validity was assessed by comparing AVE and the variance shared between the constructs. In Table 3, the diagonal values stand for the square root of the AVE, all of which are greater that the off-diagonal correlations in the corresponding rows and columns, proving discriminant validity. Considering the inter-correlations among some theoretical constructs and the self-reported nature of data, we tested the potential threat of multicollinearity and common method bias. To assess multicollinearity, collinearity diagnostics for constructs were conducted. The analysis shows that the collinearity indicators (variance inflation factors and tolerance values) are all less than the acceptable cut-off points, indicating that multicollinearity does not present a serious problem. To evaluate common method bias, our principal components factor analysis shows that each principal factor explains approximately equal variance (7.28 %~17.80 %). Furthermore, the correlation matrix (Table 3) indicates that the highest inter-construct correlations are 0.639, while common method bias is usually evidenced by high correlations (r >0.900). Taken together, common method bias is not a likely threat.
Inf Syst Front Table 1 Demographic characteristics of respondents Measure Gender Male Female Age ≤20 21–30 31–40 41–50 ≥51 Work experience
