Social Media: A Platform for Innovation

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skilled and talented individuals interact via social media platforms such as ... activities of business-to-business companies [17]. ... the GE CMO quotes “what might've taken a team – in the best case – a week, can now be done in minutes” [20].
Proceedings of the 2015 Industrial and Systems Engineering Research Conference S. Cetinkaya and J. K. Ryan, eds.

Social Media: A Platform for Innovation Sarah M. Asio and Sasan T. Khorasani Texas Tech University Lubbock, Texas Abstract The pervasive use of social media today and critical role of Innovation in economic growth and development necessitate the development of a framework for explaining this relationship. Social media provides a channel for exchange of information and sharing of views through a virtual platform. Individuals, especially millennials or Generation Y, are constantly preoccupied with social media. Spontaneous and planned innovations result as creative, skilled and talented individuals interact via social media platforms such as Facebook, Twitter, Youtube, LinkedIn, Blogs, Myspace and Wikis. This paper explores the possible use of social media platforms for identifying opportunities for innovation. The exchange and sharing of information on social media has the potential to influence perceptions and spark off debate and discussions among individuals with diverse backgrounds, culture, expertise, and viewpoints. The proposition in this study is that information exchange on these platforms presents opportunities for identification of creative ideas and solutions to problems which eventually result in useful innovations when implemented. This study develops a theoretical model describing the hypothesized relationship between innovation and social media based on theories of connectivism, social learning and the Medici Effect. A case study of student engineering design teams is discussed and directions for future research presented.

Keywords Social Media, Innovation, Organizational Productivity

1. Introduction Social media refers to internet-based networked applications that permit participants to communicate, collaborate, and creatively express themselves in an interactive manner [1, 2]. The advent of internet and communications technologies has necessitated the integration of social media technologies into organizational work practices. Social media enables individual knowledge management and construction, resulting in the development of platforms where collective knowledge is socially mediated [3, 4, 5]. Types of social media include social networks (Facebook and LinkedIn), media sharing networks (Youtube and Flickr), microblogging networks (Twitter), blogging sites and forums (WordPress, Myspace and Wikis), bookmarking sites (Delicious and Stumble upon), social news sites (Digg and Reddit) and web-based or cloud-computing sites (Google drive and Drop box). The benefits of social media range from fostering individual and group creativity through idea sharing and gathering through social network connections based on common interests [6]. Social media has been used to develop personal e-portfolios using blogs such as WordPress [7], stimulate participation through micro-blogging platforms such as Twitter [8], and encourage collaboration [9]. In general, social media provides platforms that breed creation of personal and social learning spaces that support learning [3]. Knowledge is then synthesized as experts from different fields present their viewpoints on a particular problem which may result in creative solutions. Treem and Leonardi [10] propose that the use of social media in organizations can be broadly categorized as supporting visibility, persistence, editability and association, but state that there is shortage in scholarly work on the distinction between benefits of social media and pre-existing organizational computer-mediated communications. Past studies have investigated the role of social media in diffusions of innovation [11]; fostering collaboration for open innovation [12]; management of customer involvement [13]; co-creation of business concepts [14]; online communities for collaborative customer co-design [15]; collaborative innovation mechanisms [16]; and innovative activities of business-to-business companies [17]. Statistics have shown that 80% of individual’s knowledge about their jobs is gained through informal learning activities (such as communications via social media), both coordinated and uncoordinated [18]. This study contributes to the knowledge gap by identifying the relationship between Social Media and Innovation and proposing a conceptual framework.

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Asio and Khorasani Organizations have an opportunity to capitalize on employee connections across time and space via social media. Companies can leverage employees’ personal time for further reflection, synthesis and analysis of work problems which increases productive use of employee time both on- and off- work. Social media can potentially be used as a tool to improve worker productivity and quality of products and services based on the premise that some useful information can be mined from data collected through informal social media communications. This study seeks to answer the research question: “Are social networks useful for innovation?” The main objective of the study is to identify the relationship between social media and innovation based on literature review and a case study. The case study describes the relationship between Team Innovation and social network, which will be used to explore the study hypothesis. The authors hypothesize that the extent of connectedness among individuals in a social network is indicative of their Social Media network which in turn is related to Innovation. The next section provides a literature review and subsequent sections describe the methodology, results, discussion and conclusion.

2. Literature Review Between 2007 and 2008, 500 corporations were reported to double their blogs; the percentage of companies using social networking in the same period increased from 8% to 49% [19]. Examples of use of social media for innovation include GE Aviation’s use of Salesforce chatter – an enterprise social network, to empower employees from sales and marketing departments to answer customer questions and solve problems by leveraging their time; as the GE CMO quotes “what might’ve taken a team – in the best case – a week, can now be done in minutes” [20]. In another case study, a research and technology organization, MITRE, leveraged the benefits of social media resulting in more comments on proposals from a wider group of employees [21]. The use of social media evolves from interaction of user-controlled and user defined ideas in different media platforms. The discourse occurs in a virtual setting across participants that may be geographically dispersed through the use of internet technologies. Social media platforms can therefore be regarded as virtual forms of physical interfaces such as discussions during work breaks, professional networking sessions, brown-bag sessions, brainstorming sessions, focus groups and informal learning networks [22]. Strong evidence suggests that social media can facilitate the creation of environments that allow participants to aggregate information, share achievements, participate in collective knowledge generation and develop their own understanding or interpretations [23]. Results of social media that have been reported in literature include openness, collaboration, social networking, user-generated content, and collective wisdom [3, 24-29]. Hilton [30] proposed that social media empowers individuals to take charge of their own learning, making them an arbiter of their knowledge, work, publication and thinking [23]. Social media facilitates learning, more specifically personalized learning in an informal setting as the direction of discourse rests entirely on the users of the social network. Topics for discussions are typically sparked off by various catalysts such as debates, current or past events, news stories, reflective thoughts posted by individuals in the social media network and/or topics stimulated by general interest of participants [31]. The nature of discourse on social media networks requires facilitation in order to succinctly guide or provide pointers throughout discussions so as to generate usable ideas. A facilitator sorts through accessible data from social media discussions for useful threads of information. Personalized learning environments in social media have the potential to enable active individuals to become highly self-motivated and autonomous learners as an integral part of their work experience [32, 33, 34]. This fosters both individual and overall organizational productivity. 2.1 Related Theory A number of theories are proposed to explain the relationship between social medial and Innovation. For example the social learning theory proposed by Bandura [35] posits that cognitive processes associated with learning take place in a social network. The social network theory on the other hand focuses on interactions among individuals within social networks of varying complexity [36]. Another important theory is the Medici Effect as proposed by Johansson [37] which suggests that most innovative ideas occur when people from different disciplines and cultures meet. Over the last two decades, communication and interactions between people from different background has increased, being magnified by the use of social media networking tools and technologies in recent years. The theory of diffusion of innovations further augments this concept based on the proposition that individuals openly model their own behavior against others [38]; thus, a communication of innovations over channels such as social media results in a quick propagation of novel ideas across in a social network. Last but not least, the theory of connectivism, also known as the learning theory for the digital age [39], explains how learning can be affected through a social network using digital age media platforms.

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Asio and Khorasani Continuous improvement is a necessity for business success in today’s environment. One of the ways of enabling innovation is through involvement of the workforce in problem solving process [40]. Social media can be useful as a tool for emerging innovations which have a direct impact on organizational quality and productivity. According to Robinson [41] Innovation can be defined as an idea, concept or object that is perceived new by observers. The theory of diffusion of innovation stresses that the most important element for evaluating innovations, that is to say the newness of any given concept or idea, is the point of view the audience. Furthermore, Csikszentmihalyi [42] reiterates the social development theory asserting that the only way to know whether a new thought in an individual’s mind is valuable is through social evaluation with reference to a standard thus creativity occurs through the interaction between individuals’ thoughts and a socio-cultural context. The study therefore proposes that there exists a relationship between social media and innovation. More specifically, social media is hypothesized to act as a mediating variable for the relationships between group inputs and processes and innovation. A framework modeling the theorized relationships was developed based on the Input-Process-Output framework that has popularly been used for studying team performance. Input and process factor categories surrounding teamwork and organizational environments are broadly proposed as:  Organizational context and environment - work place setting and support for team innovation activities within the organization.  Team process - series of actions and activities carried out by team members collectively in the achievement of their goals.  Psycho-social traits - factors related to the cognitive and social aspects of teamwork. Cognitive factors are related to mental and perceptual team processes. Social factors describe the relationships and networks among individual team members. The theoretical framework shown in Figure 1 is proposed. The authors theorize that social Media has moderating effects on team predictors within the organizational context/ environment, team process and psychosocial traits which ultimately have a direct effect on Team Innovation. Team Innovation would in turn drive organizational productivity and quality management efforts.

Organizational Context & Environment

Organization Productivity

Team Processes Social Media

Innovation

Psychosocial Traits

Quality Management

Figure 1.Social Media and innovation Framework

3. Methodology Social media has been measured in previous studies using surveys [17, 43]. A case study is discussed below to show any potential relations between individual indicators of Social Network (SN) and Team Innovation. Measures for the social network construct were based on social network analysis by Williams [44]. Item 1 (SN1: “Not including yourself, how many of your team members did you know before starting your project?”) and Item 2 (SN2: “If you knew any of your teammates previously, how many would you have considered a friend before your project started?”) provided quantitative data on the number of students from the same team that each participant knew before commencing the design project and how many were considered friends. The implication here is that any prior friends would potentially constitute an individual’s social media network as well. Consequently, the higher the

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Asio and Khorasani number of individuals regarded as friends, the larger one’s social media network. Item 3 (SN3: “How often did you get together for non-academic purposes with any of your teammates?”) requested participants to rate the frequency of social interaction between a respondent and fellow team mates. The extent of such informal social interaction is an indication of the degree of informal interactions between a respondent and friends (who are presumed to be in their social media network) via social media technologies. Item measures for team innovation were developed based on definitions of innovation from literature [45, 46] and participant perceptions were measured on a 6-point Likert scale. 3.1 Study Sample The study population, which was part of a broader study on predictors of Team Innovation, consisted of students, mostly seniors, engaged in senior design capstone projects; projects involving engineering design work and creative problem resolution over a period of one semester [47]. Each team of students was nested within a design sections which was further nested within a particular program of study. Table 1 shows demographics for the study population. Study data was collected from 709 participants. A response rate of 91% was observed for the social network construct, with 87% responses from males, 11 percent from female while 2% declined to specify their gender. A total of 709 respondents are recorded for the post-screening data while up to 644 responses were provided to the social network survey questions. A total of 207 teams were identified for in study. Table 1: Study sample demographics by program of study %age Program # Respondents Respondents

# Respondents per question SN1P

SN2P

SN3P

Chemical Engineering

56

8%

51

50

50

Civil Engineering

64

9%

53

58

58

Computer Engineering

22

3%

22

22

22

Computer Science

25

4%

25

24

24

Construction Engineering

24

3%

19

20

20

Electrical Engineering

204

29%

194

180

180

Industrial Engineering

27

4%

24

24

24

Mechanical Engineering

281

40%

252

242

242

Dual Degree

6

1%

4

6

6

Total

709

644

626

626

91%

88%

88%

Response Rate

3.2 Scale validity and reliability analysis Prior to carrying out any statistical analysis, data for the study was screened and verified based on procedures specified by Asio [45]. Data for the social network construct was based on different measurement scales and was therefore standardized. The constructs in the study were then checked for normality. Figure 2 shows a path diagram depicting measurement items for social network and innovation constructs. Social network, an exogenous latent variable, is hypothesized too have a relationship with Team Innovation, which is classified as an endogenous latent variable in this study.

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Figure 2: path diagrams representing measurement model Confirmatory factor analysis was carried out to verify the validity of the scales used in the study based on acceptable threshold. A measure of Cronbach’s alpha for internal consistency of the scale was also calculated to check the reliability and consistency of the scales. A Cronbach’s alpha value of α = 0.7051 was observed for Social Network and α = 0.9115 for Team Innovation. A threshold of α > 0.70 is commonly taken to indicate “good” reliability [48] The individual responses for the study were then aggregated to the team level prior to conducting any statistical analyses in order to test study hypothesis. This was done because although responses to survey items were collected at the individual level, all questions were inquiring about team-level measures. Consequently, participants provided their individual perceptions of team level constructs. The Intra Class Correlation (ICC) coefficients were therefore calculated in order to measure the degree of homogeneity within teams on each measure of interest. The ICC value for Social Network is 0.49, and that for Team Innovation is 0.2067. Acceptable thresholds suggest that an ICC value of 0.2 is demonstrative of strong group-level association [48]. Since both ICC values for Social Network and Team Innovation are greater than 0.2, this justifies aggregation of the constructs to the team level. 3.3 Correlation Analysis Correlation analysis was carried out in order to quantify the extent and direction of the relationship between Social Network and Team Innovation as set forth in the hypothesis. The formula for correlation is expressed in Equation (1) [49]:

(1) Where ρ is the correlation coefficient, is the covariance between two variables x and y, is the standard deviation of x and is the standard deviation of y. Correlation analysis was run in SPSS Version 22.

4. Results Correlation analysis results in Table 2 indicate that there is a weak but significant positive correlations between Social Network and Team Innovation at an alpha level of α = 0.05 (ρ = 0.154, p-value < 0.0001). Further analysis of individual social network indicators and Team Innovation show that there are weak but significant positive correlations between Social Network indicators and Team Innovation (Table 3). SN3 ranks first and is significant at an alpha level of α = 0.05 (ρ = 0.126, p-value = 0.002); SN2 ranks second and is significant at an alpha level of α = 0.05 (ρ = 0.102, p-value = 0.011); and SN1 ranks third and is significant at an alpha level of α = 0.01 (ρ = 0.084, pvalue = 0.032). Table 2: Correlation analysis results for the relationship between Social Network and Team Innovation Social Network

Social Network

Team Innovation

1

0.154*

Team Innovation 0.154* 1 * Correlation is significant at the 0.01 level (2-tailed).

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Asio and Khorasani Table 3: Correlation analysis results for the relationship between social network Indicators and Team Innovation SN1 SN2 SN3 Team Innovation SN1

1

0.715**

0.313**

0.084*

SN2

0.715**

1

0.382**

0.102*

SN3

0.313**

0.382**

1

0.126**

Team Innovation

0.084*

0.102*

0.126**

1

** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). Results from the broader study on predictors of Team Innovation [46] suggest that Social Network is not a statistically significant predictor of Team Innovation (beta = 0.04, t-value = 0.4373, p-value = 0.6621).

5. Discussion A weak but significant correlation was observed between Social Network and Team Innovation [46]. This implies that the extent of social media connectedness has a positive effect on collective innovation within the social network but only to a certain threshold. The insignificance of linear regression results for Social Network as a direct predictor of Team Innovation [45] may suggest that social network, hence Social Media, impacts factors such as communication frequency and trust (that is to say team process factors and/or psychosocial traits), which directly impact Innovation. This suggests that Social Media is a moderating variable that may have an effect on the direction and strength of the relationship between other predictors and Team Innovation. These results support the use of Social Media as a medium through which innovation is encouraged given that other pertinent factors are in place. For details of a comprehensive set of predictors of Team Innovation readers are referred to Asio [45]. A closer look at the relationship between Team Innovation and individual indicators of the Social Network construct suggest that the higher the rate of informal interactions among participants in a social network, the more the likelihood for innovation to occur. Social media is further proposed as a tool that fosters continuous process improvements as it increases chances for innovation. Innovation is an important value-added indicator for organization productivity. Consequently, the use of social media within organizations has the potential to increase overall productivity through increased innovation. The use of networking technologies as communication tools for faster flow of information are major advantages of social media in organizations. Social media therefore acts as a medium for information exchange between work departments.

6. Conclusion This paper identified the potential use of social media as a platform for innovation. A case study involving engineering students engaged in design team work is used to illustrate the relationship between social media and social network, which is identified to have a weak but significant correlation with Innovation. Theories of social learning, connectivism, Medici Effect, and diffusion of innovations are used to explain mechanisms through which social media contributes to innovation. A conceptual framework showing the moderating relationship between social media and other team inputs and processes is suggested. The Innovation outcome is further proposed as having an effect on organizational productivity. The findings are useful for organizations which increasingly have the need to stay connected across organizational hierarchies as well as with business counterparts and Customers. Social media also provides organizations with the opportunity to leverage employee time both on- and off- work which has a bearing on employee productivity. 6.1 Future research and Limitations One of the limitations to the use of social media in organization is lack of structure in informal social media communications which may necessitate careful mining of data for useful information. Another limitation may result from personal privacy issues (and privacy settings on various social media) .The case study described here is based on several assumptions that cannot be verified at this point. A future study could consider carrying out controlled experiments where social media use for innovation, and organizational productivity and quality management is systematically investigated.

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