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Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73.

Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage Paul M. Di Gangi* Assistant Professor of Information Systems Department of Management, Information Systems, and Quantitative Methods Collat School of Business University of Alabama at Birmingham 1150 10th Ave S. Birmingham, AL 35294-4460 [email protected] 205.934.8881

Molly Wasko Professor of Information Systems Department of Management, Information Systems, and Quantitative Methods Collat School of Business University of Alabama at Birmingham 1150 10th Ave S. Birmingham, AL 35294-4460 [email protected] 205.934.8806

ABSTRACT Business models that rely on social media and user-generated content have shifted from the more traditional business model, where value for the organization is derived from the one-way delivery of products and/or services, to the provision of intangible value based on user engagement. This research builds a model that hypothesizes that the user experiences from social interactions among users, operationalized as personalization, transparency, access to social resources, critical mass of social acquaintances, and risk, as well as with the technical features of the social media platform, operationalized as the completeness, flexibility, integration, and evolvability, influence user engagement and subsequent usage behavior. Using survey responses from 408 social media users, findings suggest that both social and technical factors impact user engagement and ultimately usage with additional direct impacts on usage by perceptions of the critical mass of social acquaintances and risk. Keywords: social media, use, user experience, user engagement, social interactions, technical features, social networking * Corresponding Author 1

Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73.

Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage INTRODUCTION Under the traditional business model, an organization’s primary goal is to create a tangible product or service and protect the organization from competitors by creating an organizational boundary. Pine and Gilmore (1999) challenged this belief by changing the value proposition to include the user experience for deriving value. Through social media, users modify, share, and reuse content, regardless of the creators’ original meaning or purpose. Lessig (2008) suggests that remixing content is a generational shift in how users communicate. Now users can provide unique perspectives on what they consider personally meaningful. Social media is defined as “a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of User Generated Content” (Kaplan & Haenlein, 2010, p. 61). Social media is valuable because it not only satisfies the needs and interests of users, but also supports an interactive audience for advertising and market intelligence to an organization. Alexa.com, a web analytics company, found eight of the 10 most visited websites rely on user-generated content (UGC). For instance, Facebook boasts over 829 million daily users. Social media represents a new business model where value is co-created by users contributing, retrieving, and exploring content with other users via a social media platform developed by an organization. Co-created value is defined as the mutual benefits that both an organization and users derive from sharing in joint activities. Organizations benefit when they leverage UGC to develop marketing insights, to realize cost savings, to grow brand awareness, and ultimately to generate new innovations. Users benefit from the ability to socially interact within the social media platform to fulfill personal needs and interests. However, co-creating value with users is risky. Organizations must focus on the user experience or negative consequences can occur, such as loss of user participation. For example, Chevrolet created a social media platform for users to create commercials for the Chevy Tahoe. Chevrolet provided video clips of the Tahoe being driven through beautiful countryside and navigating rough terrain. Chevrolet also allowed users to overlay text to create their own brand messages that users subsequently used to create negative messages about Chevrolet. The end result was a prominent position on CNNMoney.com’s 101 Dumbest Moments in Business (2007). This research seeks to answer the following two questions: what factors shape user engagement in social media and to what extent does user engagement affect an individual’s social media usage behavior? This research makes two contributions to the field of IS. First, a theory of Social Media Engagement (SME) is developed which predicts that the user experience, encompassing both the social interactions among users and the technical features of the social media platform, will influence user engagement. User engagement will, in turn, positively affect usage. Second, this theory is operationalized and tested using survey, interview, and user-log data to empirically assess the experiential factors that predict high levels of user engagement, and the impact of user engagement on the frequency of use of the social media platform.

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Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73.

SOCIAL MEDIA ENGAGEMENT THEORY This research builds upon Prahalad and Ramaswamy’s (2004) model of co-creation in the service sector and adapts this model to create a parsimonious theoretical framework to explain SME. Originally built as a model of interaction between a user and organization, we extend this model to focus on the social interactions among users that are supported by the social media platform provided by an organization. We build upon Prahalad and Ramaswamy’s (2004) work by applying a socio-technical systems perspective to first address why the user experience influences engagement and subsequently usage. Central to this model is the concept of user engagement. Within the context of IS research, the term engagement is applied inconsistently and results in many different conceptual models that lack clear definition and measurement (Hwang & Thorn, 1999; O'Brien & Toms, 2008; Ray, Kim, & Morris, 2014). Although researchers implicitly agree that user engagement matters, exactly how to define engagement and clearly delineate engagement from similar concepts such as the user experience and actual usage is needed to advance research in this area. To accomplish this, the SME theoretical model outlines distinctions separating the factors that form the user experience, user engagement, and usage. First, SME theory accounts for the role of technology as the underlying platform needed to facilitate social interactions among users that are globally and temporally distributed. Clearly, the rise of social media comes in large part from the evolution of technology to provide a unique user experience that enables users to connect in new ways that were never before possible. The user experience referred to in this research applies the definition of experience as the content of direct observation or participation in an event. When experience is defined as a noun, referring to the content stemming from direct participation, there are two critical factors that form the user experience in social media: the experience derived from the social interactions and the experience derived from the technical features. Social interactions are defined as the communication among users through social media (Prahalad & Ramaswamy, 2004). Social interactions form the user experience by fostering a personalized relationship among users, by serving as a transparent means of communication, by providing access to social resources including friends, acquaintances, and family members, and by defining the potential benefits and costs to engaging within social media (Jensen & Aanestad, 2007; Kettinger & Lee, 1994; Prahalad & Ramaswamy, 2004; Wixom & Todd, 2005). Social interactions among the users are what provide meaning and guide the user in evaluating how intensely involved they wish to be (Barley, 1996; Jensen & Aanestad, 2007). Technical features are defined as the perceived capabilities of the technology. Technical features provide users with the tools to enable interactions, and to impact the direction, magnitude and scope of benefits for individual users and the organization (Brown & Magill, 1998; Simon, 1991). Technical features include: the extent to which users can retrieve information and interact, the flexibility to use features for multiple purposes, the ability to integrate content, and the evolvability of the features to meet users’ specific needs as they become more proficient with the platform. When organizations support the creation of the user experience to meet user’s needs, higher user engagement occurs. To date there has been much discussion about how to define user engagement (Hwang & Thorn, 1999; O'Brien & Toms, 2008; Ray et al., 2014). O’Brien and Toms (2008) define user engagement as a category of user experience while several other scholars define user engagement using a more traditional approach of involvement (i.e. 3

Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73. engaging) (Hwang and Thorn, 1999) and participation (i.e, the act of being engaged) (Claussen, Kretschmer, & Mayrhofer, 2013; Lehmann, Lalmas, Yom-Tov, & Dupret, 2012) suggesting engagement is both a psychological state and behavior. More recently, Ray and colleagues (2014) define engagement as “a holistic psychological state in which one is cognitively and emotionally energized to socially behave in ways that exemplify the positive ways in which group members prefer to think of themselves.” (Ray et al., 2014, p. 531). Thus, a clear definition of user engagement still remains elusive for scholars as engagement is defined as a portion of user experience, a psychological state, and user behavior. Given this contradiction, this research defines user engagement as a user’s state of mind that warrants heightened involvement and results in a personally meaningful benefit (i.e., involvement to fulfill a need). It is the antecedent that shapes user engagement and subsequently causes an individual to act. In this way, our definition of user engagement differentiates between the psychological state and behavior that Hwang and Thorn (2008) discuss through involvement and participation, as well as distinguishes between the experience environment and engagement as a mental state discussed by O’Brien and Toms (2008). This separation aligns with the recent work of Ray and colleagues (2014) to suggest engagement is a holistic psychological state of involvement to derive personal meaning. Therefore, user engagement is divided into two psychological components: 1) individual involvement and 2) personal meaning. Individual involvement is defined as the intensity with which a user perceives his/her role within the social media platform (Barki & Hartwick, 1994; Debats, 1998; Hwang & Thorn, 1999; O'Brien & Toms, 2008; Ray et al., 2014; Zaichkowsky, 1985). Individual involvement denotes a user’s perception that his/her role is important to meet his/her needs (Barki & Hartwick, 1994; Zaichkowsky, 1985). Individual involvement has been found to increase arousal and motivation to participate (Muson & McQuarrie, 1987; Zaichkowsky, 1985). Personal meaning is defined as the degree to which a user perceives the fulfillment of his/her needs and interests (Battista & Almond, 1973; Debats, 1998). Fulfillment is derived when user interests are satisfied by the user experience (Battista & Almond, 1973). The central premise of SME theory is that higher user engagement leads to greater usage of the social media platform. Usage is defined as the frequency of a user’s contribution, retrieval, and/or exploration of content within a social media site (Kankanhalli, Tan, & Wei, 2005; Li & Bernoff, 2008). The more frequently users take part in a variety of activities, the more valuable the social media platform becomes to the organization and fellow users, resulting in the co-creation of value (Kankanhalli et al., 2005; Li & Bernoff, 2008).

HYPOTHESES Figure 1 outlines the research model used in this study. The model predicts that the user experience will influence user engagement, which subsequently influences usage behavior.

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Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73.

Social Interactions The first dimension underlying social interactivity reflects the level of personalization of the communication among users, defined as the caring, individualized attention users perceive (Kettinger & Lee, 1994). Prior research suggests that social interactions enable a sense of personalization as users focus attention on topics of interest and conversely filter content not deemed meaningful or relevant to their personal interests (Goodwin, 1996; Mittal & Lassar, 1996). When users perceive the interactions as being personalized to their specific interests, positive user attitudes increase, leading to greater satisfaction and personal relevance, and resulting in higher user engagement (Erat, Desouza, Schäfer-Jugel, & Kurzawa, 2006; Kettinger & Lee, 1994; Prahalad & Ramaswamy, 2004). Furthermore, it is the quality of the social interactions, based on the sharing of information relevant to the user, and not the frequency of the interactions that drives higher user engagement (Parasuraman, Berry, & Zeithaml, 1991; Prahalad & Ramaswamy, 2004). When a personalized experience for users is created, users perceive their role as more important and relevant, and the experience with the site becomes more fulfilling. The second dimension concerns social accessibility. Social accessibility is defined as having the ability to access social resources for the purposes of interacting. Social accessibility can be divided into two components that facilitate user experiences: 1) access to social resources 5

Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73. and 2) access to a critical mass of social acquaintances. Access to social resources refers to the ease with which information, expertise, and other users can be accessed through the social media platform (Wixom & Todd, 2005). Steinfeld et al. (2009) examined organizational workers’ use of Beehive communities and found that the intensity of involvement by workers positively increased when the community provided employees with access to new people and expertise. The second component, a critical mass of social acquaintances, refers to the perception that most people who are important to the user are participating on the same social media platform (boyd, 2007; Dickinger, Arami, & Meyer, 2008; Hsu & Lin, 2008). Research suggests that users with access to a critical mass of known acquaintances remain involved (Hsu & Lin, 2008). In a study on teenagers use of MySpace, users cited their selection and involvement was “cuz that’s where my friends are.” (boyd, 2007, p. 126). Users also indicate that social media sites allow them to share personal experiences with their friends (Joinson, 2008). Thus, a critical mass of known acquaintances was needed in order for users to perceive the experience as meaningful and warrant users intense involvement (boyd, 2007; Dickinger et al., 2008). The third dimension, perceived risk, can negatively influence feelings of engagement and ultimately discourage usage behavior. Perceived risk is defined as the perception of potential harm that a user can experience when interacting (Prahalad & Ramaswamy, 2004). In recent years, social media sites have received negative publicity over disclosing user information (e.g., Read, 2006). As a result, user concerns over the potential risks of posting on social media has received increased attention including privacy, organizational opportunistic behavior, and online identity threats (Ahern et al., 2007; boyd, 2007; Lederer, Hong, Dey, & Landay, 2004; Mishra, Heide, & Cort, 1998; Prahalad & Ramaswamy, 2004). The perception of higher risk causes the user to become more cautious and consider the potential negative consequences of participation (Chaudhuri, 1997; Laurent & Kapferer, 1985). The final dimension is transparency, defined as the degree of information symmetry among users of the social media platform (Prahalad & Ramaswamy, 2004). Transparency reduces user concerns of opportunistic behavior and creates the perception of participation in a trusted community (Prahalad & Ramaswamy, 2004). In order to alleviate user fears of opportunistic behavior, user expectations concerning information disclosure and the degree of acceptable information asymmetry are heightened (Prahalad & Ramaswamy, 2004). If users perceive that other users are taking advantage of them, it will have a negative influence on engagement (Mishra et al., 1998). Therefore: H1: The greater the perceived personalization within the social media site, the higher the level of user engagement. H2: The greater the perceived social accessibility within the social media site, the higher the level of user engagement. H2a: The greater the perceived access to social resources within the social media site, the higher the level of user engagement. H2b: The greater the perceived critical mass of social acquaintances within the social media site, the higher the level of user engagement.

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Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73. H3: The greater the perceived risk associated with the social media site, the lower the level of user engagement. H4: The greater the perceived transparency within the social media site, the higher the level of user engagement.

Technical Features The technical features of the social media platform contribute to the user experience and shape how users interact. Prahalad and Ramaswamy (2004) discuss four technology features that shape the user experience: completeness, flexibility, evolvability, and integration. Completeness is defined as a user’s perception of his/her ability to engage at the desired level of specificity (Wixom & Todd, 2005). Joinson (2008) found that social media users seek content gratification, making it necessary for users to easily access information based on their personal needs. However, users as a collective body have a variety of needs and desires for how information should be accessed and viewed. Consequently, the technical features of a social media platform can be designed to manage a range of specificity levels, depending on the individual user’s preferences. When the social media platform provides comprehensive information that meets the user’s needs, the user experience is positively affected (Wixom & Todd, 2005). Therefore, user engagement will increase when users’ perceive the social media platform meets their information needs. Flexibility is defined as the degree to which users experience existing functionalities in new ways (Prahalad & Ramaswamy, 2004). Research suggests that when users are given flexibility, they are more likely to have positive experiences and view the system as both important and fulfilling (Jensen & Aanestad, 2007; Prahalad & Ramaswamy, 2004). As the degree of flexibility increases, users are able to pursue their interests and/or needs based on their perceptions of how each function should be utilized (Jensen & Aanestad, 2007; Prahalad & Ramaswamy, 2004). As flexibility improves the user experience, this creates an experience that results in higher levels of user engagement. Evolvability is defined as the degree to which the social media platform evolves to meet a user’s current needs and/or desires (Wixom & Todd, 2005). When users first enter the site, they may possess certain expectations for how they will be guided through the initial steps to interact with their fellow users on the social media platform. As their familiarity with the site increases, users’ needs change. New functionalities become necessary to meet more advanced usage behaviors. Research suggests that when organizations provide the tools for creating content that matches users’ current needs, users are more likely to become highly involved and participatory (Di Gangi & Wasko, 2009; von Hippel & Katz, 2002). For instance, Facebook has evolved over time beyond social media profiles and groups to include a vast array of apps and games that create new ways to use Facebook (Claussen et al., 2013). Integration is defined as the degree to which content is intermixed from various sources (Wixom & Todd, 2005). Social media users are accustomed to speaking and generating meaning through combining content from various sources. These processes allow users to create new content that is personally meaningful (Lessig, 2008). Social media platforms facilitate a dynamic content environment that change rapidly, altering the information readily seen by users in real-time. Korn (2001) supports this view by suggesting that sufficient integration of content 7

Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73. can influence users to become more involved (Loebbecke, 2007). Integration increases the ease with which users can access content from different entry points and limits the development of barriers that could detract from feelings of user engagement (Korn, 2001; Loebbecke, 2007). H5: The greater the degree of completeness afforded to an individual within the social media site, the higher the level of user engagement. H6: The greater the degree of flexibility afforded to an individual within the social media site, the higher the level of user engagement. H7: The greater the degree of evolvability afforded to an individual within the social media site, the higher the level of user engagement. H8: The greater the degree of integration afforded to an individual within the social media site, the higher the level of user engagement.

User Engagement and Social Media Usage As discussed earlier, user engagement is defined as a heightened mental state, and is divided into two components: 1) individual involvement and 2) personal meaning. Research suggests that an intense perception of involvement is a key component of engagement (Barki & Hartwick, 1994; Hwang & Thorn, 1999; Ray et al., 2014; Zaichkowsky, 1985), by increasing an individual’s arousal, interest, and motivation to want to participate (O'Brien & Toms, 2008; Ray et al., 2014; Zaichkowsky, 1985). Santosa et al. (2005) examined information seeking behavior and found that user involvement was a positive and significant predictor of a user’s information seeking activities. Consequently, the higher the intensity level of involvement a user feels, the more interested they are in using the system (Santosa et al., 2005). More recently, Ray and colleagues (2014) examined community engagement’s influence on knowledge contribution in online communities and found that greater engagement increased contribution intentions. Personal meaning, the second component of user engagement, encompasses the personal relevance of the social media platform to the individual user. Personal meaning is defined as the degree to which a user perceives that he/she is fulfilling personal needs, values, and/or interests (Battista & Almond, 1973; Debats, 1998). Taken together, users who perceive their involvement with high intensity and feel that the social media platform is personally meaningful for fulfilling their needs will be more likely to contribute, retrieve and explore content. Therefore, H9: The higher the level of user engagement, the greater the individual usage.

METHODOLOGY A mixed method approach was used to develop a survey instrument and validate the perceptual measures of usage. A pre-survey quality control check was conducted. Semistructured interviews were used to identify additional constructs that may influence social media usage for their most and least favorite social media sites. Interviewees were recruited prior to the survey data collection and incentivized using gift cards. The questions used in the interviews are 8

Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73. available in the Appendix. In total, nine individual interviews and one focus group of four individuals were conducted with undergraduate students of a large, southeastern university. Based on the interviews, habit was identified as an influential factor and was added as a control variable.

Measures All measures used in the survey to assess the user experience - personalization, access to social resources, critical mass of social acquaintances, perceived risk, transparency, completeness, flexibility, evolvability, and integration - were based on previously validated items except evolvability. The latter was developed for the purposes of this study. All items assessing the user experience were measured using a 7-point Likert scale. The Appendix contains the survey instrument, originating source of the items, and item loadings. To measure user engagement, individual involvement was assessed using Zaichkowsky’s (1985) measure of involvement using a 7-point semantic differential scale as engagement was defined to include a user’s sense of wanting to be involved in the social media platform. The second dimension, personal meaning, was assessed using an adaptation of Battista and Almond’s (1973) life regard index. The life regard index contains two components 1) framework and 2) fulfillment. The fulfillment component was adapted to reflect the meaningfulness a user attributes to a social media platform. Social media usage was assessed using the frequency of use measure adapted from Kankanhalli et al (2005) with the highest value representing frequent use throughout the day by a user. Additionally, frequency of use was modified to reflect the various behaviors that users may perform within a social media platform (i.e., contribution, retrieval, and exploration). Items were adapted to reflect a user’s frequency with which he/she contributed to, retrieved from, or explored user content generally, friends content, and acquaintances content. The demographic variables of age, gender, expertise with social media (based on 5-point scale), and social media tenure, were included as controls. To measure habit, the self-reported habit index measure from Verplanken and Orbell (2003) was used.

RESULTS Data used for hypotheses testing were collected via survey over two time periods to reduce common method bias (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Subjects responded to the independent variables and then, approximately two weeks later, were asked to provide information about their social media usage over the past two weeks. Open-ended questions were asked in both time periods to identify additional factors that influence user engagement and usage behaviors. Specifically, respondents were asked to indicate why they spent time on the social media platform, what they did, and what features they used most frequently. Lastly, a subset of the respondents (30% of total respondents), were asked to keep a daily log of social media activity to verify the validity of the usage measure. The logs were subsequently checked against survey responses to verify the validity of the perceptual measures of frequency. The population of interest consisted of undergraduate students of a large, southeastern university who were enrolled in an introductory information systems course. Subjects were 9

Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73. invited to participate in the study for extra credit. The undergraduate student population was considered appropriate for assessing our theoretical model due to their likelihood of having a diverse set of social media experiences. Li and Bernoff (2008) suggest that the ideal age group for examining social media usage behavior is between the ages of 18 and 27, making undergraduate students the ideal subjects for the purposes of this study. A total of 441 respondents participated in the survey. Based on inspection of the data, 33 surveys were removed for data entry errors or incomplete survey data, resulting in 408 usable responses. The average respondent was 21.9 years old with 3.3 years of experience with the social media site. In general, the distribution of males and females was even with 207 males and 201 females participating in the study. A majority, 88.8% of respondents, selected Facebook as the social media platform they used most frequently. The remaining platforms ranged from Fantasy Sports (4.9%), Twitter (1%), LinkedIn (.7%), among others (4.6% remaining). On average, respondents indicated they were very experienced with using social media platforms. The research model was analyzed using partial least squares, a component-based structural equation modeling technique that is appropriate for theory exploration. The hypotheses were tested with SmartPLS 2.0 M3 bootstrapping using 408 cases and 5,000 samples to estimate the significance of the path coefficients (Chin, 1998; Hair, Ringle, & Sarstedt, 2011; C. Ringle, Wende, & Will, 2005; C. M. Ringle, Sarstedt, & Straub, 2012). The predictive power of the structural model was assessed via the amount of variance explained for the latent variables. To examine the hypothesized relationships, the t-statistics for the path coefficient and the p-value were evaluated to determine significance at the .05 level, two-tailed. To test the measurement model, reliability (Cronbach’s α) was examined as was convergent and discriminant validity of the constructs (Carmines & Zeller, 1979). For Cronbach’s α, the generally accepted .70 value was applied to indicate good reliability. As a general rule, a construct is considered to demonstrate adequate convergent validity if its value is greater than the 0.50 threshold to demonstrate that the majority of the variance is accounted for by the construct and not other constructs (Fornell & Larcker, 1981).

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Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73. Table 1. Descriptive Statistics

Note: Square Root of AVEs located along diagonal (in bold and italicized) of correlations table N=408

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Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73. Discriminant validity can be assessed by comparing the square root of the average variance extracted (AVE) for each construct to the correlations of all other constructs in the model. If the square root of the AVEs are larger than the correlations, discriminant validity is satisfied (Chin, 1998). All off-diagonal correlations are less than the square root of the AVEs for each construct, suggesting discriminant validity has been satisfied. Based on the analysis of the measurement model, convergent validity, reliability, and discriminant validity is acceptable to proceed with further analysis. Table 1 contains the descriptive statistics, correlations, Cronbach α’s, and square root of the AVEs. Among the control variables four paths were found to be significant with gender being the only insignificant control variable. Older users used social media less frequently which is consistent with prior research on generational demographics. The path between tenure and frequency of use was statistically significant, but negative (β = -.129, p < 0.05). Interestingly, more tenured users less frequently used social media. The finding is consistent with reasoning relative to the importance of evolvabilility in the social media platform. Figure 2 presents the hypothesized model with only the significant paths for clarity purposes.

For social interactions, hypothesis 1 was partially supported, as the path between personalization and user engagement was statistically significant on personal meaning (β = .183, p < .01), but not on individual involvement. While hypothesis 2a was not supported, hypothesis 12

Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73. 2b was fully supported, as the path between critical mass of social acquaintances and user engagement was statistically significant (on Individual Involvement: β = .290, p < .001; on Personal Meaning: β = .156, p < .05). Hypothesis 3 was partially supported, as the path between perceived risk and user engagement was statistically significant on personal meaning (β = -.104, p < .05), but not individual involvement. Hypothesis 4 was not supported. In total, three factors related to social interactions influenced the personal meaning dimension of user engagement. However, only critical mass of social acquaintances influenced how involved a user became on the social media platform. For the technical features, two of the four hypotheses were supported. Hypothesis 5 was supported, as the path between completeness and user engagement was statistically significant (on Individual Involvement: β = .255, p < 0.001; on Personal Meaning: β = .262, p < 0.001). Hypothesis 7 was also supported, as the path between evolvability and user engagement was statistically significant (on Individual Involvement: β = .242, p < 0.001; on Personal Meaning: β = .175, p < 0.01). Hypotheses 6 and 8 were not supported. Lastly, hypothesis 9 was partially supported, as the path between individual involvement and social media usage was statistically significant (β = .259, p < 0.001), but personal meaning was not. While several social interaction factors influenced user engagement via personal meaning, this influence did not ultimately impact social media usage. User engagement, as it relates to influencing user behavior, ultimately rests upon perceptions of intense involvement. To assess the explanatory power of the structural model, we evaluated the amount of variance accounted for in the model’s dependent constructs. For the hypothesized model, three constructs possess R2 values, user engagement (decomposed into Individual Involvement and Personal Meaning) and social media usage. For user engagement, approximately 30.58% of the variance was explained by the social interactions and technical features constructs for individual involvement while 21.05% of the variance was explained for personal meaning. For frequency of use, approximately 31.45% of the variance was explained by individual involvement and the control variables (i.e., age, habit, expertise, and social media tenure).

Post-Hoc Analyses In addition to the hypothesized model, this research explored whether user engagement fully mediated the influence of the user experience on usage. The revised model suggested alternative relationships of importance (see Figure 3).

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Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73.

Critical mass (β = .233, p < 0.001) and perceived risk (β = .114, p < 0.01) both positively influenced social media usage directly. In terms of R2, approximately 30.28% of the variance for user engagement was explained by the social interactions and technical features constructs for individual involvement while 20.71% of the variance was explained for personal meaning. For frequency of use, approximately 38.68% of the variance was explained by critical mass of social acquaintances, perceived risk, individual involvement, and the control variables. Lastly, we examined interaction effects between user engagement and the control variables on social media usage.1 Only the interaction between gender and personal meaning was significant (β = -.132, p < 0.05). For female subjects, hypothesis 9 is fully supported in terms of individual involvement and personal meaning influencing frequency of use. For frequency of use, approximately 34.67% of the variance was explained when adding this interaction to our original model. When the interactions between critical mass and perceived risk with the control variables were included, gender continues to interact with personal meaning (β = -.109, p < 0.05). Interestingly, perceived risk and tenure interacted positively (β = .155, p < 0.05). Also, an interaction between age and personal meaning significantly influenced frequency of use (β = .200, p < 0.05) suggesting hypothesis 9 becomes fully supported as subjects age. For 1

We would like to thank Reviewer 3 for the suggestion to examine moderating effects, as our theoretical model did not incorporate these influences and the results provide additional explanatory value.

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Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73. frequency of use, approximately 44.15% of the variance was explained when adding these interactions to our revised model. According to Hair and colleagues (2011, p. 145), the R2 of the revised model with interactions can be considered moderately robust.

DISCUSSION AND CONTRIBUTIONS This study contributes in several ways. The contribution is the conceptual refinement of the theoretical construct user engagement. As Ray and colleagues (2014) state: “engagement has largely been treated as an abstract concept in information systems research…” (p. 529). The study’s refined definition of user engagement clearly distinguishes engagement as a mental state separate from the user experience and actual behavior. Essentially, user experience (composed of social interactions and the technical features of a social media platform) is an assessment of the actual interactions that occur through a social media platform, which is an antecedent of user engagement that is the resulting psychological state resulting from the experience. User engagement influences a user’s behavior, differentiating the mental state of being engaged from the actual act of engaging. This more clearly separates user engagement as a mental state that precedes usage behavior. This research study extends the work of Ray and colleagues (2014) by incorporating user experience as an antecedent, expands the notion of engagement as psychological state to the user and social media contexts, and decomposes engagement into two sub-components – personal meaning and individual involvement. In doing so, we developed SME Theory and provided an empirical test. Additionally, this research operationalizes usage based on frequency of use. While this measure was adapted from Kankanhalli and colleagues (2005), the measure of frequency of use incorporated information gained from semi-structured interviews. It includes more comprehensive perspectives on frequency in terms of the various ways people use social media. These behaviors were expanded further to include whether these behaviors were generalized, and/or directed toward friends or social acquaintances, capturing the variety of users one might interact with using a social media platform. While this study refines the frequency of use measure, researchers should also consider other measures of usage to determine whether SME Theory has greater explanatory power (Burton-Jones & Straub, 2006). This study also challenges the conventional wisdom that social media usage is driven solely by the critical mass of social acquaintances. In particular, the surprising result that perceived risk will positively influence usage is also interesting given its counter-intuitive nature. Why would someone contribute more to an environment that is risky? Research on adolescence and risk behaviors has found that even when adolescents are well informed of the dangers of engaging in a certain behavior they will continue to act in a similar manner (Greene et al., 2000). As Greene and colleagues (2000) argue, such behavior may be motivated by risk-seeking personality traits as well as susceptibility to boredom and adventure-seeking. Given that college is a time when many undergraduate students test boundaries and develop new experiences, the subjects might be more risk-seeking than the general population. Such reasoning may explain why college students continue to use social media platforms that may be risky to their future success regardless of the warnings about social media from media outlets. Interestingly, hypothesis 2 was not supported, as access to social resources did not have a significant impact on user engagement. Other social media platforms might result in this relationship being significant if the purpose of the platform and its usage is different than the 15

Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73. more socially-oriented platform investigated in this study, predominantly Facebook based on the descriptive statistics. Specifically, social media platforms that are task or organizationallyoriented may produce different results. Therefore, further research should examine SME Theory across a variety of social media platforms. Transparency was also found to be not significant. We believe transparency may prove to be event-driven where its influence is only present when an organization’s ethics are called into question. While risk was found to be positively related to usage behavior, could a negative event result in transparency having a significant influence on either user engagement or social media usage – albeit temporarily? This study also demonstrates the importance of the social media platform’s capabilities to drive user engagement and subsequent usage. While flexibility of features did not influence user engagement, it is possible evolvability captures a similar version of the argument in that users expect features to change over time and that the organization will anticipate these changes and will provide new features. In this way, the overarching social media platform is flexible but from a longitudinal perspective. In terms of integration, we reviewed the interview transcripts from the pre-survey quality control check and found that it is the centralization of content rather than the integration of content that is important to user engagement. For instance, one interviewee noted: “The biggest impact with Facebook is when they changed Facebook setup (or when they polled me on what I like). The way Facebook looks, on the homepage they used to give you just status updates, now they give you the whole deal, everything is mixed together.” The results of this study also highlight the strong influence of critical mass on both user engagement and usage behavior. Given the social nature of Facebook, it is not surprising that social relationships among users strongly influenced user engagement and usage. The literature suggests that users seek emotional support and tangible benefits from family and friends (boyd, 2007; Dickinger et al., 2008; Hsu & Lin, 2008). This research has found that users increase their usage when a critical mass of social acquaintances are known to the user (Hsu & Lin, 2008). Motivations behind usage focus around the personal meaning obtained when involving themselves within these social structures. However, the results of this study suggest that the personal meaning derived from the platform is gender specific while the perception that a user wishes to become involved has an impact regardless of gender.

PRACTICAL IMPLICATIONS Organizational interest in social media is growing as organizations encounter fierce competition, uncertain economic environments, and a growing user base accustomed to active participation (Li & Bernoff, 2008). This study provides several implications for social media platform organizations. First, critical mass of social acquaintances was a significant predictor of user engagement as well as social media use. Social media platforms provide organizations with the ability to record the social interactions among users and to identify their social structures. Techniques such as social network analysis can be used to identify cliques and users that possess unique characteristics that make them influential (Wasserman & Faust, 1994). Organizations 16

Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73. should ensure these influential users are involved to sustain their participation and influence their social acquaintances. Second, the results of this study suggest the importance of the social media platform to influence user engagement. Specifically, completeness and evolvability indicate that organizations cannot simply build a social media platform and expect users to use it. Facebook, as an organization, has been tremendously successful due to its ability to provide users with access to information at any level of specificity while introducing new features and experiences. For instance, Facebook allows third-party developers to create applications to introduce new opportunities for users. Evolutionary actions such as these help introduce new ways in which users can become actively involved in Facebook rather than passive users.

LIMITATIONS Limitations for this study focus on three areas. First, the study focused on exploring social media usage and targeted respondents between the ages of 18 and 27. Additionally, respondents in this study focused their responses almost exclusively on Facebook. Therefore, future research should explore whether similar findings can be generalized to different generational profiles and social media platforms. Although the underlying theoretical framework is predicted to be generalizable, the results may be different. Second, criticism towards the use of subjective measures limits the potential validity of the dependent variable (Podsakoff et al., 2003). This study mitigated these validity concerns by collecting data in two time periods. Respondents were asked to indicate their usage behavior during two weeks following the first survey. Unfortunately, objective usage data from the social media platforms selected by the subjects of this study required organizational participation and could not be collected. Additionally, this study captures a single data point for social media usage. While the survey was designed to mitigate common method bias, longitudinal data analysis could provide further validation of social media usage. Such an analysis can also serve to test the linearity of the SME Theory model. It is possible that social media usage influences user experience and engagement over time and future research should consider examining SME Theory from a longitudinal perspective. Lastly, additional organizational influences were not examined in this study. The purpose of this study was to identify the factors that influenced a user’s experience and ultimately social media usage. It is possible that an organization’s social media maturity and presence (Geyer & Krumay, 2015) may influence its ability to manage a social media platform which would subsequently influence usage. However, future research should examine a wide variety of organizations with different degrees of maturity and within platform interactivity to determine if such an influence is significant.

CONCLUSION The future of business is increasingly reliant on UGC to create new forms of value and competitive advantage. In the co-created business model, value is derived mutually between the users who benefit from socially interacting with other users and organizations that maintain a captive audience for advertising and business intelligence. Competitive advantage is maintained for an organization as long as users continue to co-create with the organization and at a higher 17

Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73. level than its rivals. In the future, the ability to leverage users as key strategic stakeholders may be the difference between an organization’s survival and its obsolescence in an increasingly competitive and social world.

REFERENCES Ahern, S., Eckles, D., Good, N. S., King, S., Naaman, M., & Nair, R. (2007). Over-exposed?: privacy patterns and considerations in online and mobile photo sharing. Paper presented at the Proceedings of the SIGCHI conference on Human factors in computing systems. Barki, H., & Hartwick, J. (1994). User Participation, Conflict, and Conflict Resolution: The Mediating Roles of Influence. Information Systems Research, 5(4), 422-438. Barley, S. R. (1996). Technology as an Occasion for Structuring: Evidence from Observations of CT Scanners and the Social Order of Radiology Departments. Administrative Science Quarterly, 38, 78-108. Battista, J., & Almond, R. (1973). The development of meaning in life. Psychiatry, 36, 409-427. boyd, d. m. (2007). Why youth (heart) social network sites: The role of networked publics in teenage social life. MacArthur foundation series on digital learning–Youth, identity, and digital media volume, 119-142. Brown, C., & Magill, S. (1998). Reconceptualizing the context-design issue for the information systems function. Organization Science, 9(2), 176-194. Burton-Jones, A., & Straub, D. (2006). Reconceptualizing system usage: An approach and empirical test. Information Systems Research, 17(3), 228-246. Carmines, E. G., & Zeller, R. A. (1979). Reliability and validity assessment (Vol. 17): Sage. Chaudhuri, A. (1997). Consumption emotion and perceived risk: A macro-analytic aproach. Journal of Business Research, 39, 81-92. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336. Claussen, J., Kretschmer, T., & Mayrhofer, P. (2013). The effects of rewarding user engagement: The case of Facebook apps. Information Systems Research, 24(1), 186-200. Debats, D. L. (1998). Measurement of personal meaning: The psychometric properties of the life regard index. In I. B. Weiner (Ed.), The Human Quest for Meaning (pp. 237-259). Mahwah: Lawrence Erlbaum Associates. Di Gangi, P. M., & Wasko, M. (2009). Open innovation through online communities. In W. R. King (Ed.), Annals of Information Systems: Knowledge Management and Organizational Learning. New York, NY: Springer. Dickinger, A., Arami, M., & Meyer, D. (2008). The role of perceived enjoyment and social norm in the adoption of technology with network externalities. European Journal of Information Systems, 17, 4-11. Erat, P., Desouza, K. C., Schäfer-Jugel, A., & Kurzawa, M. (2006). Business customer communities and knowledge sharing: exploratory study of critical issues. European Journal of Information Systems, 15(5), 511-524. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 39-50.

18

Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73. Geyer, S., & Krumay, B. (2015). Development of a Social Media Maturity Model–A Grounded Theory Approach. Paper presented at the 48th Hawaii International Conference on System Sciences, Kauai, Hawaii. Goodwin, C. (1996). Communality as a dimension of service relationships. Journal of Consumer Psychology, 5(4), 387-415. Greene, K., Krcmar, M., Walters, L. H., Rubin, D. L., Hale, J., & Hale, L. (2000). Targeting adolescent risk-taking behaviors: The contributions of egocentrism and sensationseeking. Journal of Adolescence, 23, 439-461. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. The Journal of Marketing Theory and Practice, 19(2), 139-152. Hsu, C., & Lin, J. C. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation. Information & Management, 45, 6574. Hwang, M. I., & Thorn, R. G. (1999). The effect of user engagement on system success: a metaanalytical integration of research findings. Information & Management, 35(4), 229-236. Jensen, T. B., & Aanestad, M. (2007). How Healthcare Professionals "Make Sense" of an Electronic Patient Record Adoption. Information Systems Management, 24(1), 29-42. Joinson, A. N. (2008). Looking at, looking up or keeping up with people?: motives and use of facebook. Paper presented at the Proceedings of the SIGCHI conference on Human Factors in Computing Systems. Kankanhalli, A., Tan, B. C. Y., & Wei, K. K. (2005). Contributing knowledge to electronic knowledge repositories: An empirical investigation. MIS Quarterly, 29(1), 113-143. Kaplan, A. M., & Haenlein, M. (2010). Users of the world unite! The challenges and opportunities of social media. Business Horizons, 53(1), 59-68. Kettinger, W. J., & Lee, C. C. (1994). Perceived Service Quality and User Satisfaction with the Information-Services Function. Decision Sciences, 25(5-6), 737-766. Korn, J. (2001). Design and delivery of information. European Journal of Information Systems, 10(1), 41-54. Laurent, G., & Kapferer, J.-N. (1985). Measuring consumer involvement profiles. Journal of Marketing Research, 41-53. Lederer, S., Hong, J. I., Dey, A. K., & Landay, J. A. (2004). Personal privacy through understanding and action: five pitfalls for designers. Personal and Ubiquitous Computing, 8(6), 440-454. Lehmann, J., Lalmas, M., Yom-Tov, E., & Dupret, G. (2012). Models of user engagementUser Modeling, Adaptation, and Personalization (pp. 164-175): Springer Berlin Heidelberg. Lessig, L. (2008). Remix: Making Art and Commerce Thrive in the Hybrid Economy. New York: The Penguin Press. Li, C., & Bernoff, J. (2008). Groundswell: Winning in a world transformed by social technologies. Boston, MA: Harvard Business School Press. Loebbecke, C. (2007). Use of innovative content integration information technology at the point of sale. European Journal of Information Systems, 16(3), 228-236. Mishra, D. P., Heide, J. B., & Cort, S. G. (1998). Information asymmetry and levels of agency relationships. Journal of Marketing Research, August 1998, 277-295. Mittal, B., & Lassar, W. M. (1996). The role of personalization in service encounters. Journal of Retailing, 72(1), 95-109. 19

Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73. Muson, J. M., & McQuarrie, E. F. (1987). The factorial and predictive validities of a revised measure of Zaichkowsky's Personal Involvement Inventory. Educational and Psychological Measurement, 47, 773-782. O'Brien, H. L., & Toms, E. G. (2008). What is user engagement? A conceptual framework for defining user engagement with technology. Journal of the American Society for Information Science and Technology, 59(6), 938-955. Parasuraman, A., Berry, L. L., & Zeithaml, V. A. (1991). Perceived service quality as a customer-based performance measure: An empirical examination of organizational barriers using an extended service quality model. Human Resource Management, 30(3), 334-364. Pine Jr., B. J., & Gilmore, J. H. (1999). The Experience Economy: Work is Theatre & Every Business a Stage. Boston: Harvard Business School Press. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879. Prahalad, C. K., & Ramaswamy, V. (2004). The future of competition: Co-creating unique value with customers. Boston, MA: Harvard Business School Press. Ray, S., Kim, S. S., & Morris, J. G. (2014). The central role of engagement in online communities. Information Systems Research, 25(3), 528-546. Read, B. (2006). Think before You Share. Chronicle of Higher Education, 52(20). Ringle, C., Wende, S., & Will, S. (2005). SmartPLS 2.0 (M3). Hamburg. Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). Editor's comments: A critical look at the use of PLS-SEM in MIS quarterly. MIS Quarterly, 36(1), iii-xiv. Santosa, P. I., Wei, K. K., & Chan, H. C. (2005). User involvement and user satisfaction with information-seeking activity. European Journal of Information Systems, 14(4), 361-370. Simon, H. (1991). Bounded rationality and organizational learning. Organization Science, 2, 125-134. Steinfield, C., DiMicco, J. M., Ellison, N. B., & Lampe, C. (2009). Bowling online: social networking and social capital within the organization. Paper presented at the Proceedings of the 4th international conference on Communities and technologies. Verplanken, B., & Orbell, S. (2003). Reflections on Past Behavior: A Self‐Report Index of Habit Strength1. Journal of Applied Social Psychology, 33(6), 1313-1330. von Hippel, E., & Katz, R. (2002). Shifting Innovation to Users via Toolkits. Management Science, 48(7), 821-833. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. New York, NY: Cambridge University Press. Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85-102. doi:10.1287/isre.1050.0042 Xu, D. J., Liao, S. S., & Li, Q. (2008). Combining empirical experimentation and modeling techniques: A design research approach for personalized mobile advertising applications. Decision Support Systems, 44, 710-724. Zaichkowsky, J. L. (1985). Measuring the involvement construct. Journal of Consumer Research, 12, 341-352.

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Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73.

APPENDIX Semi-Structured Interview Questions (Pre-Survey Quality Control) 1. How do you participate in most favorite/ least favorite social media site? 2. Why do you participate? 3. How would you describe your experiences in most favorite/ least favorite social media site? 4. What attributes of most favorite/ least favorite social media site are important to you and why? 5. Overall, what about your experiences and the attributes of most favorite/ least favorite social media site influences your participation and why? Control Variables Construct (Prior Cronbach’s α; Source Literature) Age Gender Tenure Expertise

# Item (Loadings) A1 G1 T1 E1 (.945) E2 (.956)

Habit (α = .89 - .92; Verplanken and Orbell (2003)) Added based on SemiStructured Interviews

H1 (.894) H2 (.829) H3 (.873) H4 (.771) H5 (.870) H6 (.809)

How old are you (in years)? What is your gender? How long (in months) have you used ___? How would you rate your level of expertise on the topics discussed in ___? How would you rate your level of expertise using the technology/ features of ___? ___ is something I do automatically. ___ is something I do without having to consciously remember. ___ is something I do without thinking ___ is something that would require effort not to do it. ___ is something I start doing before I realize I'm doing it. ___ is something I would find hard not to do.

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Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73. Social Interactions Construct (Prior Cronbach’s α; Source Literature) Personalization (α = .90; Kettinger et al. (2004))

Access to Social Resources (α = .80 - .90; Dickinger et al. (2008), Feller et al. (2008), and Hsu et al. (2008))

Critical Mass of Social Acquaintances (α = .80 .90; Dickinger et al. (2008), Feller et al. (2008), and Hsu et al. (2008)) Risk (α = Unknown; Chaudhuri (1997))

# Item (Loadings) P1 (.833) P2 (.826) P3 (.735) P4 (.798) ASR1 (.713) ASR2 (.571) ASR3 (.849) ASR4 (.900) ASR5 (.898) CM1 (.855) CM2 (.909) CM3 (.631) R1 (.924) R2 (.934) R3 (.902) R4 (.924)

Transparency (α = .91; Wixom and Todd (2005))

TR1 (.782) TR2 (.757) TR3 (.829) TR4 (.826)

___ users give me individual attention. ___ users give me personal attention. ___ users have my best interests at heart. ___ users understand my specific needs. ___ enables users to contact each other. ___ enables users to gain access to the experiences of others. ___ allows information to be readily available to users. ___ makes information very accessible. ___ makes information easy to access. People who are important to me participate in ___. People that influence my behavior participate in ___. I use ___ because my friends use it. What are the chances that I can be harmed if I contribute to ___? To what extent are there risks associated with me using ____? What are the chances that ___ may be harmful or injurious to me? On the whole, considering all sorts of factors combined, what are the chances that contributing to ___ is risky? In terms of transparency, I would rate ___ users highly. Overall, ___ users are very transparent in their interactions with me. Overall, I would give openness of ___ users a high rating. Overall, ___ users are very open in their interactions with me.

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Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73. Technical Features Construct (Prior Cronbach’s α; Source Literature) Completeness (α = .90; Wixom and Todd (2005)) Flexibility (α = .86; Wixom and Todd (2005))

Evolvability Developed based on adaptation from: Wixom and Todd (2005) and Xu et al. (2008)

Integration (α = .89; Wixom and Todd (2005))

# Item (Loadings) C1 (.879) C2 (.873) C3 (.866) F1 (.854) F2 (.810) F3 (.878) EV1 (.825) EV2 (.686) EV3 (.805) EV4 (.815) EV5 (.804) EV6 (.779 I1 (.911) I2 (.843) I3 (.883)

User Engagement Construct (Prior Cronbach’s α; Source Literature) Individual Involvement (α = .93; Barki and Hartwick (1994))

___ provides me with a complete set of information. ___ provides me with comprehensive information. ___ provides me with all the information I need. ___ can be adapted to meet a variety of needs. ___ can flexibly adjust to new demands or conditions. ___ is versatile in addressing needs as they arise. ___ shapes the content/services based on users' needs and preferences. I feel that ___ evolves with users to create personalized experiences. The content/services in ___ evolve over time to meet users' needs and preferences. ___ adapts to the users' shifting needs and interests. ___ functions/services meet my most recent needs and interests. ___ produces functions/services that meet my current needs and interests. ___ effectively integrates data from different areas of the web site. ___ pulls together information from different areas within the web site. ___ effectively combines data from different areas of the web site.

# Item (Loadings) II1 (.860) II2 (.745) II3 (.831) II4 (.728) II5 (.845) II6 (.857) II7 (.773) II8 (.661)

Unimportant:Important Of no concern to me:Of concern to me Irrelevant:Relevant Boring:Interesting Doesn't matter to me:Matters to me Insignificant:Significant Nonessential:Essential Unexciting:Exciting

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Di Gangi, P. M., & Wasko, M. M. (2016). Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage. Journal of Organizational and End User Computing (JOEUC), 28(2), 53-73. Personal Meaning (α = .86; Debat (1998) and Zaichkowski (1985))

Social Media Usage Construct (Prior Cronbach’s α; Source Literature) Frequency of Use (α = .85; Kankanhalli et al. (2005))

PM1 (.857) PM2 (.784) PM3 (.753) PM4 (.759) PM5 (.836)

My experience with ___ is deeply fulfilling. When I look to ___, I feel satisfaction of really having accomplished something. I feel that I am really going to attain what I want from ___. I get so excited by what I am doing in ___ that I find new stores of energy I didn't know that I had. I have a real passion for ___.

# Item (Loadings) FREQ1 (.787) FREQ2 (.777) FREQ3 (.719) FREQ4 (.767) FREQ5 (.852) FREQ6 (.831) FREQ7 (.779) FREQ8 (.795) FREQ9 (.819)

What is your overall frequency of using ___ to contribute content to other users? What is your overall frequency of using ___ to retrieve content from other users? What is your overall frequency of using ___ to explore content from other users? What is your overall frequency of using ___ to contribute content to your friends? What is your overall frequency of using ___ to retrieve content from your friends? What is your overall frequency of using ___ to explore content from your friends? What is your overall frequency of using ___ to contribute content to acquaintances? What is your overall frequency of using ___ to retrieve content from acquaintances? What is your overall frequency of using ___ to explore content from acquaintances?

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