Sage Journals: Discover world-class research

Abstract

The purpose of this research is to describe the various roles of practitioners who use social media communication through a scale. Human organizations are role systems in which patterned behaviors take place. Literature and theory spanning multiple occupational fields guided the development of items. Through a multi-method approach and six-step process, we created a set of social media communicator roles resulting in a five-factor model: customer service provider, mobilizer, information disseminator, researcher, and community builder.

Keywords

social media communicator scale development roles role theory mixed methods

Social media communication has evolved to such a great degree that it has received organizational resources and role status. Communicators who use social media can enact communication behaviors stemming from their training in one of several fields such as marketing, communication, business, advertising, sales, journalism, and public relations (Beeler, Lebovits, & Bishop, 1983; Verhoeven, Tench, Zerfass, Morena, & Verčič, 2012), which may also mean that scholars primarily interpret social media communication from the perspective of their intellectual field.

It is theoretically and practically relevant to explore the breadth of construct and empirically identify the various roles of social media communicators (SMCs) due to the increased interest in social media. First, social media is an increasingly growing area of research for scholars, especially survey research (Khang, Ki, & Ye, 2012; Zhang & Leung, 2015). Several scholars, however, have criticized the atheoretical nature of social media research (Treem & Leonardi, 2012; Zhang & Leung, 2015). Zhang and Leung (2015) critiqued research on social media arguing, “Due to the complex and developing features of social network sites, the improvement of measurements requires a clearer definition and more systematic and theory-based research design” (p. 14). The employment of formal measurement procedures enables scholars to more systematically develop models predicting how communicators vary in their social media strategies and whether those roles influence variables such as organizational commitment, job satisfaction, and status (Khang et al., 2012).

Such measures are also necessary because communication on social media channels is comparatively different to other forms of online communication. Social media communication technologies are unique in comparison with computer-mediated communication technologies because such communication practices tend to be more visible in comparison with communication via email or messaging apps, messages are persistent because they are more likely to sustain over time, content is easily edited, and such communication behaviors tend to center on maintaining social ties (Treem & Leonardi, 2012).

And finally, social media tools are influencing communication, relational, and organizational dynamics. The impact of social media communication technologies is resulting in the widespread adoption of social media skills by practitioners, including the hiring of SMCs with knowledge of how to manage such interactions (Macnamara & Zerfass, 2012; Wigley & Zhang, 2011; Wright & Hinson, 2008). In addition, communication professionals working in occupations such as journalism, business, marketing, public relations, and advertising are repositioning themselves to be relevant and competitive by adopting social media skills (Efimova & Grudin, 2008; Porter, Sweetser, Chung, & Kim, 2007).

We sought scholarly roles literature from communication, social psychology, occupational sociology, and organizational sociology to develop and build a social media communicator roles (SMCR) measure. In this study, we defined the SMCR as the recurring behaviors of people that occupy a social media communication position within a social system. Communicators use their social skills to transmit messages to link people together and build relationships (Dance, 1970). We carried out qualitative interviews and a quantitative survey of SMCs to develop the theoretical model because no such validated scale exists. The creation of the SMCR scale involved six steps: (1) review of literature and theory, (2) qualitative interviews to determine whether additional items should be included or reframed, (3) expert advice from researchers and communication practitioners, (4) a pretest with practitioners, (5) a quantitative survey of professionals, and (6) an exploratory factor analysis (EFA) was applied to determine the structure of measure. The 60 items were reduced to a 16-item, five-factor model.

Social Media Communication

Organizational representatives perceive social media platforms as critical tools in their communication and marketing efforts (Lovejoy & Saxton, 2012; Wright & Hinson, 2011). Social media practices have evolved from informal to formal communication practices as evident based on the number of organizations adopting social media policies (Macnamara & Zerfass, 2012; Mergel & Bretschneider, 2013). The vast number of social networks requires the communicator to be able to collect, filter, assess, and organize information from various sources. Practitioners contribute information related to an organization’s goals, expertise, missions, and functions on such platforms (Leonardi, Huysman, & Steinfield, 2013). This type of work environment often involves learning group and public communication skills, dealing with the blurring of professional and personal lives, and meeting the role expectations of many different constituents (Efimova & Grudin, 2008; Mergel & Bretschneider, 2013). Essentially, SMCs are people who publish content influenced by professional expectations. This complex role consists of observing cultures within networks, experimenting with communication strategies, cultivating trust with niche audiences on different platforms, and using various tools to measure the success of their efforts. These responsibilities, however, may vary depending upon an organization’s needs, culture, and perceptions of social media (Quinton & Fennemore, 2013).

We sought to identify a fair universe of items to represent the SMCR construct. We searched for items and potential roles enacted by people who use social media spanning multiple fields including advertising, journalism, marketing, health communication, and other specializations to assist in the development of an SMCR measure. For example, several scholars have contributed theoretically by developing social media and blog use measures that embody specific occupational fields (e.g., Hedman & Djerf-Pierre, 2013; Porter, Sweetser, & Chung, 2009).

Overall, a review of the social media literature indicates the possibility of several dimensions, which include using it for promotional, research, community building, and dissemination purposes. Professional general applications include using social media to monitor interactions and metrics; engaging in two-way communication (e.g., Auger, 2013; Taylor & Kent, 2010); providing emotional support (e.g., Knight & Carpenter, 2012); promoting individual work, brands, and organizations (e.g., Efimova & Grudin, 2008); raising public awareness (e.g., Parmelee, 2013); cultivating relationships and community communication (e.g., Auger, 2013; Carim & Warwick, 2013); monitoring competition, individuals, groups, and organizations (e.g., Carim & Warwick, 2013; Wigley & Zhang, 2011); communicating with customers, clients, and employees (e.g., Efimova & Grudin, 2008; Gallaugher & Ransbotham, 2010; Greer & Ferguson, 2011); networking (e.g., Cogburn & Espinoza-Vasquez, 2011; Efimova & Grudin, 2008); demonstrating expertise (e.g., Efimova & Grudin, 2008); serving customers, audiences, and clients (e.g., Efimova & Grudin, 2008; Gallaugher & Ransbotham, 2010; Knight & Carpenter, 2012); soliciting volunteers and donations (e.g., Cogburn & Espinoza-Vasquez, 2011); sharing personal opinions and challenging mainstream media; finding story ideas and sources for news stories (e.g., Messner, Linke, & Eford, 2012; Parmelee, 2014); collaborating on content creation; verifying the accuracy of information (e.g., Messner, Linke & Eford, 2012; Parmelee, 2014); distributing information on news, crises, and events communication (e.g., Auger, 2013; Carim & Warwick, 2013; Greer & Ferguson, 2011); responding to customer’s needs (e.g., Taylor & Kent, 2010); responding to criticism and mainstream media (e.g., Knight & Carpenter, 2012); and offering thanks or recognition (e.g., Auger, 2013).

Role Theory

We consulted role theory to determine how to frame roles conceptually and operationally. In organizations and occupations, people align themselves with certain roles by gravitating toward duties reflecting those roles (Jackson, 1998). Biddle (1986) stated that role theory is concerned with the “. . . patterned and characteristic social behaviors, parts, or identities that are assumed by social participants, and scripts or expectations for behavior that are understood by all and adhered to by performers” (p. 68). Concepts often examined in this line of research are role, social position, status, expectations, consensus, and conformity.

Roles literature has an intuitive appeal. For many decades, the roles literature has been one of the most studied lines of research. Dozier and Broom (1995) said, “Perhaps no concept has proven so theoretically and empirically useful as the organization role” (p. 3). Communication scholars, however, diverge in their theoretical interpretations of roles resulting in conceptual and operational variations, which make it challenging to build on this foundation. For example, roles can be inferred in many different ways: an organizational or occupational perspective; perceptions or behaviors; and tasks, responsibilities, or normative expectations (Biddle, 1979; Dozier, 1992).

As previously mentioned, variations may hinge on whether scholars interpret the construct from an organizational or occupational perspective. In public relations, roles have been viewed through both occupational (e.g., Beurer-Züllig, Fieseler, & Meckel, 2009; Gitter, 1981; Mellado & Barría, 2012) and organizational lenses (e.g., Broom & Dozier, 1986; Dozier, 1992), whereas research from journalism scholars suggests a preference for an occupational interpretation (e.g., Hanitzsch, 2005; Weaver, Beam, Brownlee, Voakes, & Wilhoit, 2007).

Public relations scholars have taken a more task-based (e.g., “I produce brochures, pamphlets, and other publications.”) and a general behavioral approach (e.g., “I encourage management participation when making the important public relations problems.”) to operationally define roles, whereas journalism scholars have primarily adopted an occupational normative interpretation (e.g., “Be an adversary of business by being constantly skeptical of their actions.”) (Dozier, 1992; Dozier & Broom, 1995; Hanitzsch, 2005; Ramaprasad & Kelly, 2003; Weaver et al., 2007). This intellectual variation presents an interesting challenge for scholars who seek to study communication behaviors among social media practitioners.

This study’s intent was to employ the decades of research on roles and theory as a guide in item development. We further consulted several thought leaders to assist in item wording and framing (Biddle, 1979, 1986; Kahn, Wolfe, Quinn, & Snoek, 1964; Katz & Kahn, 1978; Linton, 1936; Merton, 1968; Newcomb, 1950). One theoretical issue is that norms and roles are often conceptually confused with one another according to the previously mentioned authors. Norms and values represent the dominant functions that exist within a social system. Norms are overarching umbrellas that encourage solidarity among an occupational group or organization, whereas roles work should target individuals. Based on the logic presented in several seminal books from Katz, Kahn, and their contributors, items measuring media or journalistic role conceptions focus on occupational normative expectations. Norms reflect guides about what should be and should not be done. This interpretation of role theory often takes place within occupational science in which scholars define roles based on what the occupation should look like. Jackson (1998) said, “Role theory requires a ‘given’, or normative structure to social roles that are based on an assumed consensus among members of society about what that position entail” (p. 59). For example, the question leading into one of the most often applied media roles scale by Weaver and coauthors asks journalists, “I’d like to ask you how important you think a number of things are that the news media do or try to do today.” In early work on roles in public relations, Dozier (1992) highlighted earlier similar theoretical differences within the public relations roles literature. He argued that Ferguson’s (1979) roles scale measured norms, while Broom and Smith’s (1979) selected activity variables to measure roles. In addition, the normative expectation or perceptional interpretation may be limited in its predictive scientific utility because many sociological influences often affect the roles enacted (Shoemaker & Reese, 2014). Much research on journalism roles has been comparative (i.e., medium, gender, nations, society, journalism training) rather examining the relationship between roles and other perceptions or behaviors likely because items representing role dimensions highlight ideals rather than their actual behavior.

Today, public relations scholars’ application of role theory primarily falls in line with Katz, Kahn, and their coauthors’ interpretation of roles. Dozier and Broom (1995) cite Katz and Kahn’s (1978) definition of role behaviors as recurring activities. Each role serves the overall functions of an organization. Existing operational definitions appear to focus on the extent that public relations practitioners possess the power to choose the most appropriate approach to solve public relations problems for an organization. Dozier (1992) said, “Practitioner roles are conceptually and empirically related to participation in management decision making” (p. 341). Activity items within the field of public relations appear to be broken down into the areas of tasks and responsibilities. Tasks entail specific actions that are specific to the work role such as writing press releases, whereas responsibilities reflect broader aggregate behaviors such as analyzing information or developing objectives. Tasks and responsibilities differ in their specificity, but they are both behaviorally focused (Biddle, 1979; Cunningham, 1996; Dierdorff & Morgeson, 2007).

Based on a review of theory and literature, we created scale items based on an organizational service perspective by framing measures emphasizing the responsibilities of practitioners who use social media for professional purposes. As previously mentioned, roles can be interpreted by activities performed by people residing within certain positions (i.e. reporters, editors, managers) (Biddle, 1979; Burk, 1980; Kahn et al., 1964). Example items on the questionnaire included “monitor communication behaviors of people and organizations” and “disseminate information on events, news, or issues.” Our goal of this research was to develop a scale that could serve multiple fields over time and across contexts because theoretical concepts require some level of abstraction (Reynolds, 1971).

Methods

We carried out the development of the SMCR scale because evidence suggests that there are distinct roles that exist for people who use social media communication tactics. Scale development research does not present hypotheses and research questions. It instead explores factors (Pett, Lackey, & Sullivan, 2003). Proper scale development procedures involve both qualitative and quantitative survey work to identify breadth and content for a scale and to validate its structure (DeVellis, 2012). The development of the initial set of items consisted of a review of a comprehensive list of role items, expert advice, and semi-structured interviews with SMCs.

Qualitative Research

We sought to identify potential items missing from the list of items or roles not apparent in the literature and to check for internal validity. We searched for members of social media professional groups on LinkedIn who had email contact information publicly available on the web. We interviewed a total of 10 SMCs. Ideally, the number of qualitative interviews for this type of research should range from 5 to 12 interviews in order to closely investigate and identify themes based on their opinions (Bertaux, 1981; Guest, Bunce, & Johnson, 2006). We analyzed the interview transcripts using a qualitative technique in which the answers are coded granularly. Specifically, we searched for patterns relative to the goal of identifying scale items and role dimensions.

Qualitative respondents were similar in position, experience, and education, but they differed in the types of organizations they represented. We chose participants who had leadership positions related to social media communication with titles such as social media coordinator, editor, manager, specialist, and director with an average of 4 years of experience working as an SMC. Four females and six males possessed a formalized education background in journalism, marketing, media studies, fine arts, advertising, and mass communication. They worked for several different types of organizations such as news, health and wellness, agricultural, health insurance, government, restaurant, specialized products, and non-profit.

We asked the participants a total of 10-multi-part questions in order to explicate their roles construct. The questions relevant to this study included the following: (1) “What do you think the role or function of the social media communicator is within society?”; “Could you elaborate on the ‘mentioned’ role?”; (2) “What is your role as a social media communicator within your organization?”; (3) “What tasks do you do as a SMC?”; “Could you elaborate (on mentioned specific roles)?”; (4) “What is part of your job makes you the happiest?”; “What parts of your work do you find most stressful?”; and (5) “What are the expectations of your job?”; “How does your employer evaluate your job performance?”

Expert Feedback

Next, an expert committee (i.e. six researchers and social media professionals who studied this topic) scrutinized the scale stemming from literature, theory, and interviews to assess content validity. We asked that group of people to assess internal validity by evaluating the items for redundancy and gaps. Based on feedback about conciseness, grammar, and face validity, we reworded some items.

Pretest

We administered a pretest to eight professional communicators and survey experts to assess issues related to question clarity and questionnaire structure. We made a few adjustments to the online questionnaire to address flow issues.

Based on the literature review and qualitative research, we included 60 items on the questionnaire to build the scale.¹ We expected up to eight potential dimensions: promotional (e.g., “Create awareness of organization goals, messages, and strategies.”), research (e.g., “Identify and collect information of value to communities of interest to my organization.”), community building (e.g., “Involve the public in conversations on behalf of the organization.”), dissemination (e.g., “Disseminate information on events, news, or issues.”), curation (e.g., “Aggregate information relevant to stakeholders or audiences.”), customer service (e.g., “Respond to concerns or comments related to the organization.”), verification (e.g., “Evaluate the authenticity of posts made by other content creators.”), and recruitment (e.g., “Recruit people to give back to important causes.”).

Quantitative Survey

To establish internal validity, we created a list of communicators for the quantitative survey. The purpose of sampling frame process was to survey high-quality and active SMCs spanning a diversity of fields. The development of this list was challenging for several reasons: the diversity of organization types that employ a small number of SMCs, many organizations do not provide public directories of their employees, SMCs do not often publicly share their email addresses on the web, and no national social media professional organization was found to aid in accessing members of this specialized group of communicators.

As a result, the sampling frame creation process involved multiple steps. First, we relied on reputable social media experts’ Twitter lists of social media professionals to develop the sampling frame.² We reduced the number of people on these lists due to redundancy of names, and the final list included individuals who professionally handle social media for organizations rather than people who just post information about social media trends such as an academic. We used other top SMC lists³ and LinkedIn searches to increase the number of people. The involved researchers searched for email addresses and verified the email addresses of the professionals or requested their email address via Twitter through @mentions or direct message. This process resulted in 416 people with a response rate of 30.3%.

We administered the quantitative survey using Qualtrics, Inc., over a period of 4 weeks. We chose the web survey because this national sample was most likely accessible via the Internet making this approach most suitable (Dillman, 2007; Greenlaw & Brown-Wetly, 2009). The questionnaire took approximately 15 min to complete with an incentive for a chance to win a US$50 gift card. The main question on the SMCR 5-point scale ranging from never to always asked the extent to which each role variable describes their behavior as an SMC as a communication professional who serves online communities, audiences, readers, publics, stakeholders, and leadership.

The SMCs were diverse and had several years of experience in social media. Males (47.4%) and females (52.6%) were nearly equally represented. The age ranged from 22 to 64 years, with an average age of 37.6 years. The respondents were mostly White (72.3%), followed by Asian (8.5%), Hispanic or Latino (7.4%), Black or African American (3.8%), Native American or Alaskan Native (1.1%), and Other (3.8%). Highest education levels were high school or less (4.5%), associates/vocational degree (5.3%), bachelor’s degree (44.7%), and master’s degree (18.2%). Most participants were from the United States (87.4%), Canada (3.2%), or the United Kingdom (3.2%). They worked an average of 6.2 years as a professional SMC. Titles of respondents indicate that most respondents were social media leaders. And organization size was a median of 150 people. Respondents were distributed across many organization types, but most respondents were from a corporation (22.9%), news organization (20.8%), or marketing agency (17.7%).

Scale Development Procedures

We followed scale development procedures recommended by methodologists to statistically create the scale. To determine whether exploratory analysis was appropriate, we applied Bartlett’s test of sphericity to estimate the probability that there was an identity matrix with 1s in the diagonal and 0s in the off-diagonals. The matrix would be rejected if p ⩽ .001. The Kaiser–Meyer–Olkin (KMO) is a measure of sampling adequacy. It compares the magnitude of the calculated coefficients to the partial correlation coefficients. The KMO measure (⩽.60) is useful in determining whether it is appropriate to proceed with factor analysis (Kaiser, 1974; Pett et al., 2003; Tabachnick & Fidell, 2007).

After confirming the Pearson correlation matrix was factorable, we submitted the matrix for EFA. We reviewed the responses using principal axis factoring (Conway & Huffcutt, 2003; Morrison, 2009; Norris & Lecavalier, 2010; Park, Dailey, & Lemus, 2002). Principal axis extraction was chosen because it is robust to non-normal data compared to maximum likelihood (Briggs & MacCallum, 2003; Costello & Osborne, 2005).

We selected substantive factors using a combination of the following criteria: parallel analysis (PA), Cattell’s (1966) visual scree plot, and theoretical convergence. Horn (1965) developed PA by and compares eigenvalues against a randomly ordered data set. We kept factors when their eigenvalues were larger than the eigenvalues created by the random data. The PA method has been recommended for determining an approximate number of factors to accept (Humphreys & Montanelli, 1975; Velicer, Eaton, & Fava, 2000; Zwick & Velicer, 1982, 1986). We inspected the visual scree plot, and we selected factors that broke off from the straight line drawn by the researcher (Cattell & Jaspars, 1967; Fabrigar, Wegener, MacCallum, & Strahan, 1999; Gorsuch, 1983).

We applied Promax with a k value of 4 (Gorsuch, 1997; Hendrickson & White, 1964). If a researcher is primarily interested in producing results that best fit the data, the factors should be rotated obliquely. The oblique rotation method assumes that the factors are correlated, whereas orthogonal (e.g., Varimax) assumes that factors are uncorrelated to each other. Varimax is the most commonly applied rotation in scale development, yet methodologists have argued against its use in scale development. Scales consist of highly correlated variables that form a construct, and it is uncommon for factors not to correlate with one another in the social sciences (DeVellis, 2003; McCroskey & Young, 1979). Orthogonal rotation can lead to an overestimation or underestimation of loadings leading researchers to inappropriately retain or reject items, which in turn may cause problems when conducting confirmatory factor analysis (Costello & Osborne, 2005; Preacher & MacCallum, 2003; Tabachnick & Fidell, 2007). As a result, scale methodologists argue that orthogonal rotation should be avoided because it presents a false picture of reality.

Item deletion is an expected part of the scale development process. We assessed simple factor structure as proposed by Thurstone (1947) based on pre-established criteria: item sets with item loadings at or above the .40 level (Tabachnick & Fidell, 2007; Worthington & Whittaker, 2006), no cross-loadings, no factors with fewer than three items (Fabrigar et al., 1999; Kline, 2013; Tabachnick & Fidell, 2007), and theoretical convergence (DeVellis, 2012; Hair, Black, Babin, & Anderson, 2010) were considered to determine best fit to the data.

Results

We reviewed the data matrix for missing data, outliers, and linearity using SPSS 21.0. Means ranged on the 5-point scale (never to always) from 2.43 to 4.63, with standard deviations ranging from 0.506 to 1.227. Skewness ranged from −1.734 to 0.751 and kurtosis ranged from −0.832 to 2.735 for all variables.

We conducted several statistical tests to assist in the process. Bartlett’s test (χ² = 469.395, df = 120, p < .001), the KMO sample size statistic of .76, and coefficients above .30 suggested that the correlation matrix was appropriate for factor analysis. The correlation matrix is available upon request. There is no precise solution when determining the initial factors. The goal is to maximize the amount of variance explained while maintaining the fewest number of factors. There are several guidelines that scholars recommend to identify the number of factors to retain (Pett et al., 2003). PA suggested six factors, the scree plot suggested seven factors, and theory suggested eight. As a result, five-, six-, seven-, and eight-factor solutions were sequentially examined during the initial factor extraction process. The six-, seven-, and eight-factor solutions were rejected because there were multiple cross-loadings (i.e. loadings, or pattern coefficients, were salient on more than one factor), variables not meeting the minimum cutoff of .40, singletons, and less than three-item factors. It would have been six-factor model if we accepted a two-item factor explaining little variance, but three items are more accurate measures of dimensions.⁴ We retained the five-factor solution because it best met Thurstone’s (1947) simple structure. As shown in Table 1, factor loadings for the scales ranged from .51 to .84. These factor loadings within the underlying construct explained 65.9% of the total variance. Cronbach’s alpha levels for all concepts ranged from .64 to .77. Hair et al. (2010) recommend a limit of .70, but “it may decrease to .60 in exploratory research” (p. 125).

Table 1.

Exploratory Factor Pattern Coefficients for the Five-Factor Social Media Communicator Roles Structure.

Scale items	Mean	Customer service provider	Mobilizer	Information disseminator	Researcher	Community builder
1. Identify issues, complaints, or problems being expressed	4.2	.84	−.03	−.04	.09	−.20
2. Provide customer service on behalf of an organization	3.9	.74	.11	−.03	−.14	−.00
3. Answer questions about the organization or community	4.1	.62	.11	−.05	.04	.06
4. Respond to public criticism related to an organization or products	3.6	.57	−.16	.08	.04	.16
5. Recruit people to give back to important causes	2.5	.08	.85	−.15	.00	.04
6. Recruit people to volunteer or donate	2.4	−.01	.74	.03	−.04	.11
7. Educate public about issues that matter to an organization	3.5	−.06	.51	.37	.10	−.19
8. Disseminate information on events, news, or issues	3.8	−.09	−.03	.69	−.03	−.02
9. Collect news, opinion, and commentary from audiences	4.1	.01	−.04	.65	.06	−.06
10. Share news or information that directly helps a geographic or online community	3.9	.09	.05	.65	−.07	.14
11. Research people’s behaviors on different platforms	3.8	−.12	.09	−.14	.76	.10
12. Monitor communication behaviors of people and organizations	4.0	.15	−.07	.07	.66	.00
13. Monitor external trends that might affect the organization	4.4	.02	−.02	.08	.65	.00
14. Involve the public in conversations on behalf of the organization	4.0	.10	.00	.12	−.04	.71
15. Recognize people who contribute on an organization’s social media channels	3.9	−.04	.05	−.14	.03	.64
16. Elicit reactions or feedback from the public or stakeholders	3.9	−.10	.02	.05	.11	.51

SD: standard deviation.

Principal Axis factoring and Promax rotation were used. Factor loading cutoff was .40. Items 1–4 = Customer service provider (variance: 27.8%, eigenvalue: 4.48, mean: 15.8, SD = 2.79, a = .77); Items 5–7 = Mobilizer (variance: 10.69%, eigenvalue: 1.17, mean: 8.4, SD = 2.75, a = .76); Items 8–10 = Information Disseminator (variance: 10.07%, eigenvalue: 1.16, mean: 11.9, SD = 2.75, a = .70); Items 11–13 = Researcher (variance: 9.14%, eigenvalue: 1.46, mean: 12.2, SD = 2.05, a = .68); and Items 14–16 = Community Builder (variance: 8.17%, eigenvalue: 1.31, mean: 11.8, SD = 2.08, a = .64). Bold numbers indicate that factor loadings are at or greater than the .40 cutoff.

Discussion

The results show that five factors describe the various roles of people who communicate across social media platforms. Previous scholars argued that social media was used for promotional, research, sourcing, community building, and dissemination purposes based on the literature review. We found that SMCs use it to promote action, build community, and disperse information (Lovejoy & Saxton, 2012), but this research also revealed the presence of research and customer service roles. Based on a separate descriptive analysis, the customer service and research roles were enacted on a more regular basis (see Table 1). We discuss dimensional results of the factor analysis that included the customer service provider, mobilizer, researcher, information disseminator, and community builder roles. We named each factor based on previous literature and expert consultation.

Customer Service Provider

SMCs should be able to handle communication directed at an organization often in the form of a complaint or question. This role requires them to serve as a liaison between the organization and the public. In this role, the SMC can help the organization by providing appropriate support for product and service queries, tolerating customer’s venting toward an organization, responding to rumors, apologizing, or recruiting particular staff to help address issues and complaints (Blom, Carpenter, Bowe, & Lange, 2014; Gallaugher & Ransbotham, 2010; Knight & Carpenter, 2012).

Mobilizer

Social media channels allow individuals to recruit and educate people about their purpose. Overall, mobilization has been important but not a heavily applied external function of communicators (Hestres, 2014; Leichty & Springston, 1996; Lovejoy & Saxton, 2012), although previous research shows that social media is extensively used to build and maintain communities (Hestres, 2014). Collective action through social media takes place by employing communication appeals to raise funds; increase attendance at events; educate people about politics, issues, and legislation; and enlist volunteers (Auger, 2013; Enjolras, Steen-Johnson, & Wollebaek, 2013; Kim & Reber, 2008).

Information Disseminator

Information dissemination is a prevailing application of social media tools (Auger, 2013; Bridgen, 2011; Gulyas, 2013; Kelleher & Sweetser, 2012; Lovejoy & Saxton, 2012; Parmelee, 2014). Social media allows people to distribute and broadcast information in real-time. Himelboim, Gleave, and Smith (2009) discovered posting of factual information was key to encouraging engagement among other comment posters. The numerous platforms and potential messaging strategies require the SMCs to identify content most appropriate for each culture or niche audience.

Researcher

Organizational decisions and social media practices should be rooted in research. This environmental scanning role enables organizations to know what is being said and written about their services and products. In this role, the SMC monitors, collects, and contextualizes information about social media users’ behaviors and preferences. Previous research has shown that collecting intelligence is a major function provided by public relations, journalists, and corporate communication professionals (e.g., Hedman & Djerf-Pierre, 2013; Macnamara & Zerfass, 2012).

Community Builder

A primary reason for SMCs to use social media tools is to foster a sense of community (Carim & Warwick, 2013). The movement of communications to the Internet has expanded their central role as communicators. People use social media because of a desire to belong (Seo, Houston, Taylor Knight, Kennedy, & Inglish, 2013). SMCs establish communities by acknowledging individuals and sharing information to build trust (Bridgen, 2011; Yang & Lim, 2009). SMCs enacting this role require the ability to solicit feedback and cultivate involvement with an organization, brand, or issue (Blom et al., 2014; Himelboim, 2010; Jansen & Koop, 2005). SMCs should also develop community norms and sanctioning mechanisms when launching new communities (Crystal, 2001; DiMaggio, Hargittai, Neuman, & Robinson, 2001).

Conclusion

An expressed concern for the lack of theory in social media literature arises because scholars need to comprehend and predict phenomena across platforms. We relied on the foundational work of many scholars to create a construct that can help guide future investigations across communication contexts. For example, role theory posits that individuals situated within a role follow certain pattern behaviors often resulting from organizational interactions and personal motives (Biddle, 1979). We articulated these patterns of behavior among SMCs. We revisited role theory to guide in the development of items to help ensure the items validly represented the construct roles. We specified issues concerning previous role work such as highlighting the variations of interpretations across role measures. The scholarly discussion on measurement development is necessary not only to aid in future predictive efforts but also because scale research can highlight potential areas of concern among existing measures. Such theoretical and empirical explorations in journals help scholars build scientific knowledge rooted in a more consistent framework. Scale endeavors reflect one small slice of the overall efforts required to answer the call for theoretically driven work in our fields. This scale requires continued testing on other samples because of the small sample size and the inductive reasoning involved in the scale creation process. The next logical step is to apply this scale to another sample and submit those data to a confirmatory factor analysis.

The practical implications of this study highlight the various roles of SMCs. Such knowledge can inform people less active on social media channels to understand the various roles of SMC in organizational settings. Educators, agency representatives, and company leaders could also use the scale on students, applicants, and employees to assess their social media communication capabilities and understanding as well.

We recognize that social media technologies will likely evolve as adoption of sensor, wearable, and screen technologies increases. Yet, at the navigational heart of a messy ecosystem are data and measurement. Measurement dialogue in journals can help scholars and practitioners more validly capture behaviors and intellectually evolve with such advancements.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by The Arthur W. Page Center under Grant No. 1410LSSC.

Notes

Author Biographies

Serena Carpenter (PhD, Michigan State University) is an Assistant Professor of Journalism Innovations in the School of Journalism at Michigan State University. Her research interests include journalistic practices, alternative forms of journalism, empirical methods, and theory building.

Alisa P. Lertpratchya (MA, Michigan State University) is a doctoral student in the Department of Advertising and Public Relations at Michigan State University. Her research interests include the effects of media and social media on consumers, users, and the community with special emphasis on cognitive effects on self-image such as self-esteem, self-efficacy, depression, and body image.

References

Auger

G. A.

(2013). Fostering democracy through social media: Evaluating diametrically opposed nonprofit advocacy organizations’ use of Facebook, Twitter and YouTube. Public Relations Review, 39, 369–376.

Beeler

Lebovits

Bishop

(1983). Why workers behave the way they do. Chicago, IL: Union Representative.

Bertaux

(1981). From the life-history approach to the transformation of sociological practice. In Bertrauz

(Ed.), Biography and society: The life history approach in the social sciences (pp. 29–45). London, England: SAGE.

Beurer-Züllig

Fieseler

Meckel

(2009). Typologies of communicators in Europe. Corporate Communications: An International Journal, 14, 158–175.

Biddle

B. J.

(1979). Role theory: Expectations, identities, and behaviors. New York, NY: Academic Press.

Biddle

B. J.

(1986). Recent development in role theory. Annual Review of Sociology, 12, 67–92.

Blom

Carpenter

Bowe

Lange

(2014). Frequent contributors in U.S. newspaper comment forums: An examination of their civility and informational value. American Behavioral Scientist, 58, 1314–1328.

Bridgen

(2011). Emotional labour and the pursuit of the personal brand: Public relations practitioners’ use of social media. Journal of Media Practice, 12, 61–76.

Briggs

N. E.

MacCallum

R. C.

(2003). Recovery of weak common factors by maximum likelihood and ordinary least squares estimation. Multivariate Behavioral Research, 38, 25–56.

10.

Broom

G. M.

Dozier

D. M.

(1986). Advancement for public relations role models. Public Relation Review, 12, 37–56.

11.

Broom

G. M.

Smith

D. G.

(1979). Testing the practitioner’s impact on clients. Public Relations Review, 5(3), 47–59.

12.

Burk

P. J.

(1980). The self: Measurement requirements from an interactionist perspective. Social Psychology Quarterly, 43, 18–29.

13.

Carim

Warwick

(2013). Use of social media for corporate communications by research-funding organisations in the UK. Public Relations Review, 39, 521–525.

14.

Cattell

R. B.

(1966). The screen test for the number of factors. Multivariate Behavioral Research, 1, 245–276.

15.

Cattell

R. B.

Jaspars

J. M.

(1967). A general plasmode (N. 30-10-5-2) for factors analytics exercises and research. Multivariate Behavioral Research Monographs, 67, 1–212.

16.

Cogburn

D. L.

Espinoza-Vasquez

F. K.

(2011). From networked nominee to networked nation: Examining the impact of web 2.0 and social media on political participation and civic engagement in the 2008 Obama campaign. Journal of Political Marketing, 10, 189–213.

17.

Conway

J. M.

Huffcutt

A. I.

(2003). A review and evaluation of exploratory factor analysis practices in organizational research. Organizational Research Methods, 6, 147–168.

18.

Costello

A. B.

Osborne

J. W.

(2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1–9.

19.

Crystal

(2001). Language and the internet. Cambridge, UK: Cambridge University Press.

20.

Cunningham

J. W.

(1996). Generic descriptors: A likely direction in occupational analysis. Military Psychology, 8, 247–262.

21.

Dance

F. E. X.

(1970). The “concept” of communication. Journal of Communication, 20, 201–210.

22.

DeVellis

R. F.

(2012). Scale development: Theory and applications (3rd ed.). Thousand Oaks, CA: SAGE.

23.

Dierdorff

E. C.

Morgeson

F. P.

(2007). Consensus in work role requirements: The influence of discrete occupational context of role expectations. Journal of Applied Psychology, 92, 1228–1241.

24.

Dillman

D. A.

(2007). Mail and internet surveys: The tailored design method. Hoboken, NJ: John Wiley & Sons.

25.

DiMaggio

Hargittai

Neuman

W. R.

Robinson

J. P.

(2001). Social implications of the internet. Annual Review of Sociology, 27, 307–336.

26.

Dozier

D. M.

(1992). The organization roles of communication and public relations practitioners. In Grunig

J. E.

Dozier

D. M.

Ehling

W. P.

Gruning

L. A.

Repper

F. C.

White J

(Eds.), Excellence in public relations and communication management (pp. 327–355). Hillsdale, NJ: Lawrence Erlbaum.

27.

Dozier

D. M.

Broom

G. M.

(1995). Evolution of the manager role in public relations practice. Journal of Public Relations Research, 7, 3–26.

28.

Efimova

Grudin

(2008). Crossing boundaries: Digital literacy in enterprises. In Lankshear

Knobel

(Eds.), Digital literacies: Concepts, policies, practices (pp. 203–226). New York, NY: Peter Lang.

29.

Enjolras

Steen-Johnson

Wollebaek

(2013). Social media and mobilization to offline demonstrations: Transcending participatory divides? New Media & Society, 15, 890–908.

30.

Fabrigar

L. R.

Wegener

D. T.

MacCallum

R. C.

Strahan

E. J.

(1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4, 272–299.

31.

Ferguson

M. A.

(1979). Role norms, implicit relationship attributions and organizational communication: A study of public relations practitioners (Unpublished master’s thesis). University of Maryland, College Park, MD.

32.

Gallaugher

Ransbotham

(2010). Social media and customer dialog management at Starbucks. MIS Quarterly Executive, 9, 197–212.

33.

Gitter

A. G.

(1981). Public relations roles: Press agent or counselor? Public Relations Review, 8(3), 35–41.

34.

Gorsuch

R. L.

(1983). Factor analysis (Vol. 2). Hillsdale, NJ: Lawrence Erlbaum.

35.

Gorsuch

R. L.

(1997). Exploratory factor analysis: Its role in item analysis. Journal of Personality Assessment, 68, 532–560.

36.

Greenlaw

Brown-Wetly

(2009). A comparison of web-based and paper-based survey methods testing assumptions of survey mode and response cost. Evaluation Review, 33, 464–480.

37.

Greer

C. F.

Ferguson

D. A.

(2011). Using Twitter for promotion and branding: A content analysis of local television Twitter sites. Journal of Broadcasting & Electronic Media, 55, 198–214.

38.

Guest

Bunce

Johnson

(2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59–82. doi:10.1177/1525822x05279903

39.

Gulyas

(2013). The influence of professional variables on journalists’ uses and views of social media. Digital Journalism, 1, 270–285.

40.

Hair

Jr. Black

W. C.

Babin

B. J.

Anderson

R. E.

(2010). Multivariate data analysis (Vol. 7). Upper Saddle River, NJ: Prentice Hall.

41.

Hanitzsch

(2005). Journalists in Indonesia: Educated but timid watchdogs. Journalism Studies, 6, 493–508.

42.

Hedman

Djerf-Pierre

(2013). The social journalist: Embracing the social media life or creating a new digital divide? Digital Journalism, 1, 368–385.

43.

Hendrickson

A. E.

White

P. O.

(1964). Promax: A quick method for rotation to oblique simple structure. British Journal of Mathematical Psychology, 17, 65–70.

44.

Hestres

L. E.

(2014). Preaching to the choir: Internet-mediated advocacy, issue public mobilization, and climate change. New Media & Society, 16, 323–339.

45.

Himelboim

(2010). Civil society and online political discourse: The network structure of unrestricted discussions. Communication Research, 38, 634–659.

46.

Himelboim

Gleave

Smith

(2009). Discussion catalysts in online political discussions: Content importers and conversation starters. Journal of Computer-Mediated Communication, 14, 771–789.

47.

Horn

J. L.

(1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179–185.

48.

Humphreys

L. G.

Montanelli

R. G.

Jr. (1975). An investigation of the parallel criterion for determining the number of common factors. Multivariate Behavioral Research, 10, 193–205.

49.

Jackson

(1998). Is there a place for role theory in occupational science? Journal of Occupational Science, 5(2), 56–65.

50.

Jansen

H. J.

Koop

(2005). Pundits, ideologues and ranters: The British Columbia election online. Canadian Journal of Communication, 30(4). Retrieved from http://www.cjc-online.ca/index.php/journal/article/view/1483/1601

51.

Kahn

R. L.

Wolfe

D. M.

Quinn

R. P.

Snoek

J. D.

(1964). Organizational stress: Studies in role conflict and ambiguity. New York, NY: John Wiley & Sons.

52.

Kaiser

H. F.

(1974). An index of factorial simplicity. Psychometrika, 39, 31–36.

53.

Katz

Kahn

R. L.

(1978). The social psychology of organizations (Vol. 2). New York, NY: John Wiley & Sons.

54.

Kelleher

Sweetser

(2012). Social media adoption among university communicators. Journal of Public Relations Research, 24, 105–122.

55.

Khang

(2012). Social media research in advertising, communication, marketing, and public relations, 1997–2010. Journalism & Mass Communication Quarterly, 89, 279–298.

56.

Kim

Reber

B. H.

(2008). Public relations’ place in corporate social responsibility: Practitioners define their role. Public Relations Review, 34, 337–342.

57.

Kline

R. B.

(2013). Exploratory and confirmatory factor analysis. In Petscher

Schatschneider

Compton

D. L.

(Eds.), Applied quantitative analysis education and the social sciences (pp. 171–207). New York, NY: Routledge.

58.

Knight

Carpenter

(2012). Optimal matching model of social support: An examination of how national product and service companies use Twitter to respond to the public. Southwestern Mass Communication Journal, 28(2), 21–35.

59.

Leichty

Springston

(1996). Elaborating public relations roles. Journalism & Mass Communication Quarterly, 73, 467–477.

60.

Leonardi

P. M.

Huysman

Steinfield

(2013). Enterprise social media: Definition, history, and prospects, for the study of social technologies in organizations. Journal of Computer-Mediated Communication, 19, 1–19.

61.

Linton

(1936). The study of man. New York, NY: D. Appleton-Century Company.

62.

Lovejoy

Saxton

G. D.

(2012). Information, community, and action: How nonprofit organizations use social media. Journal of Computer-Mediated Communication, 17, 337–353.

63.

Macnamara

Zerfass

(2012). Social media communication in organizations: The challenges of balancing openness, strategy, and management. International Journal of Strategic Communication, 6, 287–303.

64.

McCrosky

J. C.

Young

T. J.

(1979). The use and abuse of factor analysis in communication research. Human Communication Research, 5, 375–382.

65.

Mellado

Barría

(2012). Development of professional roles in the practice of public relations in Chile. Public Relations Review, 38, 446–453.

66.

Mergel

Bretschneider

S. I.

(2013). A three-stage adoption process for social media use in government. Public Administration Review, 73, 390–400.

67.

Merton

R. K.

(1968). Social theory and social structure. New York, NY: The Free Press.

68.

Messner

Linke

Eford

(2012). Shoveling tweets: An analysis of the microblogging engagement of traditional news organizations. ISOJ Journal, 2, 76–90.

69.

Morrison

J. T.

(2009). Evaluating factor analysis decisions for scale design in communication research. Communication Methods and Measures, 3, 195–215.

70.

Newcomb

T. M.

(1950). Social psychology. New York, NY: The Dryden Press.

71.

Norris

Lecavalier

(2010). Evaluating the use of exploratory factor analysis in developmental disability psychological research. Journal of Autism Development and Disorders, 40, 8–20.

72.

Park

H. S.

Dailey

Lemus

(2002). The use of exploratory factor analysis and principal components analysis in communication research. Human Communication Research, 28, 562–577.

73.

Parmelee

J. H.

(2013). Political journalists and Twitter: Influences on norms and practices. Journal of Media Practice, 14, 291–305.

74.

Parmelee

J. H.

(2014). The agenda-building function of political tweets. New Media & Society, 16, 434–450.

75.

Pett

M. A.

Lackey

N. R.

Sullivan

J. L.

(2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research. Thousand Oaks, CA: SAGE.

76.

Porter

Sweetser

Chung

(2009). The blogosphere and public relations: Investigating practitioners’ roles and blog use. Journal of Communication Management, 13, 250–267.

77.

Porter

Sweetser

Chung

Kim

(2007). Blog power: Examining the effects of practitioner blog use on power in public relations. Public Relations Review, 33, 92–95.

78.

Preacher

K. J.

MacCallum

R. C.

(2003). Repairing Tom Swift’s electric factor analysis machine. Understanding Statistics, 2, 13–43.

79.

Quinton

Fennemore

(2013). Missing a strategic marketing track? The use of online social networks by UK charities? International Journal of Nonprofit and Voluntary Sector Marketing, 18, 36–51.

80.

Ramaprasad

Kelly

J. D.

(2003). Reporting the news from the world’s rooftop: A survey of Nepalese journalists. Gazette, 65, 291–315.

81.

Reynolds

P. D.

(1971). A primer in theory construction. New York, NY: Palgrave Macmillan.

82.

Seo

Houston

J. B.

Taylor Knight

Kennedy

E. J.

Inglish

A. B.

(2013). Teens’ social media use and collective action. New Media & Society, 16, 883–902.

83.

Shoemaker

P. J.

Reese

S. D.

(2014). Mediating the message in the 21st Century. New York, NY: Routledge.

84.

Stampler

(2013, July 2). RANKED: The 30 most creative people in social media marketing. Business Insider. Retrieved from http://www.businessinsider.com/the-30-most-creative-social-media-marketers-ranked-2013-7

85.

Tabachnick

B. G.

Fidell

L. S.

(2007). Using multivariate statistics (Vol. 5). Boston, MA: Allyn & Bacon.

86.

Taylor

Kent

M. L.

(2010). Anticipatory socialization in the use of social media in public relations: A content analysis of PRSA’s Public Relations Tactics. Public Relations Review, 36, 207–214.

87.

Thurstone

L. L.

(1947). Multiple factor analysis. Chicago, IL: University of Chicago Press.

88.

Treem

J. W.

Leonardi

(2012). Social media uses in organizations. Communication Yearbook, 36, 143–189.

89.

Tung

E. T.

(2013, June 13). The top 100 social media power influencers in Houston 2013. Retrieved from http://ericttung.com/2013/06/13/top-100-social-media-power-influencers-in-houston-2013/

90.

Velicer

W. F.

Eaton

C. A.

Fava

J. L.

(2000). Construct explication through factor or component analysis: A review and evaluation of alternative procedures for determining the number of factors or components. In Goffin

R. D.

Helmes

(Eds.), Problems and solutions in human assessment: Honoring Douglas Jackson at seventy (pp. 41–71). Boston, MA: Kluwer.

91.

Verhoeven

Tench

Zerfass

Morena

Verčič

(2012). How European PR practitioners handle digital and social media. Public Relations Review, 38, 162–164.

92.

Weaver

Beam

R. A.

Brownlee

B. J.

Voakes

P. S.

Wilhoit

G. C.

(2007). The American journalist in the 21st Century: U.S. news people at the dawn of a new millennium. Mahwah, NJ: Lawrence Erlbaum.

93.

Wigley

Zhang

(2011). A study of PR practitioners’ use of social media in crisis planning. Public Relations Journal, 5(3), 1–16.

94.

Worthington

R. L.

Whittaker

T. A.

(2006). Scale development research. A content analysis for recommendations for best practices. The Counseling Psychologist, 34(6), 806–838.

95.

Wright

D. K.

Hinson

M. D.

(2008). How blogs and social media are changing public relations and the way it is practiced. Public Relations Journal, 2(2), 1–21.

96.

Wright

D. K.

Hinson

M. D.

(2011). A three-year longitudinal analysis of social and emerging media use in public relations practice. Public Relations Journal, 5, 1–32.

97.

Yang

Lim

(2009). The effects of blog-mediated public relations (BMPR) on relational trust. Journal of Public Relations Research, 21, 341–359.

98.

Zhang

Leung

(2015). A review of social networking service (SNS) research in communication journals from 2006 to 2011. New Media & Society, 17(7), 1007–1024. doi:10.1177/1461444813520477

99.

Zwick

W. R.

Velicer

W. F.

(1982). Factors influencing four rules determining the number of components to retain. Multivariate Behavioral Research, 17, 253–269.

100.

Zwick

W. R.

Velicer

W. F.

(1986). Comparison of five rules for determining the number of components to retain. Psychological Bulletin, 99, 432–442.

Social Media Communicator Roles: A Scale

Abstract

Keywords

Social Media Communication

Role Theory

Methods

Qualitative Research

Expert Feedback

Pretest

Quantitative Survey

Scale Development Procedures

Results

Discussion

Customer Service Provider

Mobilizer

Information Disseminator

Researcher

Community Builder

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

Notes

Author Biographies

References