Content creators as social influencers: predicting online video posting behaviors

Leo Jeffres; David Atkin; Kimberly Neuendorf

doi:10.1515/omgc-2024-0022

Article Open Access

Content creators as social influencers: predicting online video posting behaviors

Leo Jeffres , David Atkin and Kimberly Neuendorf

Published/Copyright: December 3, 2024

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From the journal Online Media and Global Communication Volume 3 Issue 4

Abstract

Purpose

Creative content influencers are increasingly seizing the opportunity not only to express themselves, but also to monetize their videos and become entrepreneurs. The present study tests an integrative framework to determine whether the “creative influencers environment” impacts the degree of motivation for posting videos online, alongside the gratifications derived from such activity.

Design/methodology/approach

Utilizing a national online survey of video “creators” (n = 327), we test a hierarchical model assessing the impact of social categories, personality factors (e.g., narcissism), and mediated communication constructs (e.g., uses and gratifications [U&G]).

Findings

Multiple regression analyses explain roughly half or more of the variance in our models for predicting (1) motivations for and (2) gratifications received from posting videos on social media.

Practical implications

Study findings reveal a different mix of predictors for each video content category that creative influencers post on social media. This includes a strong pattern of influences on posting motivations from the larger environment in which people live, social categories, and personality factors, but not from the use of legacy media nor the use of newer technologies.

Social implications

Results also underscore “different influences for different types of content,” which can be explained by a “content trumps form” theme that has emerged in recent research. Overall, mixed support was found for the proposed model, although we did establish this collection of constructs as important in predicting the strength of gratifications received and motivations sought by posting videos on social media.

Originality/value

The present analysis of larger environment factors, social categories, personality indicators, and use of media (legacy and newer) helps profile a social media “creator culture” for the production of online videos. Study findings offer a clear signpost that an additional component could be added here—that of “content types” for online videos—as a moderator for the variegated predictive power of the model components.

Keywords: survey; creative content influences; uses and gratifications; social media videos

Revenue in the online creative influencer industry is projected to reach $480 million by 2027—encompassing over 50 million creators trying to earn a living online—although only 4.3 % of them make more than $100,000 per year (Hutchinson 2024). Research by Chillingworth (2024) suggests that TikTok is the top revenue generating platform; the platform was selected by 30 % of creative influencers, followed by You Tube (28.5 %), Facebook (16.5 %), Twitter (13.1 %) and Instagram (7.1 %). The recent controversy accompanying the House ban on TikTok (Yilek 2024) raises concern about adverse impacts on innovation in the U.S., underscoring the need to examine the uses and motivations driving online video posting.

Social media creators do not live in a vacuum, but are part of a social media creative influencer culture, where individuals compete for attention and need to be aware of their audiences. Pitching the concept at an industry level, Cunningham and Craig (2021) examine creator culture in the entertainment arena through an industry lens; they define it as the sum total of all commercial elements involving content creators. Here we examine the individual level of analysis and the social media culture in which creative influencers operate. Impacts on creative influencers include not only their social categories but also the personal goals directing their behaviors, and effects of their own use of legacy media and online sources across platforms.

Focusing on media use motivations, Yang and Stohl (2020) found that American social media activists utilized traditional media content to a greater degree than the leaderless bottom-up action characteristic of personalized action frames. They suggest a motivation based on a need for greater legitimacy and expediency. Across the globe, Imani-Giglou et al. (2017) found that social media were used by those supporting a protest movement in Turkey, while those who opposed it used legacy media, including Turkish and European TV. Fenton et al. (2020) note that social media, instead of replacing legacy media, actively direct traffic to mainstream media while censoring alternative media.

Audience uses and gratifications (U&G) sustain people’s media behaviors (Rubin 2009), so it’s a logical extension to think they also sustain online posting behavior. Here we extend that framework to examine the factors that comprise the culture of creative influencers who post videos on social media. Warren et al. (2014) found that using social media for civic engagement significantly impacted trust in institutions. Vraga and Tully (2021) found that those who are more news literate and value news literacy are more skeptical of the quality of information on social media. The current study examines whether the “creative influencer’s environment” impacts motivations for posting videos online, alongside the gratifications derived from such activity.

Drawing from that literature, the proposed analytical framework examines the impact of the larger environment, social categories, and personality indicators, alongside uses of legacy media and digital media (see Appendix). U&G theory is used to conceptualize consumption of media content/messages for creators of content/messages. In particular, we focus on the types of content that “creative influencers” often feature in the videos they post on social media (e.g., social issues, one’s own image)—as outcomes—alongside motivations and gratifications for posting videos online.

1 Background: understanding video posting behavior

The larger framework utilizes an integrated audience-centered approach to examine motivations for posting and the gratifications derived from posting activity. Research on social media posting behavior combines entertainment and utilitarian aspects of media use, encompassing theoretical domains ranging from personality theory (Hunt and Langstedt 2014) to audience U&G (e.g., Sundar and Limperos 2013). Given that social media use encompasses a wide range of proximal and distal factors—merging interpersonal and mass communication (i.e., “intermass”) functions—we frame this research in the context of an overarching model incorporating Information Communication Technology (ICT) and media uses.

For instance, the Integrated Technology Adoption Paradigm (ITAP) considers ICT adoption and use along a spectrum ranging from micro (e.g., audience/use) to macro (social, technology) factors (e.g., Lin 2003). Since the current focus involves media uses rather than adoption, it’s useful to posit an integrative framework on social media posting behaviors that involves a set of components similar to the ITAP; those include system factors like indicators of the larger environment, social categories, and use factors governing legacy media as well as newer technologies. These variegated factors, in turn, would predict the key dependent measures of interest: (1) strength of motivations for posting videos on social media, and (2) strength of gratifications from posting videos on social media, per Figure 1 below:

Figure 1:

Model of influences from creative influencer culture on behaviors for posting videos on social media.

The current analytical framework is structured to explore how the “creative influencer’s environment” predicts the motives for posting and the gratifications derived from posting activity. These components are centered in the context of media use, beginning with factors related to the larger environment that can drive media use, followed by individual-level factors (social categories, personality attributes) that will also have an effect on media use.

This framework is also consistent with research employing the Theory of Reasoned Action (Fishbein and Ajzen 1975) and its revised version, the Theory of Planned Behavior (Ajzen 1991). The former says that one’s intention to engage in a behavior is the best predictor of actually doing so while the latter supports that relationship but adds attitude toward the behavior and recognizes situations where one does not have total control over the situation. The final two variables in our model, strength of motivations for posting and strength of gratifications from posting, match these key concepts.

Motivations represent one’s intentions to post on social media, while gratifications represent the relationship/attitude one has towards the posting behavior. Examining numerous empirical studies, Ajzen (1991) notes that both predictors, intentions and perceived behavioral control, correlate quite well with behavioral performance. In five studies, regression analyses showed that each of the two antecedent variables made a significant contribution to the prediction of behavior, while in most of the remaining studies, intentions proved the more important of the two predictors. The environmental factors and social categories discussed below point out that content creators have considerable control over their creative activities. But they also are constrained by a host of other factors that make the relationship between intention (motivations) and posting less than perfect.

1.1 Environment factors

Creative people operate within a wider context that includes the local community in which they live as well as broader international influences. The strength of people’s links to the local community has been positively related to their media use (Lain 1986; Neuwirth et al. 1989). Merton and Lazarsfeld (1950) identified two types of such ties to community: local and cosmopolitan. Jeffres (1994) mapped out influences on media behaviors, with social system, the physical environment, and existing media communication system at the macro level. Here we examine several indicators of these influences, e.g., community attachment, perceived quality of life (Jeffres et al. 2015), and cosmopoliteness (Jeffres et al. 2004), all of which have been empirically linked to media behaviors. Based on the theory and research above, we assume that a richer set of environmental factors would predict engagement with video posting; more formally,

H1:

Indicators of environmental richness will positively predict (H1a) gratifications received from posting videos on social media and (H1b) motivations for posting videos on social media.

1.2 Social categories

Much of the early research on ICT diffusion and use was focused on the influence of social locators (e.g., diffusion of innovations theory [Rogers 2003)] and how gaps in resources and adoption persisted across time, including the implications for people’s social and economic lives. Although early diffusion-based perspectives saw adoption of ICTs as resource-driven (e.g., Atkin et al. 1998; Hargittai 2008; Huang and Chen 2010), research found that these “digital divides” were gradually bridging as digital media began reaching the flat part of their diffusion curve (e.g., Lin 2020). Vogels (2021) summarized Pew data suggesting that these divides persist for low-income and minority populations. Scholars (e.g., Floberg 2018) attribute such gaps to social barriers, ISP use of credit checks, and differing levels of digital skill levels.

These digital divides have also extended to the realm of a “gray gap” favoring younger users in ICT adoption and use, although these too have been bridging as use of such technology becomes normalized (e.g., Lin 2020). Importantly, Hargittai (2008) foresees a narrowing of these ascriptive divides via enhanced understanding of “the skills and expertise associated with using the Internet from the content producer’s and content viewer’s perspective” (p. 86). Uncertainty over the extent to which these ascriptive gaps in media adoption and use have been levelling over time—combined with the general dearth of scholarly work on video posting—preclude a clear theoretical prediction; we thus inquire:

RQ1:

How do social categories relate to (RQ1a) gratifications received from posting videos on social media, and (RQ1b) motivations for posting videos on social media?

1.3 Personality factors

Framing online video posting as a creative activity, it’s useful to consider how personality factors can characterize creative individuals (e.g., Jeffres et al. 2014, Jeffres et al. 2023; Ohly et al. 2010). Creative individuals typically master up-to-date technology and interact well with others (e.g., Ko and Butler 2007). Creative entrepreneurs have traditionally been defined by such motivations as wanting to be one’s own boss, risk-taking, or creative aptitudes that require control and independence (e.g., “to be fulfilled”) (e.g., Costa and McCrae 1992; Jeffres et al. 2014). Rae (2007), for instance, found that creative individuals score highly on openness to experience.

Some of these motivations may be a larger function of foundational “Big 5” traits (e.g., Eysenck 1991)—the most widely used personality scalar—which can thus help explain the creative processes underscoring video posting (e.g., Jeffres et al. 2023). Drawing from Cherry’s (2023: para 2) review of these traits: “Extraversion is sociability, agreeableness is kindness, openness is creativity and intrigue, conscientiousness is thoughtfulness, and neuroticism often involves sadness or emotional instability” (emphasis added).

Exploring these traits in relation to social media use, Liu and Campbell’s (2017) meta-analysis identified openness and extraversion as strong predictors. Posting photographs was predicted by extraversion as well as self-expression motives, although neuroticism was an inverse predictor. Similarly, extraverts and “people with the quality of being vain” were more active in contributing to (and engaging with) TikTok (Meng and Leung 2021: 1). Kircaburun et al. (2020) found that problematic social media use was related to agreeableness, introversion, and conscientiousness as well as female gender.

Moving beyond the Big 5, research indicates that narcissism represents arguably the most powerful personality predictor of SNS posting behavior (e.g., Appel et al. 2020; Tan and Yang 2014). Casale and Banchi (2020) found that narcissism “may not have consistent effects across all platforms,” but could figure importantly in problematic Facebook use (p. 1). Similarly, Davenport et al. (2014) underscore the need to consider contextual variables, such as the difference between passive v. active usage (reflecting content consumption and content generation, respectively).

Finally, studies of political expression suggest that authoritarianism has been linked to political expression on social media, which is in turn linked with participation (Skoric et al. 2016). Their findings also reinforce other work at the juncture between personality factors and media use motives (e.g., Hunt et al. 2012; Hunt and Langstedt 2014), including links between social media posting and informational, relational and entertainment needs. Posting about news, for instance, has been related to exhibitionism (Wu and Atkin 2017).

Since the literature on personality predictors of social media use remains incomplete—yielding inconsistent patterns of results for factors like extraversion (e.g., Clemens et al. 2015; Hunt and Langstedt 2014; Krishnan and Atkin 2014)—any directionally hypothesized links with video posting would be speculative. Given these uncertainties, we inquire:

RQ2:

How do personality factors relate to (RQ2a) gratifications received from posting videos on social media, and (RQ2b) motivations for posting videos on social media?

1.4 Traditional media uses and gratifications

Engagement with traditional print, video, radio, and film media can be best understood in the context of U&G theory (Katz et al. 1974), which assumes that audiences choose media to fulfill various psychological and social needs (e.g., the Internet for pleasure [Sundar and Limperos 2013]). In particular, the level of media use is determined by the strength of media motivations (e.g., Lin 1993, 2003; Rubin 2009). According to Katz et al. (1974), the U&G theoretical framework demonstrates “(1) the sociological origins of (2) needs, which generate (3) expectations of (4) the mass media or other sources, which lead to (5) differential patterns of media exposure (or engagement in other activities), resulting in (6) need gratifications and (7) other consequences, perhaps mostly unintended ones” (p. 20).

Rubin (2009) outlines five key assumptions upon which the U&G framework is based, which Vinney (2024) updates as:

(1) Media use is motivated and goal-oriented. People always have a reason for consuming media, even if it’s simply habit or entertainment. (2) People select media based on their expectation that it will satisfy specific wants and needs. (3) Media use is driven by individual social and psychological factors. (4) Media compete with other forms of communication, especially in-person communication, for selection and use in the fulfillment of needs and desires. Today, since so much of the media we consume is mobile, that competition is more immediate than ever … (5) Because people are active media users, media messages don’t exert especially strong effects on people. (para 7)

Several motives for Internet use have been identified by Papacharissi and Rubin (2000), including convenience, interpersonal utility, passing time, entertainment, and information seeking. Lin (1996: 574) found that media uses were predicted by “psychological motives and communication channels, communication content and psychological gratifications.” Based on the theory and literature reviewed above, we assume that (a) traditional media U&G extend to online video posting behaviors and (b) their use would be positively interrelated with motives to engage with such media. More formally, we hypothesize:

H2:

Use of legacy media (e.g., books) will be positively related to (H2a) gratifications received from posting videos on social media and (H2b) motivations for posting videos on social media.

1.5 Newer media uses

Early research on social media use motivations underscores the social utility function of connecting with friends (Raacke and Bonds-Raacke 2008), including friendship, connection dimensions and information as key gratifications sought (Bonds-Raacke and Raacke 2011). Appealing social networking sites are those that can deliver social utility, or ability to share information about oneself with others in their network. Similarly, Hunt et al. (2012) found that entertainment, interpersonal communication, and self-expression motives predicted social media use. The more powerful predictors included relationship maintenance (as opposed to formation), alongside impression management motivations (e.g., self-expression and self-presentation).

Such findings point to a new set of intermass functions underscoring individual motivations for their media selection and consumption (e.g., Haridakis 2002; Raacke and Bonds-Raacke 2008; Yoo 2011; Zeng 2011). In particular, individuals who are more extensively engaged with social media are more commonly called the “users,” which is closer to the “active audience” concept that the U&G model proposes. Studies examining U&G sought in Internet usage identify entertainment and convenience as strong indicators of media use (e.g., Sundar and Limperos 2013). For instance, Papacharissi and Rubin (2000) uncovered a blend of traditional and emerging factors motivating Internet use, including entertainment, information seeking, passing time, convenience, and entertainment and interpersonal utility.

Per the latter, expressive gratifications are particularly strong predictors of digital media U&G and persist when media content can satisfy gratifications sought (e.g., Hunt et al. 2012; Hunt and Langstedt 2014; Papacharissi and Mendelson 2011). These individual needs and desires can be gratified by specific features offered through such processes as video posting. Drawing from the research and theory outlined above, we assume that use factors involving social interaction and information predict engagement with the production of videos on social media:

H3:

Use of newer media will be positively related to (H3a) gratifications received from posting videos on social media and (H3b) motivations for posting videos on social media.

1.6 Content types for videos posted online

Contemporary research has found a wide array of content types emerging in user-created online videos (Jeffres et al. 2023). As with the ascriptive and personality factors reviewed above, the variegated nature of the background factors—combined with their declining influence as video posting behaviors become normalized—prompt us to pose the following research question:

RQ3:

What is the relative influence of environment factors, social categories, personality indicators, use of legacy media as well as newer media technologies, and audience U&G on predicting content types of videos posted on social media?

2 Methods

An online survey of social media video “creative influencers” was conducted. Recruitment was conducted via Amazon MTurk, with specific instructions to recruits that they must reside in the U.S., be over 18 years of age, and be creators of social media videos. Qualified respondents were directed via link to the survey instrument, hosted on SurveyMonkey. The use of MTurk and SurveyMonkey was conducted using contemporary guidelines for best practices (Aguinis et al. 2021; Committee for Protection of Human Subjects 2020; Sheehan and Pittman 2016). Several validity checks were built into the process, so that cases were accepted from MTurk only if all criteria were met (i.e., two attention checks (e.g., “I will click the option ‘1 = strongly disagree’ on this item”) and one repeated measurement reliability check). The study design was reviewed and approved by the senior authors’ university’s Institutional Review Board.

The instrument included four indicators concerning the larger environment: Single-item Likert-type measures of perceived quality of life (QOL), community attachment, cosmopoliteness (seeing oneself as a citizen of the world), and perceived happiness. The survey included six measures of social categories: Age, ethnicity, gender, education, income, and political philosophy (on a five-point response scale from strong conservative to strong liberal). For the measurement of personality indicators, the instrument included eight established psychometric scales: The Short Big 5 Scale tapping openness, conscientiousness, extraversion, agreeableness, and neuroticism (Rammstedt and John 2007; alphas = 0.04, 0.53, 0.57, 0.33, 0.92, MICs = −0.02, 0.23, 0.22, 0.16, 0.33); the four-item Narcissism Scale (Jonason and Webster 2010; alpha = 0.91, MIC = 0.73); a five-item Novelty-Seeking Scale adapted from Lee and Crompton (1992; alpha = 0.87, MIC = 0.58); and a seven-item composite Very Short Authoritarianism (VSA) Scale (Bizumic and Duckitt 2018; Duckitt et al. 2010; alpha = 0.68, MIC = 0.23). (It should be noted that all scales met internal consistency criteria established for alpha or for the MIC (mean interim correlation, which should be between 0.15 and 0.50; Briggs and Cheek 1986; Clark and Watson 1995), except the openness scale. Results for this scale should be viewed with caution).

For the measurement of use of legacy media, the survey asked how often respondents watch TV, listen to the radio, read newspapers hard copy and online, read magazines, read books, go out to see films in a theater, check email, and surf the Internet for pleasure (not work) (all measured on a 9-point response scale from “never” to “many times each day”). The use of newer media technologies was tapped with five measures: Four items that asked respondents how often they stream a movie on home TV, stream a TV program on home TV, watch a film on a tablet, computer, or cell phone, and watch a TV program on a tablet, computer, or cell phone (all measured on a 7-point response scale from “never” to “more than several times a week”), and a three-item scale that measured new technology enthusiasm (alpha = 0.51, MIC = 0.26).

A total of 327 respondents completed the survey. The sample is 54 % male and 46 % female. Age ranges from 21 to 73, with a mean of 39. The sample reports a fairly high level of education, with 23 % having advanced degrees, 62 % having a college degree, and 16 % some college or less. Median household income is in the $50,001–$75,000 range. Per racial/ethnic identity, 85 % of respondents indicate White/Caucasian, 7 % Black/African American, 5 % Latinx/Hispanic, 5 % Asian, and 1 % Native American/American Indian (multiple identities were allowed). Regarding political philosophy, 39 % indicate a strong conservative or leaning toward conservative orientation and 43 % indicate a strong liberal or leaning toward liberal orientation.

Two dependent measures were constructed—gratifications from posting activity, and motivations for posting activity. The scale for gratifications derived from video posting summed across the following items: “I feel a sense of achievement when I post a video on social media”; “I feel like I’m connecting to other people, often a large audience, when I post”; “I feel productive when I post; I feel like I’m competitive, competing with others when I post a video on social media”; “I get a sense of satisfaction when I post”; “I get a sense of achievement when I post videos on social media” (alpha = 0.88, MIC = 0.62). The scale for motivations for video posting activity summed across the following four items: “I just feel a need to express myself through videos on social media”; “I have a personal aesthetic to share with others”; “I want to share something with my family”; “I want to share something with friends” (alpha = 0.77, MIC = 0.45).

These two scales—motivations and gratifications—represent the relationship between intentions and behaviors, which are key concepts in research employing the Theory of Reasoned Action (Fishbein and Ajzen 1975) and its updated version, the Theory of Planned Behavior (Ajzen 1991). As noted earlier, the former says that one’s intention to engage in a behavior is the best predictor of actually doing so while the latter supports that relationship but recognizes situations where one does not have total control over the situation. Examining numerous empirical studies, Ajzen (1991) notes that both predictors, intentions and perceived behavioral control, correlate quite well with behavioral performance.

In this study, the scale of posting gratifications is positively related to how often one posts videos on social media (r = 0.46, p < 0.001) and the number of types of videos posted (r = 0.38, p < 0.001). Similarly, the scale of posting motivations is related to frequency of video posting (r = 0.44, p < 0.001) and the number of types posted (r = 0.39, p < 0.001). Although content creators have considerable control over their creative activities, they also are constrained by a host of other factors that make the relationship between intention and posting was less than perfect.

To measure the additional nine dependent variables—the use of various content types in social media videos—we asked: “Which of the following things do you often feature in the videos you post on social media?” Respondents checked all that applied, resulting in nine binary, dummy variables (with percent responding affirmatively in parentheses): Social issues (44 %), political ideas (36 %), things in the arts (34 %), stories that make people smile or laugh (40 %), pets/animals (43 %), beautiful images of nature/the environment (42 %), “how to” (educational) content (26 %), things that feature me and my lifestyle (32 %), and “my own image” (19 %).

Hierarchical multiple regression analyses were employed to assess the prediction of the motivations and gratifications scales. Hierarchical logistic regression analyses were used to examine the prediction of the nine video content types. For all analyses, the first five blocks were those listed in the Appendix. Tolerances revealed no substantial issues with multicollinearity.

3 Results and discussion

3.1 Predicting posting gratifications

The current study set out to test the utility of an integrative framework to determine how the creative influencers environment influences the degree of motivation for posting videos online, while profiling the gratifications derived from such activity. As Table 1 shows, each of the first four blocks of variables explains a significant amount of variance in posting gratifications. In the first block, three of the four indicators of the larger environment are significant unique predictors. Thus, those who live in communities where they believe the quality of life is higher experience greater posting gratifications, as do those who see themselves as “citizens of the world” (cosmopoliteness) and those feeling a stronger attachment to their communities. This provides support for H1a. Per the ascriptive factors (social categories) outlined in RQ1a, the second block reveals that stronger posting gratifications are experienced by younger, White, and male creators; other social categories are not significant (providing no support for the notion of a socioeconomic digital divide, but some support for a “gray gap”). As for RQ2a, several personality indicators in the third block are related to posting gratifications—being higher on conscientiousness, novelty-seeking, and narcissism. The fourth block, including use of legacy media, shows stronger posting gratifications experienced by those who read books and check email more often, supporting H2a. The final block of predictors included use of more recent technologies, and this block does not explain additional variance in posting gratifications, leaving H3a without support.

Table 1:

Predicting summary measure of gratifications received from posting videos on social media (multiple regression).

Blocks of predictors	Significant/near-significant βs	R ²	R ² change	F for change
1. Indicators of larger environment	Perceived quality of life (β = 0.20, p < 0.001) Community attachment (β = 0.37, p < 0.001) Cosmopoliteness (β = 0.17, p = 0.001)	0.35	0.35	42.1 p < 0.001
2. Social categories	Age (neg.) (β = −0.10, p = 0.03) White ethnicity (β = 0.10, p = 0.03) Female gender (neg.) (β = −0.11, p = 0.02)	0.37	0.03	2.2 p = 0.04
3. Personality indicators	Conscientiousness (β = 0.11, p = 0.04) Novelty-seeking (β = 0.19, p = 0.001) Narcissism (β = 0.25, p < 0.001)	0.48	0.11	7.6 p < 0.001
4. Use of legacy media	Reading books (β = 0.11, p = 0.05) Checking email (β = 0.09, p < 0.10)	0.51	0.04	2.4 p = 0.01
5. Use of newer technologies	Non-sig. block; all βs are also non-sig.	0.52	0.01	0.8 ns
Final Equation Significant/Near-Sig. βs: Block 1: perceived quality of life (β = 0.12, p = 0.02), community attachment (β = 0.21, p < 0.001) Block 2: age (neg.) (β = −0.08, p = 0.08), female gender (neg.) (β = −0.10, p = 0.03) Block 3: openness (neg.) (β = −0.09, p = 0.10), conscientiousness (β = 0.11, p = 0.06), novelty-seeking (β = 0.16, p = 0.01), narcissism (β = 0.17, p = 0.008) Block 4: reading books (β = 0.13, p = 0.03)

Note. R ² = 0.52, F(32, 290) = 9.8 p < 0.001; All tolerances are above 0.26.

In the final equation predicting gratifications from posting videos on social media, the following variables are statistically significant or near-significant: from Block 1, community attachment, perceived quality of life; from Block 2, age (neg.), gender/female (neg.); from Block 3, openness (neg.), conscientiousness, novelty-seeking, narcissism; from Block 4, reading books.

On balance, significant predictions can be found regarding posting gratifications from the environment and context that surrounds us, the enduring social categories that affect so much of our life, aspects of our personality that we often don’t even recognize, and the media menu that connects us to public life, including “classic” or older media such as books.

3.2 Predicting posting motivations

As Table 2 shows, four of the five blocks of predictor variables each explains a significant amount of variance in the strength of posting motivations. The same three indicators of the larger environment that are important in predicting gratifications appear as significant unique contributors here as well, providing support for H1b. In the second, near-significant block, males and those with a more liberal political philosophy are more strongly motivated to post videos on social media, providing some response to RQ1b (but providing no support for the notion of a digital divide). Per RQ2b, three of the seven personality indicators are related to stronger posting motivations—conscientiousness, novelty-seeking, and narcissism. The next block of predictors entered into the regression, use of legacy media, is non-significant, though the β for book reading is significant. Hypothesis 2b is not supported. The final block, use of newer technologies, accounts for a significant amount of variance, but only one individual predictor is significant—not streaming films on home TV. Hypothesis 3b is partially supported.

Table 2:

Predicting summary measure of motivations for posting videos on social media (multiple regression).

Blocks of predictors	Significant/near-significant βs	R ²	R ² change	F for change
1. Indicators of larger environment	Perceived quality of life (β = 0.28, p < 0.001) Community attachment (β = 0.24, p < 0.001) Cosmopoliteness (β = 0.18, p = 0.001)	0.32	0.32	37.2 p < 0.001
2. Social categories	Female gender (neg.) (β = −0.09, p = 0.06) Political philosophy (liberal) (β = 0.08, p = 0.08)	0.34	0.02	1.8 p = 0.10
3. Personality indicators	Conscientiousness (β = 0.15, p = 0.009) Novelty-seeking (β = 0.22, p < 0.001) Narcissism (β = 0.24, p < 0.001)	0.43	0.09	6.2 p < 0.001
4. Use of legacy media	Reading books (β = 0.12, p = 0.04)	0.46	0.02	1.4 ns
5. Use of newer technologies	Stream a movie on home TV (neg.) (β = −0.16, p = 0.03)	0.48	0.02	2.3 p < 0.05
Final Equation Significant/Near-Sig. βs: Block 1: perceived quality of life (β = 0.21, p < 0.001), community attachment (β = 0.11, p = 0.08) Block 2: female gender (neg.) (β = −0.11, p = 0.02), political philosophy (β = 0.10, p = 0.04) Block 3: conscientiousness (β = 0.13, p = 0.03), novelty-seeking (β = 0.20, p = 0.003), narcissism (β = 0.16, p = 0.02) Block 4: reading books (β = 0.12, p = 0.04) Block 5: stream a movie on home TV (neg.) (β = −0.16, p = 0.03)

Note. R ² = 0.48, F(32, 290) = 8.3 p < 0.001; All tolerances are above 0.26.

The following variables are statistically significant or near-significant in the final equation predicting the importance of motives for posting videos on social media: from Block 1, perceived quality of life, community attachment; from Block 2, gender/female (neg.), political philosophy; from Block 3, conscientiousness, novelty-seeking, narcissism; from Block 4, reading books; from Block 5, streaming films on home TV (neg.). In summary, we find a strong pattern of impacts on posting motivations from the larger environment in which people live, social categories, and personality factors, but not from the use of legacy media nor the use of newer technologies.

The creative people who post videos on social media no doubt draw on a host of sources for ideas and content. We would expect that the media environments, online and otherwise, are important in addition to one’s personal experiences and network of friends. Thus, it is surprising that legacy media and use of more recent technologies don’t appear as more important predictors in Tables 1 and 2. The relative non-significance of media and other sources of ideas as predictors of gratifications from posting activity and motivations for posting videos on social media led us to ask whether they were more important for what was posted, per RQ3’s query on predictors of posting various content types.

3.3 Predicting content types of videos posted

Tables 3 through 11 reveal a different mix of predictors for each video content category that our creative influencers post on social media. As shown in Table 3, video posting about social issues is predicted by five blocks—indicators of the larger environment, social categories, personality indicators, use of legacy media and use of newer technologies. From the first block, community attachment and cosmopoliteness are significant individual predictors, while three social categories are important—age (being young), White ethnicity, and being male. Three personality indicators are significant; those who are lower on openness, conscientiousness, and agreeableness are more likely to post content about social issues. Those who read newspapers online more frequently, go out to see films in a theater more often, watch films on tablets/computers/cell phones are more likely to post such content. In the final equation, the variables with significant Exp(B)s are being younger, male, reading newspapers online, and watching films on tablets/computers/cell phones.

Table 3:

Predicting content categories of videos posted on social media—social issues (logistic regression).

DV: Video content type	Blocks of predictors	Block Chi-sq.	Model Chi-sq.	Model R ²*
Social Issues	1. Indicators of larger environment Community attachment (Exp(B) = 1.35, p = 0.003) Cosmopoliteness (Exp(B) = 1.23, p = 0.03)	24.78 p < 0.001	24.78 p < 0.001	0.07
	2. Social categories Age (neg.) (Exp(B) = 0.96, p = 0.002) White ethnicity (Exp(B) = 2.56, p = 0.01) Female gender (neg.) (Exp(B) = 0.46, p = 0.002)	25.94 p < 0.001	50.72 p < 0.001	0.15
	3. Personality indicators Openness (neg.) (Exp(B) = 0.85, p = 0.01) Conscientiousness (neg.) (Exp(B) = 0.86, p = 0.02) Agreeableness (neg.) (Exp(B) = 0.88, p = 0.04)	26.81 p < 0.001	77.52 p < 0.001	0.21
	4. Use of legacy media Reading newspapers online (Exp(B) = 1.20, p < 0.05) Going out to see films in a theater (Exp(B) = 1.27, p = 0.07)	26.51 p < 0.01	104.03 p < 0.001	0.28
	5. Use of newer technologies Watch a film on a tablet/computer/cell phone (Exp(B) = 1.59, p = 0.002)	11.29 p < 0.05	115.31 p < 0.001	0.30
	6. Summary measures of U&G Both Exp(B)s are non-sig.	1.06 ns	116.38 p < 0.001	0.30
Final Equation Sig./Near-sig. Exp(B)s: Block 2: age (neg.) (Exp(B) = 0.97, p = 0.07), female gender (neg.) (Exp(B) = 0.58, p = 0.07) Block 4: reading newspapers online (Exp(B) = 1.23, p = 0.03) Block 5: watch a film on a tablet/computer/cell phone (Exp(B) = 1.59, p = 0.002)

Note. Model -2 Log likelihood = 326.66, Model chi-square = 116.38, p < 0.001; *-Cox & Snell R ².

A second content category—political ideas—also is predicted by the same five blocks of variables but the key individual predictors from each block often differ. We see in Table 4 that stronger community attachment is related to posting videos with political ideas, as is being White, male, or from higher income households. Posting videos with political ideas also is more likely with those who are less open or less conscientious but more novelty-seeking. Four media variables are linked to posting videos with political ideas—watching TV, going out to see films in theaters, streaming TV programs on home TV, and watching films on tablets/computers/cell phones. In the final equation, the variables with significant or near-significant Exp(B)s are perceived happiness, male gender, household income, watching TV, reading newspapers online, going out to see films in theaters more often, not streaming TV programs on home TV, and watching films on tablets/computers/cell phones.

Table 4:

Predicting content categories of videos posted on social media—political ideas (logistic regression).

DV: Video content type	Blocks of predictors	Block Chi-sq.	Model Chi-sq.	Model R ²*
Political ideas	1. Indicators of larger environment Community attachment (Exp(B) = 1.30, p = 0.01)	15.72 p < 0.01	15.72 p < 0.01	0.05
	2. Social categories White ethnicity (Exp(B) = 2.12, p < 0.05) Female gender (neg.) (Exp(B) = 0.42, p = 0.001) Income (Exp(B) = 1.12, p = 0.08)	21.36 p < 0.01	37.08 p < 0.001	0.11
	3. Personality indicators Openness (neg.) (Exp(B) = 0.82, p = 0.004) Conscientiousness (neg.) (Exp(B) = 0.89, p = 0.07) Novelty-seeking (Exp(B) = 1.40, p < 0.10)	23.84 p < 0.01	60.93 p < 0.001	0.17
	4. Use of legacy media Watching TV (Exp(B) = 1.31, p = 0.03) Going out to see films in a theater (Exp(B) = 1.45, p = 0.009)	40.86 p < 0.001	101.78 p < 0.001	0.27
	5. Use of newer technologies Stream a TV program on home TV (neg.) (Exp(B) = 0.73, p = 0.07) Watch a film on a tablet/computer/cell phone (Exp(B) = 1.47, p = 0.02)	11.32 p < 0.05	113.10 p < 0.001	0.30
	6. Summary measures of U&G Both Exp(B)s are non-sig.	3.49 ns	116.59 p < 0.001	0.30
Final Equation Sig./Near-sig. Exp(B)s: Block 1: perceived happiness (Exp(B) = 1.30, p = 0.08) Block 2: female gender (neg.) (Exp(B) = 0.57, p = 0.07), income (Exp(B) = 1.21, p = 0.01) Block 4: watching TV (Exp(B) = 1.35, p = 0.02), reading newspapers online (Exp(B) = 1.20, p = 0.07), going out to see films in a theater (Exp(B) = 1.44, p = 0.02) Block 5: stream a TV program on home TV (neg.) (Exp(B) = 0.74, p = 0.08), watch a film on a tablet/computer/cell phone (Exp(B) = 1.45, p = 0.02)

Note. Model -2 Log likelihood = 304.02, Model chi-square = 116.59, p < 0.001; *-Cox & Snell R ².

As outlined in Table 5, for predicting posting videos about things in the arts, only one block of variables is (near-) significant, that with the two summary variables of gratifications from posting videos and motivations for posting them. And only the summary measure of gratifications makes a unique contribution. It is somewhat surprising that posting videos about arts and culture isn’t connected to quality of life, for example, or the use of legacy media and new technologies that dwell on music, theater, or other artistic activities, but neither of those blocks are important. In the final equation, the variables with significant or near-significant Exp(B)s are community attachment, (lower) neuroticism, watching TV less often, and reading newspapers online more often.

Table 5:

Predicting content categories of videos posted on social media—things in the arts (logistic regression).

DV: Video content type	Blocks of predictors	Block Chi-sq.	Model Chi-sq.	Model R ²*
Things in the arts	1. Indicators of larger environment All three Exp(B)s are non-sig.	5.56 ns	5.56 ns	0.02
	2. Social categories All six Exp(B)s are non-sig.	2.64 ns	8.19 ns	0.03
	3. Personality indicators Agreeableness (neg.) (Exp(B) = 0.89, p = 0.06)	9.96 ns	18.15 ns	0.06
	4. Use of legacy media Reading newspapers online (Exp(B) = 1.17, p = 0.05)	14.29 ns	32.44 ns	0.10
	5. Use of newer technologies All five Exp(B)s are non-sig.	2.19 ns	34.63 ns	0.10
	6. Summary measures of U&G Gratifications from posting videos (Exp(B) = 1.07, p = 0.03)	11.95 p < 0.01	46.57 p < 0.10	0.13
Final Equation Sig./Near-sig. Exp(B)s: Block 1: community attachment (neg.) (Exp(B) = 0.67, p = 0.002) Block 3: neuroticism (neg.) (Exp(B) = 0.88, p = 0.06) Block 4: watching TV (neg.) (Exp(B) = 0.84, p = 0.06), reading newspapers online (Exp(B) = 1.18, p = 0.05)

Note. Model -2 Log likelihood = 370.35, Model chi-square = 46.57, p = 0.06; *-Cox & Snell R ².

Humor is a key feature of posts on social media, reflected in our content type of “things that make people smile or laugh.” In Table 6, only two of the six blocks explain a significant amount of variance—the blocks for using legacy media and newer technologies. Individual variables making significant contributions are watching TV, reading hard copy newspapers less often, reading newspapers online more often, reading magazines less often, and surfing the internet for pleasure less often. Also, those who stream TV programs on home TV and have greater enthusiasm for technology also are more likely to post videos with things that make people smile or laugh. In the final equation, the variables with significant or near-significant Exp(B)s are authoritarianism, not reading hard copy newspapers, reading online newspapers, not reading magazines, not surfing the internet for pleasure, streaming TV programs on home TV, and enthusiasm for technology.

Table 6:

Predicting content categories of videos posted on social media—things that make people smile or laugh (logistic regression).

DV: Video content type	Blocks of predictors	Block Chi-sq.	Model Chi-sq.	Model R ²*
Things that make people smile or laugh	1. Indicators of larger environment All three Exp(B)s are non-sig.	4.65 ns	4.65 ns	0.01
	2. Social categories Political philosophy (liberal) (Exp(B) = 1.16, p < 0.05)	6.20 ns	10.85 ns	0.03
	3. Personality indicators All eight Exp(B)s are non-sig.	7.15 ns	18.00 ns	0.05
	4. Use of legacy media Watching TV (Exp(B) = 1.27, p = 0.01) Reading newspapers hard copy (neg.) (Exp(B) = 0.85, p = 0.06) Reading newspapers online (Exp(B) = 1.21, p = 0.01) Reading magazines (neg.) (Exp(B) = 0.84, p = 0.06) Surfing the Internet for pleasure (neg.) (Exp(B) = 0.86, p = 0.08)	22.70 p < 0.01	40.71 p < 0.05	0.12
	5. Use of newer technologies Stream a TV program on home TV (Exp(B) = 1.31, p = 0.06) Technology enthusiasm (Exp(B) = 1.43, p = 0.04)	9.44 p < 0.10	50.15 p < 0.05	0.14
	6. Summary measures of U&G Both Exp(B)s are non-sig.	2.00 ns	52.15 p < 0.05	0.15
Final Equation Sig./Near-sig. Exp(B)s: Block 3: authoritarianism (Exp(B) = 1.30, p = 0.08) Block 4: reading newspaper hard copy (neg.) (Exp(B) = 0.84, p = 0.06), reading newspapers online (Exp(B) = 1.20, p = 0.03), reading magazines (neg.) (Exp(B) = 0.83, p = 0.05), surfing the Internet for pleasure (neg.) (Exp(B) = 0.84, p = 0.05) Block 5: stream a TV program on home TV (Exp(B) = 1.28, p = 0.09), technology enthusiasm (Exp(B) = 1.39, p = 0.07)

Note. Model -2 Log likelihood = 383.26, Model chi-square = 52.15, p = 0.02; *-Cox & Snell R ².

Videos about pets or animals are abundant on social media, and as displayed in Table 7, three blocks explain significant or near-significant amounts of variance in posting such content—social categories, use of legacy media, and the final summary measures of motivations and gratifications. Videos about pets or animals are more likely to be posted by Whites, those who watch TV more often, listen to the radio more often, and read magazines less often. In the final equation, the variables with significant or near-significant coefficients are cosmopoliteness, novelty-seeking (neg.), watching TV, listening to the radio, and reading magazines (neg.).

Table 7:

Predicting content categories of videos posted on social media—pets or animals (logistic regression).

DV: Video content type	Blocks of predictors	Block Chi-sq.	Model Chi-sq.	Model R ²*
Pets or animals	1. Indicators of larger environment All three Exp(B)s are non-sig.	3.22 ns	3.22 ns	0.01
	2. Social categories White ethnicity (Exp(B) = 2.36, p = 0.02)	10.76 p < 0.10	13.98 ns	0.04
	3. Personality indicators Conscientiousness (neg.) (Exp(B) = 0.91, p = 0.09)	8.98 ns	22.96 ns	0.07
	4. Use of legacy media Watching TV (Exp(B) = 1.24, p = 0.02) Listening to radio (Exp(B) = 1.15, p < 0.05) Reading magazines (neg.) (Exp(B) = 0.82, p = 0.03)	17.58 p < 0.05	40.54 p < 0.05	0.12
	5. Use of newer technologies All five Exp(B)s are non-sig.	1.90 ns	42.44 p < 0.10	0.12
	6. Summary measures of U&G Both Exp(B)s are non-sig.	6.46 p < 0.05	48.89 p < 0.05	0.14
Final Equation Sig./Near-sig. Exp(B)s: Block 1: cosmopoliteness (Exp(B) = 1.23, p = 0.08) Block 3: novelty-seeking (neg.) (Exp(B) = 0.67, p = 0.06) Block 4: watching TV (Exp(B) = 1.21, p = 0.06), listening to radio (Exp(B) = 1.16, p = 0.04), reading magazines (neg.) (Exp(B) = 0.81, p = 0.03)

Note. Model -2 Log likelihood = 392.59, Model chi-square = 48.89, p = 0.04; *-Cox & Snell R ².

Videos featuring beautiful images of the environment also are popular social media posts, for example, hiking or travel posts. In this logistic regression, shown in Table 8, two blocks made significant contributions—use of legacy media and the final block with the two summary measures of motivations and gratifications. Two variables made individual contributions—reading books and the summary measure of motivations for posting videos on social media. In the final equation, the variables with significant or near-significant Exp(B)s are reading newspapers online, being less enthusiastic for technology, and having stronger motivations for posting videos.

Table 8:

Predicting content categories of videos posted on social media—beautiful images of the environment (logistic regression).

DV: Video content type	Blocks of predictors	Block Chi-sq.	Model Chi-sq.	Model R ²*
Beautiful images of the environment	1. Indicators of larger environment All three Exp(B)s are non-sig.	7.00 ns	7.00 ns	0.02
	2. Social categories Education (Exp(B) = 1.56, p = 0.01)	8.37 ns	15.37 ns	0.05
	3. Personality indicators Conscientiousness (neg.) (Exp(B) = 0.91, p < 0.10)	8.51 ns	23.88 ns	0.07
	4. Use of legacy media Reading books (Exp(B) = 1.16, p = 0.05)	19.03 p < 0.05	42.91 p < 0.05	0.12
	5. Use of newer technologies All five Exp(B)s are non-sig.	4.93 ns	47.84 p < 0.05	0.14
	6. Summary measures of U&G Motivations for posting videos (Exp(B) = 1.17, p = 0.002)	11.25 p < 0.01	59.08 p < 0.01	0.17
Final Equation Sig./Near-sig. Exp(B)s: Block 4: reading newspapers online (Exp(B) = 1.15, p < 0.10) Block 5: technology enthusiasm (neg.) (Exp(B) = 0.73, p = 0.09) Block 6: motivations for posting videos (Exp(B) = 1.17, p = 0.002)

Note. Model -2 Log likelihood = 380.60, Model chi-square = 59.08, p = 0.003; *-Cox & Snell R ².

There is great diversity in the tasks featured in “how to” or educational videos, including, for example, cooking or food videos (Jeffres et al. 2023). In our analyses, shown in Table 9, we find that three of the blocks make significant contributions—indicators of the larger environment, use of legacy media, and the final block with the two summary measures. Three individual variables make significant contributions—community attachment, not checking email, and the summary measure of gratifications from posting videos. In the final equation, the variables with significant or near-significant Exp(B)s are quality of life, listening to the radio, checking email less often, and receiving stronger gratifications from posting videos on social media.

Table 9:

Predicting content categories of videos posted on social media—how to…educational videos (logistic regression).

DV: Video content type	Blocks of predictors	Block Chi-sq.	Model Chi-sq.	Model R ²*
How to....educational videos	1. Indicators of larger environment Community attachment (Exp(B) = 1.27, p = 0.04)	13.44 p < 0.01	13.44 p < 0.01	0.04
	2. Social categories All six Exp(B)s are non-sig.	3.64 ns	17.08 p < 0.10	0.05
	3. Personality indicators All eight Exp(B)s are non-sig.	1.43 ns	18.51 ns	0.06
	4. Use of legacy media Checking email (neg.) (Exp(B) = 0.75, p = 0.03)	15.25 p < 0.10	33.76 ns	0.10
	5. Use of newer technologies All five Exp(B)s are non-sig.	4.48 ns	38.24 ns	0.11
	6. Summary measures of U&G Gratifications from posting videos (Exp(B) = 1.10, p = 0.02)	12.63 p < 0.01	50.87 p < 0.05	0.15
Final Equation Sig./Near-sig. Exp(B)s: Block 1: perceived quality of life (Exp(B) = 1.38, p < 0.05) Block 4: listening to radio (Exp(B) = 1.18, p = 0.05), checking email (neg.) (Exp(B) = 0.68, p = 0.006) Block 6: gratifications from posting videos (Exp(B) = 1.10, p = 0.02)

Note. Model -2 Log likelihood = 232.48, Model chi-square = 50.87, p = 0.02; *-Cox & Snell R ².

The final two content types reflect on one’s persona: Things featuring me and my lifestyle, and “my own image,” the results for which are displayed in Tables 10 and 11. The only block explaining a significant amount of variance in videos about me and my lifestyle is the final block, where the summary measure for motivations for posting videos makes an individual contribution as well. In the final equation, the variables with significant or near-significant Exp(B)s are less community attachment, being younger, surfing the internet for pleasure less often, and having stronger motivations for posting videos.

Table 10:

Predicting content categories of videos posted on social media—things featuring me and my lifestyle (logistic regression).

DV: Video content type	Blocks of predictors	Block Chi-sq.	Model Chi-sq.	Model R ²*
Things featuring me and my lifestyle	1. Indicators of larger environment Perceived quality of life (Exp(B) = 1.24, p = 0.08)	7.15 ns	7.15 ns	0.02
	2. Social categories Age (neg.) (Exp(B) = 0.98, p = 0.08)	4.77 ns	11.92 ns	0.04
	3. Personality indicators Extraversion (Exp(B) = 1.12, p = 0.04)	7.46 ns	19.38 ns	0.06
	4. Use of legacy media Watching TV (Exp(B) = 1.18, p = 0.09) Surfing the Internet for pleasure (neg.) (Exp(B) = 0.78, p = 0.005)	13.90 ns	33.38 ns	0.10
	5. Use of newer technologies All five Exp(B)s are non-sig.	1.99 ns	35.27 ns	0.10
	6. Summary measures of U&G Motivations for posting videos (Exp(B) = 1.14, p = 0.02)	16.78 p < 0.001	52.05 p < 0.05	0.15
Final Equation Sig./Near-sig. Exp(B)s: Block 1: community attachment (neg.) (Exp(B) = 0.76, p = 0.04) Block 2: age (neg.) (Exp(B) = 0.98, p = 0.08) Block 4: surfing the Internet for pleasure (neg.) (Exp(B) = 0.76, p = 0.004) Block 6: motivations for posting videos (Exp(B) = 1.14, p = 0.02)

Note. Model -2 Log likelihood = 349.27, Model chi-square = 52.05, p = 0.02; *-Cox & Snell R ².

Table 11:

Predicting content categories of videos posted on social media—“my own image” (logistic regression).

DV: Video content type	Blocks of predictors	Block Chi-sq.	Model Chi-sq.	Model R ²*
“My own image”	1. Indicators of larger environment All three Exp(B)s are non-sig.	7.51 ns	7.51 ns	0.02
	2. Social categories Age (neg.) (Exp(B) = 0.97, p = 0.05)	7.09 ns	14.60 ns	0.04
	3. Personality indicators Conscientiousness (Exp(B) = 1.14, p = 0.09)	7.37 ns	21.97 ns	0.07
	4. Use of legacy media Watching TV (Exp(B) = 1.31, p = 0.04) Reading books (Exp(B) = 1.23, p = 0.04)	15.15 p < 0.10	37.12 p < 0.10	0.11
	5. Use of newer technologies All five Exp(B)s are non-sig.	5.01 ns	42.13 p < 0.10	0.12
	6. Summary measures of U&G Gratifications from posting videos (Exp(B) = 1.09, p = 0.05)	8.02 p < 0.05	50.15 p < 0.05	0.14
Final Equation Sig./Near-sig. Exp(B)s: Block 2: age (neg.) (Exp(B) = 0.97, p = 0.06) Block 6: gratifications from posting videos (Exp(B) = 1.09, p = 0.05)

Note. Model -2 Log likelihood = 259.94, Model chi-square = 50.15, p = 0.03; *-Cox & Snell R ².

In the prediction of the last content category, “my own image,” only the blocks of use of legacy media and the final block of summary measures are important. Looking at individual variables we find that watching TV, reading books, and finding greater gratifications from posting videos are significant predictors of posting “my own image” in social media videos. In the final equation, the variables with significant or near-significant Exp(B)s are younger age and stronger gratifications. Surprisingly, narcissism doesn’t appear as a key predictor, given its prominence elsewhere (e.g., Jeffres et al. 2023). The trait was modestly correlated with posting one’s own image (r = 0.14, p < 0.01), but not with posts that “feature me and my lifestyle.” Such posting behavior may thus reflect an outward look rather than an inward focus on one’s life.

Examining final individual coefficients for patterns across the content types, we find that reading newspapers online is the most robust predictor, appearing in five of the nine equations. Several other measures of legacy media use are important, but in variegated fashion—watching TV predicts posting videos with political ideas and videos with pets/animals; not using several different legacy media predicts posting humorous video; watching movies at the theater is related to posting videos with political ideas. Predictions by use of newer technologies are equally scattered. Media sources seem to operate differentially in the prediction of specific online video types, perhaps serving differentially as motivators or seeds for the creation of different content.

Variables from both the social categories and personality variables blocks also appear as significant contributors in scattered fashion, demonstrating principally that the type of content is critical. Of the indicators of the larger environment, all four appear in final equations, but variously across content types. And the two summary measures of gratifications received from posting videos on social media and motivations for posting appear twice in final equations—only those related to the creative influencer’s individual “persona.”

The answer to RQ3 seems to be that there are “different influences for different types of content.” This can be explained by a “content trumps form” theme that has been encountered in a range of research endeavors over the past 20 years (e.g., Wu and Atkin 2023). Content type (in general) has eclipsed form in studies of modality of delivery (text vs. audio vs. video), in studies of screen size and 3D video, and in studies of theatrical versus home viewing (Jeffres et al. 2023).

Overall, mixed support was found for the model proposed in Figure 1, although we did establish this collection of constructs as important in predicting the strength of gratifications received and motivations sought by posting videos on social media. The collection of larger environment factors, social categories, personality indicators, and use of media (legacy and newer) does help carve out a social media “creator culture” for the production of online videos (Cunningham and Craig 2021). Additionally, the study’s findings offer a clear signpost that an additional component could be added to Figure 1 model—that of “content types” for online videos, as a moderator for the variegated predictive power of the model components.

On balance, multiple regression analyses of national survey data explain roughly half or more of the variance in our models for predicting (1) motivations for and (2) gratifications received from posting videos on social media. Study findings provide support for an integrative framework that supports new constructs--exploring the role of gratifications involving posting motivations, social categories and personality factors--but not the use of legacy media nor newer technologies. This underscores the notion of “different influences for different types of content,” which can be explained by a “content trumps form” theme that has emerged in recent research (e.g., Jeffres et al. 2023). Study findings thus help profile a social media “creator culture” supporting the production of online videos, including “content types” for online videos, as a moderator for the variegated predictive power of the model components. Later work could profitably move beyond simply different media modalities and focus on content-specific measures involving videos posted and consumed.

Corresponding author: David Atkin, University of Connecticut, Storrs, USA, E-mail: djatkinmail@yahoo.com

Article Note: This article underwent single-blind peer review.

Appendix: List of predictor blocks and variables for regressions and logistic regressions

Block 1. Indicators of larger environment Perceived quality of life Community attachment Cosmopoliteness Perceived happiness	Block 4. Use of legacy media Watching TV Listening to radio Reading newspapers hard copy Reading newspapers online Reading magazines Reading books Going out to see films in a theater Checking email Surfing Internet for pleasure
Block 2. Social categories Age White ethnicity Female gender Education Income Political philosophy (liberal)	Block 5. Use of newer technologies Stream a movie on home TV Stream a TV program on home TV Watch a film on a tablet, computer, or cell phone Watch a TV program on a tablet, computer, or cell phone Technology enthusiasm
Block 3. Personality indicators Openness Conscientiousness Extraversion Agreeableness Neuroticism Novelty-seeking Authoritarianism Narcissism	[Block 6. Summary measures of U&G Gratifications from posting videos Motivations for posting videos]* *-Block 6 included in logistic regressions only

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Received: 2024-06-17

Accepted: 2024-11-11

Published Online: 2024-12-03

Published in Print: 2024-12-17

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

https://doi.org/10.1515/omgc-2024-0022

Keywords for this article

survey; creative content influences; uses and gratifications; social media videos

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