Instagramming Social Presence: A Test of Social Presence Theory and Heuristic Cues on Instagram Sponsored Posts

Abstract

This study investigates social presence theory, using sponsored posts on Instagram. By testing a 3 (social presence) × 2 (heuristic cues) × 2 (source of sponsorship) mixed factorial experiment (N = 378), the results showed significant main effects between social presence, heuristic cues, and sponsorship sources on social media outcomes. In sum, higher purchase intention and the intention to click “Like” generated higher social presence, higher “Likes” (heuristic), and official sponsorship sources. Our findings provide empirical evidence on how to effectively deliver sponsored content on Instagram.

Keywords

social presence Instagram heuristic cues source

Social presence is an important concept in the pursuit to understand psychological and physiological media immersion, and corresponding behavior (Biocca, Harms, & Burgoon, 2003). For instance, the presence of others automatically activates impression management concerns, makes words that are applicable to social desirability more accessible (Puntoni & Tavassoli, 2007), and provides a higher sense of presence in a virtual store or mediated environment (T. Kim & Biocca, 1997). This higher sense of presence can lead to audiences being more confident in regard to their attitudes toward a product or brand (T. Kim & Biocca, 1997). As computer-mediated communication (CMC) has dominated people’s everyday conversation platform, recent business communication studies have attempted to establish a physical versus psychological immersion divide in terms of how presence impacts engagement and other variables (Lombard & Ditton, 1997). Physical immersion involves tactile experiences with technologies, while psychological immersion or presence more so involves the feeling that a person is there (Lombard & Ditton, 1997). More recent research has attempted to understand social presence’s psychological and behavioral effects in the context of social media, since social media are rapidly replacing or supplementing face-to-face interactions in terms of delivering corporate and marketing messages whereby companies employ social media to directly communicate with target audiences (Mangold & Faulds, 2009). People trust content with an interactive online source when they experience the presence of an actual human in a virtual space (Shen, 2012). For example, the feeling of social presence is a positive predictor of purchase intention in online shopping (Beldad, De Jong, & Steehouder, 2010).

Recent marketing literature recommends that companies pay social media influencers to promote products on platforms like Instagram, since it wields over 800 million users (Levy, 2017; Morrison, 2017; Wykes, 2017). Because Instagram features predominantly images rather than text, it conveniently defines and articulates the practice of social presence in the current business communication sphere. With Instagram’s interactive culture, the current experiment examines the practice of social presence interacting with the following features: source (exposing the source of sponsorship brand vs. an Instagram user) and (heuristic cues: the number of “Likes”—higher and lower).

Social Presence Theory

Social presence theory (SPT) is derived from social psychology and was developed by Short, Williams, and Christie (1976). They posed social presence as “being there” in the mediated communication exchange between parties. Some media have higher social presence than others (e.g., video more than still media, for instance). SPT specifies how users might choose communication channels, based on varying capabilities of media to create awareness of other users (or presence of these other humans or actors; Mennecke et al., 2011). Since SPT was originally posited, the definition of social presence has garnered no universal concept or operational definition (Tu, 2001). Due to a lack of agreement on how to measure social presence, studies on social presence have followed different trajectories. For instance, older definitions of social presence understood the concept as an attribute of a given medium (Short et al., 1976), while newer definitions suggest that social presence is influenced by a medium, but more important, involves interactivity and learning environments and the individual perspective (Gunawardena, 1995). Presence has also been defined as the feeling of being in the media environment (Biocca, 1997), which is similar to the Short et al. (1976) original definition.

As computers have become more commonplace in society for information and communication purposes, research on social presence has become more popular in the context of CMC. Some researchers began to see that CMC could help foster social presence and that real connections and relationships could develop through the help of CMC experiences (Biocca et al., 2003; Hiltz & Turoff, 1993). For instance, students who have a greater perceived social presence of an instructor reported higher perceived learning and satisfaction with that instructor (Richardson & Swan, 2003).

Gefen and Straub (2004) showed that social presence can positively influence e-commerce because personable and human-like photos and warmth in text-based content create interactivity in the social media environment. Therefore, social presence can influence message effectiveness (Li, Daugherty, & Biocca, 2002). Our research proposes that human presence (a person using the product in the frame of an Instagram photo) can increase engagement and purchase intention due to the feeling of presence and connection with the person in a photo. We expect that when a person sees a human being actively interacting with a product in an Instagram post, that post is more motivationally relevant and engaging than a post without a human presence. This type of social presence can also make the post seem more intimate, sociable, sensitive, and personal which creates a psychological immersion in the absence of an actual physically immersive experience. In short, social presence becomes an attribute of the message when it is operationalized as how much a human being is perceived in the photo.

The level of social presence as the appearance of a human is measured using three subdimensions (T. Kim & Biocca, 1997; Mallmann & Maçada, 2018). This is accomplished by (a) Access, which is defined as the feeling of being more accessible and having more access to another person, (b) Shared Environment, which is defined by feeling of being in the same space (e.g., in the same room), and (c) Proximity, which is defined as the feeling of being close to another person (Biocca et al., 2003; Biocca & Harms, 2002; Mennecke et al., 2011; Nowak & Biocca, 2003). Our research makes social presence a dimensional concept by manipulating human presence, keeping the aforementioned dimensions of access, shared environment, and proximity in mind. In short, we operationalized the level of social presence as follows: high by showing the face of the human(s), medium by showing a part of body (e.g., hands), and low by not presenting a human face or other human body part in the picture, but showing only a product. Using this operationalization, we hypothesize:

Hypothesis 1: The level of social presence in a sponsored post will influence (a) the intention to click “Like” and (b) the intention to purchase the product.

Source Effect

Eagly, Wood, and Chaiken (1978) conducted early research related to source credibility. Their research showed that people had their own expectations and biases about information sources, which lead to how any given information was interpreted. In regard to biases about information sources, people naturally create their own “arm chair” theories about why the source person is communicating certain information and, thus, about whether the individual could be trusted. With that, source, a construct shaped by what the receiver believes it to be, is often conceptualized as subjective and psychological, rather than as absolute and unconditional (Sundar & Nass, 2001). Past research suggested that this carries over to on social networking sites where consumers can be easily influenced by the source of information (Booth & Matic, 2011; Chatterjee, 2015).

Therefore, testing the source in social media must be taken into account. Advertising directly by the brand versus advertising by a lay person can produce different results for messaging and social media outcomes. Consumer-generated brand messages are more likely to be recommended by consumers (Chatterjee, 2015). Thus, content generated by lay people may be more effective in social media and may trigger perceived homophily and similarity on Instagram, where user-generated content on life experiences is commonly published (Morrison, 2017). For instance, customers prefer a visible interpersonal-like source who is perceived as more familiar, trustworthy, friendly, and engaging to an official product maker or advertiser (Dou et al., 2012). Testimonials and other people’s opinions operate as a decision making, motivated cognitive shortcut (Metzger, Flanagin, & Medders, 2010). Since humans are known to be cognitive misers and motivated to process information as efficiently as possible with limited cognitive resources (Lang, 2000), it is compelling to argue that a non(official interpersonal-like sources on Instagram may be more persuasive in terms of engagement and purchase intention. Thus, this research poses the following hypothesis:

Hypothesis 2: The source of a sponsored posts will differently influence (a) the intention to click “Like” and (b) the intention to purchase.

Bandwagon Effects of Numeric Cues

A high number of “Likes” on a post can be perceived as a social recommendation. Those “Likes” are defined as cues showing that a large number of individuals support the content (Halaszovich & Nel, 2017; Pöyry, Parvinen, & Malmivaara, 2013). This phenomenon is generally interpreted as the bandwagon effect when individuals are likely to assume that something is legitimate if many others agree (Sundar, 2008). With technological advancements in digital media, it is easy to spot aggregated information about “what others are doing, listening, watching, reading, and thinking” (Sundar, 2008, p. 84), especially with heuristic cues indicating what’s popular and being “Liked” by other users (Sundar, 2008). In our experiment, the number of ‘Likes’ is operationalized as a heuristic cue that triggers bandwagon heuristics as an indication of the content’s impact in the persuasive process.

Our article is focused on its effect on Instagram posting, which does not require a long decision time. Hilligoss and Rieh (2008) indicated that heuristics work when people make quick judgments. Taken together, we form the third hypothesis and the following research questions. Because the connection between social presence, heuristic cues, and source has not been explicitly established in past research (e.g., T. Kim & Biocca, 1997; X. J. Lim, Cheah, & Wong, 2017; Mallmann & Maçada, 2018), the current study seeks to explore whether these three variables interact to influence effectiveness perceptions in Research Questions 1 and 2.

Hypothesis 3: The number of “Likes” will differently influence (a) the intention to click “Like” and (b) the intention to purchase.

Research Question 1: Will there be any interaction effect among social presence, source of the sponsorship and number of “Likes” on the intention to click “Like”?

Research Question 2: Will there be any interaction effect among social presence, source of the sponsorship and number of “Likes” on the intention to purchase?

Method

Study Design

This study used a 3 (social presence: high vs. medium vs. low) × 2 (source of sponsorship: brand’s official account vs. lay user’s account) × 2 (heuristic cues: high number of “Likes” vs. low number of “Likes”) mixed-respondents factorial experimental design where social presence served as the within factor while source and heuristic cues served as the between factor.

Sample

A total of N = 430 Instagram user participants were originally recruited by the Qualtrics Panel team, which has a reputation of valid population representative data collection (Brandon et al., 2013). The Qualtrics team received a payment from the researchers for access to their panel and handled the compensation of respondents. Participants first opened the link to the online survey including the main study questions as well as questions to determine whether respondents were paying attention to the stimuli. Because this study was designed as an online experiment, the quality of data had to be addressed. (LaRose & Tsai, 2014). To prevent possible issues, Qualtrics omitted respondents who did not complete the survey or failed the attention check questions. Also, researchers employed additional data screening steps. First, participants who spent too brief of a time on the survey (less than 3 minutes on average) were excluded. This accounted for 10% of the population being excluded. Second, participants who failed to show evidence of having verified Instagram accounts were excluded. Third, if users were not actively using Instagram with the criteria of having at least 50 followers, their data were excluded. Fourth, data from participants under 20 were excluded because our stimuli showed an alcoholic beverage brand. Finally, data collected from incorrect IP addresses (e.g., outside of the United States) was omitted. As a result, N = 376 participants remained for the main analysis.

Participants ranged in age from 20 to 69 with an average age of 32 (SD = 8.72). Some 73% of participants were female (n = 278). In terms of educational background, the majority of them were college graduates (48%, n = 178). Some 39% were high school graduates (n = 147); 12% held masters degrees and higher (n = 46).

On average, as Instagram users, they had 460 (SD = 1309.34) followers and were following 379 accounts (SD = 722.63) at the time of data collection. The majority of participants were Caucasian (72%, n = 271), 6% (n = 23) were African American, 11% (n = 42) were Hispanics, 10% (n = 35) were Asian, and 2% identified themselves as “other” (n = 5).

Procedure

Participants received an online link once they started the experiment. They were randomly assigned to one of the four conditions where social presence was assessed as a within variable. Prior to reading the stimuli, participants were asked to indicate their prior attitude toward Starbucks and Budweiser brands. The reasons why Starbucks and Budweiser were chosen were: (a) they are top brands actively utilizing their Instagram accounts and (b) they publish multiple messages which prevented the study results from being skewed by idiosyncrasies of a single message (Reeves & Geiger, 1994; Slater, Peter, & Valkenburg, 2015). Next, participants were exposed to an Instagram post with questions about their perceived level of social presence (as a manipulation check), their intention to click “Like,” and their intention to purchase. The stimulus, an Instagram post, was composed of one picture and one line of text with the name of source (official brand name vs. nonofficial name). This set was repeated six times (two multiple messages × three levels of social presence) per condition (see Table 1 for experimental design). The order of stimuli was randomized by Qualtrics. The procedure was identical across the four conditions. Upon completing the questionnaire, each subject was debriefed and received compensation from Qualtrics. The online experiment lasted about 20 minutes on average.

Stimuli

The participants were asked to view six Instagram posts (two high social presence/the product with a person’s face, two medium social presence/a person’s hand holding the product, and two low social presence/the product only) per condition. Source was operationalized as the official brand’s or lay person’s Instagram accounts. Heuristic cues were indicated by the number of “Likes” the Instagram post received (e.g., high condition/more than 6,000 “Likes” vs. low condition/less than 5 “Likes,” see Figure 1 for examples).

Measurements

Independent Variables

Social presence

To check its manipulation, three items were used from previous literature on a 5-point scale: “I am feeling more accessible to and of having more access to this brand on Instagram,” “I feel like I am in the same space with the brand on Instagram,” “I feel like I am close to this brand on Instagram.” (Biocca et al., 2003; α = .94).

Heuristic cue

The level of heuristic cue was manipulated by the number of “Likes” that the Instagram post received from other users. It was measured with an item from previous literature (Hong & Cameron, 2018) for a manipulation check: “I see that many people liked this post.”

Dependent Variables

Intention to click “Like.”

Two items (“the post is worthy to click like,” “I would click like for this post”) were used to measure the participants’ intentions to click “Like” on the sponsored posts on Instagram (López et al., 2017; Marder et al., 2016) on a 5-point Likert-type scale (r = .95, p < .001).

Intention to Purchase

Two items from a study of Hong and Park (2012) were adapted to gauge the intention to purchase a product from sponsored posts on Instagram. For instance, “I would like to purchase the products from brand name,” “I intend to purchase products from brand name” on a 5-point Likert-type scale (r = .98, p < .001).

Results

Manipulation Checks

Since social presence served as a within-respondents factor, a repeated measures analysis of variance (ANOVA) was used to check the manipulation. The data showed that participants perceived social presence differently, F(2, 744) = 15.028, p < .001, partial η² = .039, power = .99, which showed that the social presence manipulation was successful. Consistent with our operationalization, the high level of social presence had the highest score, (M = 2.88, SD = 1.11), followed by the medium level of social presence (M = 2.80, SD = 1.12), and the low level of social presence (M = 2.72, SD = 1.11). Differences among three levels were tested and confirmed by a post hoc test using Bonferroni. The manipulation for the perceived heuristic cue was checked with a one-way ANOVA. Participants in the higher number of “Likes” condition were significantly different from participants in the lower number of “Likes” condition, F(1,357) = 10.91, p < .001.

Hypothesis Testing

To test our three hypotheses, a series of repeated measures ANOVAs were conducted. Hypothesis 1 stated that level of social presence in sponsored posts would differently influence (a) the intention to click “Like” and (b) the intention to purchase. There was a main effect of social presence on the intention to click “Like,” F(2, 714) = 7.75, p < .001, partial η² = .021, power = .95, and the intention to purchase, F(2, 714) = 13.47, p < .001, partial η² = .036, power = 1.00. Specifically, posts with high social presence (M = 3.32, SD = 0.06) generated a higher intention to click “Like” than the medium (M = 3.01, SD = 0.06) and the low social presence posts (M = 2.88, SD = 0.06). Similarly, posts with a high level of social presence generated the highest score for purchase intention (M = 3.17, SD = 0.06), followed by posts with a medium level of social presence (M = 3.13, SD = 0.06) and a low level of social presence (M = 3.03, SD = 0.06). Thus, Hypotheses 1a and 1b were supported. The results from the post hoc test is included in Table 2.

Hypothesis 2 stated that the source of the sponsorship would differently influence (a) the intention to click “Like” and (b) the intention to purchase. There was a main effect of source on the intention to click “Like,” F(1, 357) = 28.87, p < .01, partial η² = .075, power = .1.00, and the intention to purchase, F(1, 357) = 15.52, p < .001, partial η² = .042, power = .98. Sponsored posts generated from an official brand produced a higher intention to click “Like” (M = 3.25, SD = 0.08) and a higher intention to purchase the product (M = 3.33, SD = 0.07), as compared with posts from lay people’s intention to click “Like” (M = 2.63, SD = 0.09) and lay people’s intention to purchase the product (M = 2.89, SD = 0.08). As a result, Hypotheses 2a and 2b were supported. The results from the additional post hoc test is included in Table 2.

Hypothesis 3 stated that the number of “Likes” would differently influence (a) the intention to click “Like” and (b) the intention to purchase. There was a main effect of heuristic cues on the intention to click “Like,” F(1,357) = 8.81, p < .01, partial η² = .024, power = .84, supporting Hypothesis 3a. Specifically, sponsored posts with high heuristic cues (i.e., a higher number of “Likes”) generated a higher intention to click “Like” (M = 3.11, SD = .08) than posts with low heuristic cues (M = 2.77, SD = 0.08).Yet, there was no main effect on purchase intention, F(1, 357) = 2.995, p = .08. The results from the additional post hoc test is included in Table 2.

Research Questions 1 and 2 asked if there is any interaction between social presence and heuristic cues on the dependent variables. A repeated measures of ANOVA test revealed that there was an interaction between social presence and heuristics cues, F(1,357) = 6.460, p < .001, partial η² = .018, power = .91, on the intention to click “Like.” The results show that participants who viewed posts with high heuristic cues were not swayed by social presence: high (M = 3.11, SD = 0.08), medium (M = 3.11, SD = 0.08), and low (M = 3.10, SD = 0.08). On the contrary, for participants exposed to low heuristic cues, Instagram posts that had a high level of social presence generated a significantly higher intention to click “Like” ( M = 2.91, SD = 0.09), as compared with the medium (M = 2.75, SD = 0.08) and low social presence posts (M = 2.67, SD = 0.09).

Another repeated measures of ANOVA showed that there was a significant three way interaction among social presence, source of sponsorship and heuristics cues, F(2,357) = 4.124, p < .05, partial η² = .011, power = .73, on the intention to purchase (Research Question 2). The results showed that participants who viewed low social presence posts were more susceptible by the number of “Likes” (M_difference = 0.20) for official or nonofficial posts (M_difference = 0.20) compared with posts with high or medium social presence. This indicates that low social presence posts are more influenced by the number of “Likes” and the source of sponsorship of the posts. High social presence posts generated a bigger difference (M_difference = 0.26) between a high number of “Likes” and a low number of “Likes” when the source of sponsorship was nonofficial. The difference was smaller (M_difference = 0.11) when the source was official.

Discussion

Human-computer interaction is constantly happening in connection with others (Walther & Parks, 2002). While such connections simply refer to strong emotional bonds among close friends and family, they may also be manifested by the presence of others on social media. Despite this topic’s clear contribution, limited research has investigated the impact of social presence on user engagement in the context of social media (S. V. Jin Jin, Muqaddam, & Ryu, 2019; Kahlow, Coker, & Richards, 2020). This research examines how to effectively deliver sponsored contents through social presence, heuristic cues, and source on social media marketing. It sheds light on social presence using images of people as a multidimensional perception in the mind of the participant. In this regard, the most important finding is that our results provide empirical evidence that social presence is positively related to social media engagement. Not only was the operationalization of the level of social presence in our experiment successful, but it could be also applied to future practice.

Use of human faces in content could accelerate content engagement with social media users, as compared with images with only products or parts of the human body. This is in line with previous literature showing that social presence, or the sensation of “being there” in mediated communication (Short et al., 1976), leads to engagement with content with high social presence (T. Kim & Biocca, 1997; Richardson & Swan, 2003). For example, a higher sense of presence in a virtual store can lead to audiences being more confident in regard to their attitudes toward the product or brand (T. Kim & Biocca, 1997). Using social presence on Twitter can improve learning (Dunlap & Lowenthal, 2009; Munoz, Pellegrini-Lafont, & Cramer, 2014) and the feeling of connection to celebrities (J. Kim & Song, 2016) because it induces the feeling of coexistence through constant interactions (Dunlap & Lowenthal, 2009).

Our article contributes theoretically to the literature by viewing social presence as a dimensional concept, using three subdimensions (T. Kim & Biocca, 1997; Mallmann & Maçada, 2018) of access, shared environment, and proximity (Biocca & Harms, 2002; Biocca et al., 2003; Mennecke et al., 2011; Nowak & Biocca, 2003). Our findings indicate that increased human presence leads to a feeling of social presence. A participant seeing more of a human presence on Instagram is likely to feel greater perceived access, shared environment, and proximity. Future studies should consider the operationalization of social presence via human presence in images and explore the notion that social presence is multidimensional. The current article is a unique contribution that shows that operationalization of social presence should be employed by strategic communication practitioners, including companies and organizations to use social media as a persuasive strategy. Unlike previous studies comparing sponsored versus nonsponsored posts, we only tested “sponsored” posts to specifically look at how message content on Instagram best suits message processing and engagement behaviors. Our social presence findings provide practical value because we have shown that having human “faces” on Instagram posts generates desirable outcomes. In other words, we recommend not to use “product only” pictures in sponsored posts if practitioners want to receive more “Likes” and engagement from users.

Source of sponsorship is also a significant predictor for social media engagement. Official brand posts were more favorable, perhaps because people trust the information from the official source more; this may be consistent with previous literature that persuasion knowledge leads to more positive outcomes (i.e., disclosing sponsorship of an ad on social media positively influences audience memory, brand attitude, and engagement; Evans et al., 2017). However, there have been inconsistencies in regard to the fundamental effects of official source. Some scholars argue that familiar people as sources may be perceived as more familiar, more credible, and more friendly and engaging. According to Metzger et al. (2010), people prefer to consult testimonials and lay opinions in regard to decision making as a motivated cognitive shortcut. On the other hand, Hu and Sundar (2010) found that participants preferred health information from an official website, compared with the information from a personal website or a personal blog. However, we must also consider that the way we manipulated source as a random stranger versus the brand might have led to increased favorability even more.

It could be that when the platform is online, people may not trust the content unless it is from a reliable source; for example, people trust online reviews more if the reviewer discloses more identifying information (Xie et al., 2011). In this regard, people are more likely to engage with an official branded source, rather than one that is interpersonal and perhaps unfamiliar. In future studies, it would be better to use interpersonal sources that may be more popular or introduced as popular and identified to the participants (such as celebrities or popular influencers). In this study, Instagram users seem to prefer the official brand’s content rather than user generated sponsored content.

For the main effect for heuristic cues, it appears that higher “Likes” as a bandwagon heuristic was effective for engagement. Literature on the effects of heuristic cues in current social media found similar outcomes, such as reporting higher credibility toward online comments with high numbers of “Likes” (Hong & Cameron, 2018) and the success of bandwagon cues on attitude and behavior (H. S. Kim & Sundar, 2014). Seeing that a post has a high number of “Likes” can also induce perceptions that others are engaging and interacting with the content (J. W. Kim, 2018).

The interactive effect between social presence and heuristic cues provides fresh insights for applying our findings on a practical level. Seeing posts with high heuristic cues (“Likes”) and social presence did not make a significant difference on participants’ intentions to click “Like.” On the other hand, exposure to high social presence was more effective than exposure to posts with medium and low levels of social presence (with a lower number of “Likes”). Based on this finding, we recommend that the strategic use of social presence should be applied to brands that may not have a meaningful number of Instagram followers; lacking many followers might only generate a relatively low number of “Likes.”

Limitations and Future Research

The current experiment is not free from limitations. First, even though the Qualtrics panel team had high control over the data quality, we are careful to generalize our findings to the population since it only focused on Instagram users. Second, our manipulation check probes may have triggered additional questions among the participants and may have even acted as heuristic cues pointing to what participants should be looking for in the stimuli. For instance, during the experiment, participants were asked how many people liked the posts to check the manipulation of high and low number of “Likes.” Since they were asked to view multiple posts, after they saw the first post, it is possible that they may have explicitly looked for the number of “Likes” in other stimuli that followed. This may have led to unnecessary attention toward the manipulation and lowered external validity. Future research should test if there is any systematic differences between stimuli photos to strengthen claims around differences in popularity or source cues. Additionally, it is rumored that Instagram may soon remove likes as a feature, which makes this study lacking in its future application to Instagram; however, the findings can still be applied to other platforms.

This research also did not consider product category. Product category could be added as a factor to consider in future research, since Instagram users might be more engaged or involved with certain low involvement or mass marketed products at baseline. For example, beer and coffee are more low involvement, perhaps allowing for instant gratification and triggering ease of liking and purchase intention. Buying a home or car may be considered more high involvement and some people may not be in a financial position to express purchase intention or liking if they are not oriented toward the purchase; these types of products are less so mass marketed and only engage certain types of people (Dessart, 2017) in mediated content like marketing messages (Schultz, 2017).

Conclusion

Practically speaking, this study has several strengths. First, it generates external validity since we employed real, existing brands, Starbucks and Budweiser, in an experimental study, making the study theoretically generalizable, but also practical. Second, our sample was also motivated to process the content, since we used only valid Instagram users in our sample. Additionally, we attained high statistical power and effect sizes in our results, giving us further confidence in the support for our hypotheses. With that, we can provide empirical suggestions for communication practitioners who aim to target Instagram users, by employing social presence and using branded companies or organizational content. This study is particularly innovative in business communication because it uniquely tested social presence and bandwagon heuristics within visual content from a business or organization on a real and popular social media platform for organizations to wield positive business outcomes. Bandwagon heuristics in the form of a high number of “Likes” can also positively affect persuasive outcomes for a company or organization, since higher “Likes” as a bandwagon heuristic was effective for engagement. Advertising dollars might be well spent on promoting branded posts by carefully crafting an engagement and planning strategy for use on Instagram, involving the manipulation of cues and social presence. In regard to research contributions, researchers should use interpersonal sources that may be more popular or introduced as popular and identified to the participants (such as celebrities or popular influencers) to further test what kinds of sources should be used to best engage audiences. Researchers should also consider other heuristics other than “Likes” in real Instagram pages in the future as the platform may change over time (e.g., comments on a post, number of comments, etc. could be considered).

Supplemental Material

IJBC_Appendix_Jan_30 – Supplemental material for Instagramming Social Presence: A Test of Social Presence Theory and Heuristic Cues on Instagram Sponsored Posts

Supplemental material, IJBC_Appendix_Jan_30 for Instagramming Social Presence: A Test of Social Presence Theory and Heuristic Cues on Instagram Sponsored Posts by Erika Katherine Johnson and Seoyeon Celine Hong in International Journal of Business Communication

Footnotes

Authors’ Note

This article is original and is not under consideration or published elsewhere.

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) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Erika Katherine Johnson

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Erika Katherine Johnson studies influence and persuasion in health contexts, media effects, health communication with and among women, information processing, strategic communication, and entertainment and social media source. Her work is published in Communication Research, Howard Journal of Communications, Mass Communication and Society, and Newspaper Research Journal, among other mass communication- and health-related journals.

Seoyeon Celine Hong’s research deals with various PR and mass communication topics, including crisis communication, social media, and theory. She has published in Public Relations Review, Journal of Public Relations Research, Journalism & Mass Communication Quarterly, Howard Journal of Communications, and Journal of Contingencies and Crisis Management, among other persuasion and mass communication journals.

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Supplementary Material

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Instagramming Social Presence: A Test of Social Presence Theory and Heuristic Cues on Instagram Sponsored Posts

Abstract

Keywords

Sponsored Posts on Social Media

Social Presence Theory

Source Effect

Bandwagon Effects of Numeric Cues

Method

Study Design

Sample

Procedure

Stimuli

Measurements

Independent Variables

Social presence

Heuristic cue

Dependent Variables

Intention to click “Like.”

Intention to Purchase

Results

Manipulation Checks

Hypothesis Testing

Discussion

Limitations and Future Research

Conclusion

Supplemental Material

IJBC_Appendix_Jan_30 – Supplemental material for Instagramming Social Presence: A Test of Social Presence Theory and Heuristic Cues on Instagram Sponsored Posts

Footnotes

Authors’ Note

Declaration of Conflicting Interests

Funding

ORCID iD

Supplemental Material

Author Biographies

References

Supplementary Material