Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=rjmc20 Journal of Marketing Communications ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/rjmc20 Digital content marketing and consumer brand engagement on social media- do influencers’ brand content moderate the relationship? Raphael Odoom To cite this article: Raphael Odoom (29 Aug 2023): Digital content marketing and consumer brand engagement on social media- do influencers’ brand content moderate the relationship?, Journal of Marketing Communications, DOI: 10.1080/13527266.2023.2249013 To link to this article: https://doi.org/10.1080/13527266.2023.2249013 Published online: 29 Aug 2023. 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Raphael Odooma,b aDepartment of Marketing and Entrepreneurship, University of Ghana Business School, Legon, Accra, Ghana; bDepartment of Marketing Management, University of Johannesburg, Johannesburg, South Africa ABSTRACT Digital content marketing (DCM) has been found to be effective in engaging consumers with brands, particularly on social media. Complementing DCM pursuits with social media influencers’ (SMIs) brand content can further enhance the ameliorating results. This study aimed to empirically assess DCM’s relationship with consumer brand engagement on social media and how SMI’s brand content moderates this relationship. Through a quantitative approach, the empirical data was purposively collected from 1022 respondents via a web-based survey questionnaire. The findings indicate that DCM campaigns with an information, entertainment, commercial, and emotional focus have positive relationships with consumer brand engagement, except when DCM campaigns have negative emotional elements. Moreover, the brand content of SMIs significantly moderates the relationship between DCM elements and brand engagement. However, DCM campaigns with negative emotional elements have a negative relationship on brand engage- ment when SMI is a moderator. This study is significant as it empiri- cally tests the interplay among DCM, SMIs, and brand engagement on social media, contributing to the literature on this topic. ARTICLE HISTORY Received 1 February 2023 Accepted 13 August 2023 KEYWORDS Digital content marketing; influencer marketing; brand engagement; social media influencers; DCM; SMIs Introduction This study seeks to investigate the role of social media influencers (SMIs) in digital content marketing (DCM) and their impact on consumer behaviour within a social media setting. DCM has become an increasingly important tool for brands looking to engage customers in a persuasive manner (Devereux, Grimmer, and Grimmer 2020; Mathew and Soliman 2021). It has become an essential tool for marketers aiming to break through the clutter in the digital space and enhance favourable reactions from consumers. DCM has been around for decades, enveloped in advertising (Beard, Petrotta, and Dischner 2021; Koiso- Kanttila 2004), but the explosive growth of the Internet and social media platforms like Facebook, TikTok, Twitter, and Instagram have transformed the way it is executed and consumed. DCM has proven to be effective in driving consumer engagement with brands (Hollebeek and Macky 2019), but the rise of SMIs has added a new dimension to this CONTACT Raphael Odoom rodoom@ug.edu.gh Department of Marketing and Entrepreneurship, University of Ghana Business School, Legon, Accra, Ghana JOURNAL OF MARKETING COMMUNICATIONS https://doi.org/10.1080/13527266.2023.2249013 © 2023 Informa UK Limited, trading as Taylor & Francis Group http://www.tandfonline.com https://crossmark.crossref.org/dialog/?doi=10.1080/13527266.2023.2249013&domain=pdf&date_stamp=2023-08-25 phenomenon (Khan 2022). SMIs can now impact nearly every aspect of a consumer’s decision-making process, particularly in their interactions with brands on social media platforms. Theoretically, the Uses and Gratifications Theory (UGT) offers scholarly relevance as a media consumption paradigm by explaining the many and varied reasons why con- sumers use social media (Whiting and Williams 2013). From the UGT, consumers actively select media to satisfy specific needs rather than to passively receive mass media (Katz, Blumler, and Gurevitch 1973). Therefore, for consumption of digital content, consumers scrutinise and selectively decide what they engage with based on functional, hedonic, and authentic motivations (Plume and Slade 2018). Within the domain of social media, too, the source of the content plays a critical role in affecting the levels of consumers’ brand engagement. Such sources have been identified to include brand users, social media influencers or the brand itself, typically executed in the form of DCM (Giakoumaki and Krepapa 2020). Over the past few years, the literature has witnessed a growth in research regarding influencers’ role in affecting marketing (Abhishek and Srivastava 2021). On social media, for instance, these SMIs are serving as a route to brand engagement for their followers, given that they engage consumers and hold the potential to promote customer-brand relationships across different product categories (Delbaere, Michael, and Phillips 2021). Nevertheless, while some scholars assert that many important questions about the effectiveness of SMIs still remain (Ki and Kim 2019; Torres, Augusto, and Matos 2019), other studies also seem to point out an insufficiency of research on digital engagement practices from consumers’ perspectives (Beard, Petrotta, and Dischner 2021; Eigenraam et al. 2018). Indeed, the DCM literature is dominated by conceptualisations and proposi- tions, as well as firm-focused viewpoints (Beard, Petrotta, and Dischner 2021; Christodoulides, Michaelidou, and Siamagka 2019; Hollebeek and Macky 2019; Rowley 2008), with limited corroborations from the perspective of the consumer who is at the receiving end of these DCM pursuits. Generally, complementary theories also explain how different elements can interact and create more value than if they were used in isolation. This research, operationally, draws on the business management standpoint of Ennen and Richter (2010) as well as Hakala (2011) to suggest that the presence of SMIs may produce a moderating effect on the relationship between brands’ DCM campaigns and consumer engagement. More specifically, the complementarity standpoint in this study context proposes that an enhanced engagement can be realised among consumers when brands’ DCM efforts are complemented with marketing content from SMIs (Lee and Theokary 2021; Zhou et al. 2021). From the influence framework (Scheer and Stern 1992), the literature notes that certain favourable traits and contents of SMIs, which allow them to amass and influence millions of followers (Ki et al. 2020), may also play advantageous roles during SMIs’ brand advocacy. Indeed, Hollebeek and Macky (2019) also suggest that brand engagement remains DCM’s first-tier, intra-interaction consequence, while the commonly used success measurement metric for influencer brand advocacy is also brand engagement. Remarkably, partnerships with SMIs are becoming increasingly important stimuli in brands’ marketing and promotion strategies (Ki and Kim 2019; Zhou et al. 2021), calling for a holistic exploration of its nexus with DCM and consumer engagement. This is presently missing in the literature. The study therefore examines the influence of brands’ DCM on 2 R. ODOOM consumer engagement in the social media landscape. It as well investigates this relation- ship within the moderating boundaries of SMIs’ digital content. Thus, this study in its uniqueness seeks to answer two questions; (1) to what extent does DCM influence brand engagement from consumers on social media, and (2) in what trajectory do SMIs’ brand content affect the relationship between DCM efforts from brands, and consumers’ brand engagement. This study addresses the research questions and offers theoretical and practical impli- cations. Notably, to the best of our knowledge, the empirical exploration of the interplay between DCM, consumer brand engagement, and the moderating role of SMIs remains limited in the existing literature. In addition to advancing scholarly developments in DCM and SMIs research (Beard, Petrotta, and Dischner 2021; Borchers and Enke 2021), this study provides an empirical examination that holds significance for marketing practi- tioners. It highlights the potential of leveraging SMIs’ content to enhance brand engage- ment in DCM endeavours. Moreover, the study offers valuable insights for practitioners aiming to co-create effective digital marketing strategies with SMIs, fostering mutually beneficial relationships between consumers and endorsed brands. The subsequent sec- tions delve into the related literature, hypotheses development, methodology, data analyses, discussions, and research implications. Related literature DCM and consumer brand engagement on social media Content marketing is a strategic approach to marketing that focuses on creating and disseminating valuable, relevant, and consistent content to attract and retain a clearly defined audience, ultimately driving profitable customer action (Content Marketing Institute 2020). Since the famous declaration of ‘Content is king’, the digital marketing industry has been striving to prioritize what consumers truly desire from brands – mean- ingful conversations (Müller and Christandl 2019). The term ‘content’, which has gained prominence in the past decade, encompasses a wide array of information, resources, and materials that are purposefully communicated to a targeted audience. These include text, photographs, graphics, infographics, PDFs, stories, emails, blog articles, news, social media posts, viral content, reviews, videos, audio, e-books, apps, and various combina- tions thereof (Rattan 2019). Given its intersection of communication, marketing, journal- ism, and consumer behaviour, Wall and Spinuzzi (2018) consider content marketing an evolving field with fluid boundaries. Hollebeek and Macky (2019) conceptualise DCM as the creation and dissemination of relevant, valuable brand-related content to current or prospective customers on digital platforms. Indeed, DCM has become a growing and relevant vehicle for fostering and developing favourable brand engagement, trust, loyalty, consumer awareness, and sales lead conversion (Ashley and Tuten 2015; Wang et al. 2019). Contrary to advertising, which is designed to directly persuade consumers to purchase focal offerings in the short run, DCM’s focus tends to be an art that adds value to the lives of consumers by educating them about the brand without overtly and directly selling to customers (Wall and Spinuzzi 2018). Consumers mostly forward such digital content to friends, share YouTube video links to their relatives, and send restaurant reviews to their neighbours. Lately, educative, JOURNAL OF MARKETING COMMUNICATIONS 3 and interesting videos, as well as ‘trends’ are shared among friends and followers on Facebook, Twitter, Instagram, TikTok, Twitch, and during Clubhouse discussions. Engagement describes a consumer’s psychological and behavioural predisposition to interact with brands (Tafesse 2016). More specifically, the behaviours transcend transac- tions to become manifestations that have a brand or firm focus beyond purchase, resulting from motivational drivers (Van Doorn et al. 2010). These manifestations are also related to many cognate themes such as consumer product involvement, relation- ship marketing, brand loyalty, marketing orientation, and brand networks and commu- nities (Devereux, Grimmer, and Grimmer 2020). Meanwhile, engagement is behaviour‐ based and extends beyond brand purchase (Giakoumaki and Krepapa 2020). Within the social media context, consumer brand engagement has been described as a customer’s interaction with a business’ post on social media (Oh et al. 2017). Such interactions are expressed in the form of likes, clicks, shares, retweets/reposts, comments (Devereux, Grimmer, and Grimmer 2020; Stieglitz and Dang-Xuan 2013), and subsequent behaviours emanating from the calls to action within the posts/content. On social media, consumers’ gratifications may include information seeking, entertain- ment, social interaction, self-expression, and impression management (Bu, Parkinson, and Thaichon 2021; Gao and Feng 2016). Thus, consumers may engage digital brand contents on social media to learn about news and events, obtain recommendations, develop social relationships, and promote self-statuses via identity creation, share personal experiences, escape from problems, or negative feelings, and even seek health advice and online diagnoses. Due to its wider reach and accessibility, social media has assumed a dominant position over traditional marketing channels (Devereux, Grimmer, and Grimmer 2020). It comes as no surprise that the most frequently used content types by B2C marketers recently are related to social media (Beard, Petrotta, and Dischner 2021). In DCM strategy, the role of social media is vital (John et al. 2017), as they become a key catalyst to reach target consumers in a less obtrusive way than traditional media, often via sponsored content (Plume and Slade 2018). Normally, these sponsored contents contain promotional messages that are made to look like the content posted by other users from a person’s network of friends. Indeed, such content depict great semblance in format and style, and are embedded in a user’s newsfeed alongside regular posts from befriended contacts (Boerman, Willemsen, and Van Der Aa 2017). The overarching goal here is to move the consumer from social to sale (Kumar et al. 2016) through social commerce. Some key advantages of this approach include enabling companies to create, co-create, share, and discuss user-generated content, as well as augmenting their visibility and engagement on a global scale (Dolega, Rowe, and Branagan 2021). Number of followers gained, reactions to posts, and likelihood to click through, are typical superficial measures of DCM success, particularly regarding consumer brand engagement. Conceptual framework and hypotheses From the UGT, information-seeking and entertainment are identified among the gratifica- tions behind media usage (Luo, Chea, and Chen 2011). In DCM, these gratifications are often further blended with commercialised content and emotionally captivating elements as means to garner brand engagement from consumers (Tellis et al. 2019). This study proposes a conceptual framework (see, Figure 1) covering DCM, consumer brand 4 R. ODOOM engagement, and SMIs’ brand content. The DCM elements provide the underpinning features that influence consumer brand engagements on social media, while the SMIs’ brand content explicates the contribution of social media influencers’ narrative strategies (Zhou et al. 2021) by means of their posts. Information focus The unique characteristics of the Internet, such as interactivity, telepresence, hypermedia, and network navigation, provide opportunities for brands to deliver information-focused digital content as part of their social media marketing activities (Khan 2022). Information- focused content typically aims to provide non-commercial details about a brand, includ- ing argumentative or factual descriptions of its features, attributes, and uses. This type of content serves to address FAQs and offer solutions to consumer problems related to the brand. Consumers expect digital content from brands to be interesting, useful, and provide gratification through new knowledge or ideas that aid in their decision-making process (Plume and Slade 2018). Various formats, such as e-newsletters, podcasts, social media posts, and videos, are commonly employed to deliver information-focused content. It is important to note that the reception of information-focused content can vary depending on the familiarity and popularity of the brand. For well-known brands, such content may be perceived as dry or uninteresting, potentially leading consumers to ignore or even find it irritating, resulting in a lack of further sharing (Tellis et al. 2019). However, for unknown or unfamiliar brands, information-focused content can be valuable in providing relevant and important information to educate users and attract consumers. On social media platforms, consumers may engage with information-focused content if they per- ceive the brand to be unique and find the content personally interesting, prompting them to bookmark it on their personal pages (Chung and Han 2017). Consequently, consumers not only consume brand-generated informative content but also share it with their DCM Elements Information focus Entertainment focus Emotion focus Commercial focus Consumer Engagement with brand content SMIs’ brand content Figure 1. Conceptual model. JOURNAL OF MARKETING COMMUNICATIONS 5 connections, stimulating interactions among group members and fostering engagement (Giakoumaki and Krepapa 2020). For this reason, we hypothesise that: H1: Digital content with information focus is positively related to consumer brand engagement on social media. Entertainment focus According to Bu et al. (2021), entertainment features are considered crucial aspects of brand content on social media, as highlighted in various exploratory content analyses. From a hedonic gratification perspective, consumers of digital content often seek enter- tainment value, allowing them to derive pleasure, escape from everyday life, and engage in enjoyable activities (Plume and Slade 2018). Digital content with an entertainment focus typically takes the form of short videos, incorporating amusing and captivating material. By integrating humour and entertaining concepts, such content effectively captures and sustains consumers’ interest, leading to enhanced brand recall (Sprott, Czellar, and Spangenberg 2009) and increased brand interaction on social media plat- forms (Giakoumaki and Krepapa 2020). Furthermore, Ashley and Tuten (2015) emphasize that incorporating entertainment into brand content is instrumental in fulfilling consu- mers’ desires for escapism, hedonism, and aesthetic enjoyment. Branded entertainment serves as a creative strategy employed by brands to foster engaging consumer experi- ences, particularly within social networks. Moreover, even though the UGT suggests that social media participants are likely to desire both entertainment and informativeness, the work of Luo (2002) hints that enter- tainment is a stronger motivator of engagement with top brands than informativeness. The literature also provides several evidence that most brands are employing entertain- ment-focused content on social media (Bu, Parkinson, and Thaichon 2021; Gavilanes, Flatten, and Brettel 2018; Zhang, Sung, and Lee 2010). A key observation from these studies points to the fact that branded entertainment offers continuous sensory immer- sion, building a connection between the entertaining content and the audience to provide affirmation in driving engagement behaviour (Plume and Slade 2018). Other empirical works further demonstrate that an entertainment-focused content helps garner peer endorsement and normative social impact (Bu, Parkinson, and Thaichon 2021). These reasons suggest the hypothesis that: H2: Digital content with entertainment focus is positively related to consumer brand engagement on social media. Emotion focus Emotions are one of the key drivers that underpin consumers’ inclination to interact and engage with brands. Brands are known to incorporate not just cognitive but also 6 R. ODOOM emotional approaches in their digital marketing pursuits (Giakoumaki and Krepapa 2020). More specifically, DCM focuses on communicating with target consumers to strengthen the brand’s emotional connection with customers, rather than triggering sales (Ashley and Tuten 2015). As a result, most digital content strategies that seek to build long-term relationships with consumers and maintain customer followings are saturated with emo- tional-based element. In operationalising emotion focused ads, for instance, Tellis et al. (2019) suggest that such ads may have dramatic plots that elicit feelings of joy, love, inspiration, amusement, excitement, and warmth which can, in turn, lead to sharing when viewers find them emotionally evocative. Nonetheless, there could also be arousals of negative feelings such as sadness, shame, anger, and fear, which may also prohibit further engagements. Even in entertaining content, emotional connections are triggered during consumer engagements. In fact, Gavilanes et al. (2018) note that despite requiring little time or effort, clicking the ‘like’ button after engaging with a brand’s content represents more emotional investment from the consumers’ side than a simple click on a company’s post link does. Clicking the ‘like’ button is a form of rating the branded content positively and is a key metric of brand engagement on social media. If the storyline of the digital content arouses positive emotions for consumers, a higher possibility of further shares, reposts, comments, and action-taking exist (Stieglitz and Dang-Xuan 2013). The reverse is also a possibility in in avoiding further engagement in the event of negative emotions arousals. Considering this logic, it is expected that: H3a: Digital content with positive emotion focus is positively related to consumer brand engagement on social media. H3b: Digital content with negative emotion focus is negatively related to consumer brand engagement on social media. Commercial focus Undoubtedly, the ultimate motive of DCM for most brands is to derive commercial gains, given that content marketing generates more leads than outbound marketing at a lower cost (Content Marketing Institute 2020). For this reason, the commercial focus within DCM aims to trigger direct purchases of the brand (Tellis et al. 2019). While other focal elements of DCM may directly gratify consumers, the commercial focus is embedded with explicit or subtle commercial features, such as pricing details, promotional deals, persuasive arguments for choosing the brand, and information about locations for purchase or patronage. Although commercial content may raise concerns about potential consumer resistance to persuasion messages (Friestad and Wright 1994), it serves a crucial purpose. In our study, we operationalize commercial digital content as information that conspicuously or subtly provides commercial features to consumers. Indeed, some consumers may coun- terargue such persuasive elements, potentially creating resistance to the intended per- suasion. However, it is important to consider that other consumers may find these JOURNAL OF MARKETING COMMUNICATIONS 7 commercial elements useful, especially if they are encountering the brand for the first time or have future intentions of patronage. It is essential to strike a balance between persuasive elements and informative value to ensure engaged consumers are not deterred from taking subsequent actions. Furthermore, even when digital content is channelled through third parties, such as influencers, it is advised to integrate commercial information into their messages to minimize followers’ suspicion of authenticity issues (Zhou et al. 2021). By including commercial features in digital content, brands can establish transparency, enhance consumer knowledge, and facilitate future patronage. Additionally, digital content containing commercial information may be saved, bookmarked, or shared with fol- lowers, friends, or connections on social media for various self-serving or altruistic reasons. Therefore, while the commercial focus within DCM may raise concerns about potential consumer resistance, it plays a significant role in driving direct purchases and informing consumers about commercial aspects of the brand. This prompts us to reason that: H4: Digital content with commercial focus is positively related to consumer brand engagement on social media. Social media influencers’ brand content Campbell and Farrell (2020) describe an influencer as someone who posts on social media in exchange for commercial compensation. Currently, the influencer economy is a wild frontier of Internet Marketing owing to the exponential growth of expenditure on influencer marketing in recent times (Lou and Yuan 2019). Despite the rise of new forms of social influence (Appel et al. 2020), the use of admirable personalities indirectly via a cascade of influence is not a new concept. Brands in earlier years have collaborated with well-known personalities (with a high social value) and celebrities to promote their products and services (Knoll and Matthes 2017). Over time, however, consumers have become wary of brand messages emanating from these sources, due to the perceptions that they are just being paid to endorse brands whose ideals they may not even uphold. In the digital dispensation now, consumers are gravitating towards forming virtual connec- tions with SMIs who affect the digital consumption journey with their narrative strategies (Zhou et al. 2021). Within the conceptual premise of this study, DCM and SMI brand content are inter- connected, with the latter being a crucial aspect of a firm’s overall DCM strategy. Yet, the unique characteristics, influence and impact that SMI brand content holds within the broader context of DCM requires the explication and treatment of the two as separate entities in this study. In demonstrating their interconnectedness while also highlighting their distinct aspects that warrant separate consideration, a number of reasons are provided. As far as their interconnectedness are concerned, SMIs create and share branded content on social media platforms, leveraging their personal brand identities and influence to engage with their audience, as well as help promote brands (Appel et al. 2020). This collaboration between brands and SMIs forms a crucial part of the DCM 8 R. ODOOM ecosystem, as the SMIs’ content and reach play a significant role in enhancing brand visibility, credibility, and consumer engagement. Although interconnected, SMI brand content normally boasts of distinct features that warrant the separate consideration within the broader context of DCM. These distinct aspects include the unique personal brand identities of SMIs, their individual content creation styles, and their ability to cultivate authentic connections with their audience (Lee and Theokary 2021). SMI brand content often carries a personal touch, storytelling elements, and relatability, which may differ from other facets of digital content employed in DCM strategies by brands. Additionally, SMIs’ influence and impact on consumer behaviour, trust-building, and brand perceptions often require specific attention and analysis to understand their contribution to consumer brand engagement in the digital landscape (Hollebeek and Macky 2019). By separately considering SMI brand content as a moderator, the study explores and evaluate the specific role and contribution of SMIs in driving consumer engagement and brand outcomes. This differentiation allows for a more focused analysis of the moderating role and influence of SMIs in the broader context of DCM strategies. Indeed, the Parasocial Interaction theory – the illusory feeling of friendship with a media personality – offers an explanation to the close relationships that develop between SMIs and their audiences (Rihl and Wegener 2019). These audiences are some- how drawn by a form of charismatic authority towards the SMIs that keeps them fixated on regularly consuming the influencers’ shared experiences and opinions (Cocker and Cronin 2017). Generally, these SMIs, by virtue of their specialised knowledge, authority, or insight into a specific subject, offer complementary services to companies. SMIs often have a globally genuine following across the world (Lee and Theokary 2021), and can reach hundreds of thousands or even millions of followers on social media platforms. They offer a unique form of electronic word-of-mouth by blogging or vlogging personal information (e.g., daily routines, major life events) and niche specialisations as part of their pitch when promoting products (AlRabiah et al. 2022). Research on complementarities suggest that alignments with complementary pro- cesses, capabilities, or strategic pursuits may offer possible augmented performance rents for firms (see, for instance, Ennen and Richter 2010; Hakala 2011). As far as their connection with consumers’ brand engagements are concerned, SMIs can augment DCM efforts of brands in several ways. For audiences who may not be regular followers of a particular brands’ social profiles, a single regular post or even a sponsored branded content from the SMIs may help in creating an initial product awareness to their audi- ences (AlRabiah et al. 2022). Since influencers are often noted for certain niches (such as fitness, beauty, lifestyle, tech, fashion, and eco-sustainability, among others (Jiménez- Castillo and Sánchez-Fernández 2019), they are most likely to provide product informa- tion directly or indirectly on the sponsoring brand. Such information could be explicit or subtle in SMIs’ content and may be useful in offering new knowledge and ideas that help in consumer decision making. Furthermore, the content and posts from SMIs may encompass elements that evoke both positive (excitement, inspiration, etc.) and negative (shame, fear, anxiety, etc.) emotions towards brands. Such emotions can arise due to the parasocial connected- ness between SMIs and their followers, as established in previous studies (Hu et al. 2020; Rihl and Wegener 2019). Consequently, the emotional expressions embedded in JOURNAL OF MARKETING COMMUNICATIONS 9 the posts and content of SMIs are often transmitted to their followers through emotional contagion (Lee and Theokary 2021). Additionally, SMIs frequently present campaigns that are interesting, entertaining, and enjoyable, prompting their followers to engage with the brands featured in their posts (Gavilanes, Flatten, and Brettel 2018). To achieve this, SMIs employ wit and humour in their content, aiming to attract and maintain their audience’s attention throughout content consumption. When it comes to commercialized content, SMIs may provide their followers with new (often discounted) pricing information, promotional coupon deals and codes, as well as indirect or direct word-of-mouth recommendations (Zhou et al. 2021), compelling their audience to act and engage with the brand. Fuelled by social media, collaborations with niche social media influencers (SMIs) often serve as complementary endeavours that are pursued alongside brand’s digital content marketing (DCM) efforts. According to the complementarity theory, when brands strike a complementary balance between their DCM pursuits and the contribution of SMIs’ brand content, they can achieve engagement outcomes that surpass the sum of their individual parts (Ennen and Richter 2010). In order to drive increased engagement with their content, SMIs’ commercial content on social media is expected to possess informa- tive, authentic, and honest qualities, visually appealing aesthetics, as well as demonstrate expertise and trustworthiness. Given that social media consumers often view SMIs as role models to emulate, with their preferences, examples, and behaviours seen as worthy of imitation (Ki and Kim 2019), it is anticipated that consumers’ inclination to mimic SMIs will influence their engagement with the brand content shared by these influencers. Additional evidence from Giakoumaki and Krepapa (2020) suggests that such a content source can play a moderating role between an individual’s self-concept and their engage- ment with brand content. Hence, we expect that: H5: SMIs’ brand content moderates the relationship between DCM and consumer brand engagement on social media. Methodology Questionnaire and measurement items The study employed a quantitative approach using questionnaire to statistically carry out the investigation on the empirical data. The measurement items were adopted from previous research and modified to fit our study’s context. The scale for SMIs’ brand content were adapted from Ki et al. (2020) and were measured by four items; social media brand engagement variable was from Devereux et al. (2020) and were measured by four items; the information focus, emotion focus, and commercial focus variables were from Tellis et al. (2019), and were measured by four items, seven items, and five items respectively; the entertainment focus variable was adapted from Bu et al. (2021) and was measured by four items. The specific items measuring all the variables can be seen in Table 2. Besides the demographic detail measures, the digital content items were measured on a five-point Likert scale. 10 R. ODOOM Data and sample Prior to questionnaire administration, we carried out an adequate assessment of the psychometric properties of the scale items using five academic faculty and seven experts from the field of digital marketing, who were not part of the main study. The face validity was intended to be high in the study to ensure that the measurement items were easily understandable and relevant to the study’s objectives. The pre-test phase ensured that the items in the questionnaire underwent rigorous assessment of face validity and content validity (Bagozzi and Yi, 1988). The pre-test phase ensured that we shared the items in the questionnaire with the experts and sought their input on the relevance, clarity, and appropriateness of the questions. Their feedback was incorporated into the refinement of the items, thereby enhancing the face validity of the scale. Furthermore, we ensured content validity by adapting the measures from previously validated scales in the literature. Data collection was carried out using a structured online questionnaire survey approach, which became necessary given that the data collection took place during the latter COVID-19 pandemic period when many of the participants were under restricted movement conditions. Participants were recruited purposively to target those who are familiar with digital content and activities of brand influencers on social media. Information about the study was shared on various social media plat- forms, such as WhatsApp, Instagram, Twitter, and Facebook. There were instances where notifications about the study was put across during Clubhouse discussions. The initial recruitment was based on broadcasting to the social media connections of the researchers to request their participation. Targeted participants were invited through direct messaging on social media platforms or via their emails and other forms of contacts provided on their profiles/bios. We followed up with sharing a link to the online survey questionnaire created in Google Forms, which was made avail- able to interested participants and made open for access within a period of four months. Respondents were also informed to pass on the link to potential social connections who fit the inclusion condition, showed interest, and understood the ramifications of the study. To enhance the quality of the data, the condition for inclusion was that participants must be active social media consumers with at least one social media account. Additionally, they must be aware of, and engage with at least one social media influencer of a brand the respondents relate to. After the four months period, we realised a total of 1022 global responses from the Google Forms link to the online survey questionnaire and were thus retrieved for data analyses. The data was analysed using IBM SPSS Statistics (Version 23) as well as AMOS (version 21) for Microsoft Windows. A summary statistic of the demographic profiles of the respondents is presented in Table 1. Common method bias To minimise the potential for common method bias, we relied on the procedural and statistical approaches. First, at the questionnaire design stage, the items were mixed up to prevent respondents’ ability to predict the relationships among the JOURNAL OF MARKETING COMMUNICATIONS 11 measures. Secondly, clarity on the scale items and reduction of ambiguity were done during the pre-testing of the survey instrument. Again, given that the model contained multiple interactive relationships implied a minimal likeliness of respon- dents’ ability to foresee the complex relationships to be tested in this research. By using anonymous questionnaires through an online survey via Google forms, the possibility of a socially desirable response is minimised. Statistically, a Harman’s single-factor test was conducted using exploratory factor analysis with all key variables, and no single factor was extracted or had high factor loadings for many items. Furthermore, the imposed single factor solution explained only 30.69% of the variance, which is below the suggested threshold of 50% (Podsakoff, MacKenzie, and Podsakoff 2012), suggesting that common method bias is an unlikely issue in the current study. Results and analyses Evaluation of measurement model After profiling the sampled respondents, the seven multi-item constructs were subjected to confirmatory factor analysis (CFA) to examine factor unidimensionality. For internal Table 1. Demographic details of respondents. Profile Measurements Frequency Percent Gender male 486 47.6 female 535 52.3 non-binary 1 0.1 Age up to 20 years 202 19.8 21–25 years 557 54.5 26–30 years 137 13.4 31–35 years 71 6.9 36 years and above 55 5.4 Highest educational qualification up to senior high 156 15.3 degree 726 71.0 professional 39 3.8 masters 90 8.8 PhD 11 1.1 Number of years active on social media below a year 5 .5 1–2 years 44 4.3 3–5 years 293 28.7 6–8 years 284 27.8 9 years and above 396 38.7 Average time spent on social media per day 10 mins or less 14 1.4 11–30 mins 59 5.8 31–59 mins 77 7.5 60–119 mins 207 20.3 2–3 hours 218 21.3 above 3 hours 447 43.7 Average number of social media followers less than 100 147 14.4 100–599 336 32.9 600–999 171 16.7 1000–1500 172 16.8 above 1500 196 19.2 Number of active social media platforms one 363 35.5 two 224 21.9 three or more 435 42.6 N = 1022 12 R. ODOOM consistency, the Cronbach’s α values for the seven constructs ranged between 0.808 and 0.893, with the corrected item-to-total correlations all exceeding the acceptable threshold of 0.50. These demonstrate that the measures in the model reflected their intended underlying constructs. The overall model fit indices (χ2/df = 1.98; GFI = 0.98; CFI = 0.97; TLI = 0.96; NFI = 0.98; RMSEA = 0.06) were all satisfactory and exceeded their recom- mended acceptable levels. In addition, all the standard factor loadings of the correspond- ing latent factors were statistically significant (i.e., t > 2.0, ρ < 0.01). The results of the CFA are all shown in Table 2. Table 2. CFA results (χ2/df = 1.98; GFI = 0.98; CFI = 0.97; TLI = 0.96; NFI = 0.98; RMSEA = 0.06). Construct items Standardised factor loadings t-value AVE Information focus There is usually an information in the content . . . . (α = .853; CR = .853) 40.811 .597 about something that interests me .794 that might be useful to others .883 to help in my decisions .836 that offered new knowledge and useful ideas Fixed Entertainment focus The content of the digital campaigns is normally . . . (α = .888; CR = .888) 54.833 .665 enjoyable .838 relaxing .848 interesting .808 entertaining Fixed Emotion focus (+ve) The digital contents I come across often elicit . . . . (α = .853; CR = .853) +45.394 .594 feelings of inspiration .822 feelings of warmth .805 feelings of amusement Fixed feelings of excitement .770 The digital contents I come across often elicit . . . . (α = .865; CR = .869) −63.396 .692 Emotion focus (−ve) feelings of fear Fixed shame .940 sadness .803 Commercial focus The contents of the digital campaigns usually provide . . . (α = .842; CR = .846) 36.285 .526 pricing information .690 new product/service information Fixed information on available locations of purchase/ patronage .788 promotion deals .809 arguments about why I should choose the brand .705 SMIs’ brand content The content posted by the influencer . . . (α = .893; CR = .893) 52.589 .679 be informative .844 have visually appealing aesthetics .909 demonstrate expertise and trustworthiness .827 be authentic and honest Fixed Engagement with brand content When I see brand contents of interest, . . . (α = .809; CR = .809) 39.238 .605 I click any links provided for more detail Fixed I share with family and friends .826 I repost to my social media connections .811 I take action (usually a purchase/patronage interest) .728 I make comments (compliments) on the advertised product/service .801 JOURNAL OF MARKETING COMMUNICATIONS 13 Next, we tested our instrument’s convergent and discriminant validity. The factor loadings from the CFA were between 0.690 and 0.940. Furthermore, the average variance extracted (AVE) for each construct ranged from 0.526 to 0.692, all greater than the recommended threshold value of 0.50. Also, the composite reliabilities of all the con- structs ranged from 0.846 to 0.893, exceeding the threshold of 0.70. In addition, discrimi- nant validity was established by comparing the shared AVE values between pairs of constructs with their squared Phi (φ) correlations. As Table 3 shows, the AVE values were larger than the corresponding shared variance (squared correlation coefficient) between all possible pairs of factors, and this confirmed discriminant validity. Moderated hierarchical regression tests of hypotheses To examine the theoretical relationships of this study, we chose to use a multi-stage moderated hierarchical regression analysis for several reasons. Firstly, this technique is well-suited for correcting endogeneity concerns, which can be especially relevant in situations where the predictor and outcome variables are measured on continuous scales (Hamilton and Nickerson 2003). This is important in our study since the variables of interest, such as DCM and consumer brand engagement, may be interrelated and mutually influential. Secondly, the multi-stage regression permits analysing the modera- tion effect of SMIs’ brand content in a more nuanced manner by including it as an additional predictor variable. This enables us to better understand the specific ways in which SMIs’ brand content may moderate the relationship between DCM and consumer brand engagement. Thirdly, the multi-stage regression method allowed us to create composite scores by averaging across the multi-item constructs. This reduces model complexity and improves the accuracy of our analysis (see, Sheng, Zhou, and Li 2011). In the first stage (see, Table 4), the demographic control variables were regressed on brand engagement to obtain their accountable residuals on the dependent variable. It was observed from the R2 value in Model 1 that, despite some of them exhibiting statistical significance, the demographic control variables collectively account for only 2.3 percent of the variance in brand engagement. In stage two, we added the indepen- dent variables – information focus, entertainment focus, emotion focus (positive and negative), and commercial focus – into the initial model to produce Model 2. These contributed to an improved 21.3 percent of the R2 variance in brand engagement. At this stage, the beta coefficients and their associated ρ values exhibited that four out of the five independent variables had significant and positive coefficients. In effect, the direct effects model (2) demonstrates statistically significant support for H1 (Information: β = 0.165, ρ < 0.001), H2 (Entertainment: β = 0.059, ρ < 0.05), H3a (Emotions(+ve): β = 0.205, ρ < 0.001), and H4 (Commercial: β = 0.164, ρ < 0.001). There was, however, no statistically significant support for H3b (Emotions(−ve): β= −0.045, ρ > 0.05). Similar trends were observed from the statistics in Model 3 (the introduction of the moderating variable) and Model 4 (the interactive effects) respectively. To test the moderating role of SMI’s content, we first tested its direct effect in stage 3 of the moderated hierarchical regression (Model 3). A statistically significant and positive relationship was observed between SMI’s content and brand engagement (β = 0.133, ρ ≤0.001), as well as a change of 11.3 percent in the R2 value, resulting in a 32.6 percent variance in the model. We then tested the interaction items for the moderating effects in 14 R. ODOOM Ta bl e 3. D es cr ip tiv e st at is tic s an d co rr el at io n m at rix fo r m od el v ar ia bl es . D es cr ip tiv es Co rr el at io ns Co ns tr uc ts M ea n SD 1 2 3 4 5 6 7 8 9 10 11 12 1. A ge – – 1 2. A ct iv e ye ar s – – .3 4* * 1 3. A ve ra ge t im e – – − .1 1* * .2 3* * 1 4. F ol lo w er c ou nt – – − .0 1 .2 9* * .2 2* * 1 5. Y ea rs o n SM – – .0 1 .1 5* * .0 8* .1 0* * 1 6. In fo rm at io n 3. 83 .8 2 .0 2 .0 3 .0 1 .0 2 .1 2* * 1 7. E nt er ta in m en t 3. 58 .8 5 − .0 0 .0 1 .0 5 .0 7* .0 7* .6 8* * 1 8. E m ot io n (+ ve ) 3. 47 .8 1 .0 1 − .0 1 .0 3 .0 3 .0 8* .6 4* * .7 7* * 1 9. E m ot io n (− ve ) 2. 44 1. 00 .0 2 − .0 3 .0 7* .0 3 − .0 1 .1 1* * .1 9* * .2 7* * 1 10 . C om m er ci al 3. 66 .7 9 .0 6* .0 1 − .0 3 .0 1 .0 8* * .6 0* * .5 4* * .5 4* * .1 6* * 1 11 . S M I’s c on te nt 4. 25 .8 4 − .0 2 .0 5 .0 8* * .0 5 .1 8* * .4 3* * .3 2* * .3 1* * − .0 2 .4 1* * 1 12 . E ng ag em en t 3. 23 .8 3 − .0 4 .0 2 .0 4 .1 1* * .1 0* * .3 8* * .3 3* * .3 9* * .1 6* * .3 6* * .3 0* * 1 N ot e: * Co rr el at io n is s ig ni fic an t at ρ < .0 5 ** Co rr el at io n is s ig ni fic an t at ρ < .0 1 SD = st an da rd d ev ia tio n. JOURNAL OF MARKETING COMMUNICATIONS 15 Model 4. With an R2 value of 52.4 percent of the variance in brand engagement, the moderation tests realised an additional 19.8 percent increment in the overall model. Also, all the interaction items exhibited statistical significance, with the beta values and their corresponding ρ values revealing that, indeed, a moderation from the interaction has occurred. These statistics provide support for the fifth hypothesis (H5). Notably, however, the interaction term ‘SMIC × Emotions(−ve)’ was negative. These statistical results imply that an additional stint of SMI’s content positively moderates the relationship between information focus, entertainment focus, emotions(+ve) focus and commercial focus, and brand engagement, while negatively moderating the relationship between emotions(−ve) focus, and brand engagement. Discussions Findings from our empirical analyses offer support and permit the conclusion that there is a relationship between DCM and brand engagement among consumers on social media. Specifically, our results reveal that DCM campaigns with information focus, entertainment focus, commercial focus, and emotions(+ve) focus, all have direct positive relationships with brand engagement on social media. These align with some prior works on DCM and consumer engagement and behaviour (see, Giakoumaki and Krepapa 2020; Mathew and Soliman 2021). In contrast, we find that negative emotions do not produce a significant influence on brand engagement. Besides not being statistically significant, the relationship here is as well inverse in nature. In other words, negative emotions in digital content can have a counterproductive effect on Table 4. Moderated hierarchical regression. Variables Model 1 Model 2 Model 3 Model 4 Controls Gender −.022 −.039 −.051* −.052 Age −.030 −.053 −.055 −.055 Education −.021 −.021 −.011 −.010 Active years on social media −.012 .002 −.002 .001 Average time spent .011 .007 −.002 −.003 Average follower count .106*** .097*** .095*** .094 Active social media platforms .085*** .040 .025 .025 Direct effects Information .165*** .129*** .127*** Entertainment .059** .071** .070** Emotion (+ve) .205*** .203*** .206*** Emotion (−ve) −.045 −.043 −.040 Commercial .164*** .131*** .133*** SMI brand content (SMIC) .133*** .132*** Moderating effects SMIC × Information .177*** SMIC × Entertainment .053** SMIC × Emotion (+ve) .091** SMIC × Emotion (−ve) −.046* SMIC × Commercial .210*** Fit indicators R2 .023 .213 .326 .524 Adj. R2 .016 .204 .316 .503 ΔR2 – .190*** .113** .198*** Observations (n) 1022 1022 1022 1022 Notes: DV = Brand engagement, ***ρ < .01, **ρ < .05, *ρ < .1All Tolerance above .1All VIF < 1.3. 16 R. ODOOM brand engagement (Stieglitz and Dang-Xuan 2013; Tellis et al. 2019). One possible explanation to the lack of effect could be that since social media are used for escapism by some consumers, negative emotions (often associated with undesirable experi- ences) may not align with this usage pattern. Hence, consumers may be less likely to engage (by avoiding further clicks, shares, retweets/reposts, or comments) with content that elicits such emotions. From the direct influential DCM features, the study’s results revealed that consumers’ inclinations towards brand engagement on social media was the greatest from digital content containing positive emotion elements (Tellis et al. 2019). As noted from the literature, engaging with a brand’s content represents a more emotional investment from the consumers. Hence, an arousal of positive emotions is a key source of responding to calls to action made in the content. This was closely followed by content with informative undertones (Chung and Han 2017). Arguably, consumers initial motivation to consume a brand’s digital content is likely to stem from the quest for either first-hand or second-hand information about the brand to aid in brand consumption or the estab- lishment of any form of relationship with the brand. Interestingly, Tellis et al. (2019) have averred that information-focused content can be dry and uninteresting to consumers who are already familiar with a brand, which may lead to lower engagement with the content. Yet, in our research, participants were instructed to engage with at least one social media influencer of a brand they relate to before responding to the questionnaire. This means that participants were not completely unfamiliar with the brand in question. Based on this, the finding from this research that information-focused content can be effective in engaging consumers, even when they are somewhat familiar with the brand, contradicts the earlier assertion from Tellis et al. (2019). Notwithstanding, we point out that conditions such as the participants’ level of familiarity with the brand and specific content they had in mind may have played a role in the different outcomes observed. From this relationship, the commercial enquiries may become the next logical gratifi- cation sought. This is where the quest for [discounted] pricing details, promotional deals, and accessibility to the brand (Gavilanes, Flatten, and Brettel 2018; Zhang, Sung, and Lee 2010) could become a key reason underlying consumers’ consumption of the brand’s digital content. Interestingly, entertainment elements were the least significant consid- eration among the DCM elements to affect brand engagement among consumers in this study. Perhaps, most of the digital content considered by the respondents came from brands that have fewer enjoyable and relaxing elements, but with more commercial elements. Besides, it is generally common for consumers to perceive and uphold the commercial intent of a brand’s digital content even if they find it entertaining. This, notwithstanding, the significant effect of the entertainment result further affirms the role played by branded content in offering consumers’ need for escapism, hedonism, as well as aesthetic enjoyment (Ashley and Tuten 2015). Additionally, we found, to a significant extent, that the presence of SMIs’ content also affects the relationship between the DCM elements and consumer brand engagement. With the exception of DCM campaigns that have negative emotional elements, indirect positive influences with consumer brand engagement are further realised when the other DCM elements were interacted with SMIs content. Such a finding also lends empirical corroborations with previous works in the literature (Giakoumaki and Krepapa 2020; Ki JOURNAL OF MARKETING COMMUNICATIONS 17 et al. 2020; Lou and Yuan 2019; Zhou et al. 2021). Notably, contrary to what happens with direct effects on brand engagement, when considering SMI, the negative relation between negative emotion focus and brand engagement, which is not significant in the direct effects, becomes significant during the moderation. Indeed, with the variance improvement in the moderation effects model, the complementarity between DCM and SMIs’ brand content is superior in brand engagement than their isolated instances. Inferably, such a result lend support to a conclusion that DCM efforts produce sub- stantial positive amplification effects on consumers’ brand engagements on social media when complemented with SMIs’ brand content, except when DCM campaigns have negative emotional elements. For this reason, the supposition holds that the amplification effect will also yield enhanced engagement rents (Hakala 2011) for the brands exploiting the use of SMIs in combination with their DCM pursuits. Notably, however, the amplifica- tion effect holds in a detrimental way when the focus is on negative emotions. The intriguing result here shows that there is a strong amplification effect when the emphasis is placed on negative emotions, but it has adverse consequences. This means that even a weak negative relationship becomes significant due to the potent amplification. This finding holds considerable appeal from both a theoretical and practical standpoint. Theoretical implications The current study makes several theoretical contributions to the field. Firstly, it addresses a research gap by empirically examining the impact of DCM on brand engagement among social media consumers. Existing literature stock in the DCM domain points to a predominant focus on firm-level perspectives (Beard, Petrotta, and Dischner 2021; Christodoulides, Michaelidou, and Siamagka 2019), leaving a lack of research from the end-user’s standpoint. This study fills this underexplored gap by providing an empirical test of digital content marketing elements and consumer brand engagement. Thus, it adds to the existing literature on DCM with a consumer-based perspective as well as contributes to the theoretical understanding of brand engagement. Secondly, the study extends the Uses and Gratifications Theory (UGT) into the bur- geoning literature on DCM and consumer brand engagement, particularly within the context of social media (Plume and Slade 2018). By drawing on the UGT and developing a conceptual framework and hypotheses (Katz, Blumler, and Gurevitch 1973), the study provides an empirical test of the gratifications derived from DCM by brands. The results shed light on the nuanced ways in which specific elements of digital content influence consumer brand engagement. This extension of the UGT expands its theoretical domain to include examinable gratifications from DCM and consumer brand engagement, offer- ing a frame of reference for future studies in these areas. Thirdly, since existing literature lacks a comprehensive exploration of the com- plementary effects of collaborative partnerships between brands and SMIs on social media consumers, the study extends the complementarity theory (Ennen and Richter 2010; Hakala 2011) with a replicable model that captures the nexuses among DCM, consumer brand engagement, and SMIs. By demonstrating the ampli- fication effect of blending brands’ DCM and SMIs’ content on consumer engage- ment, the study enriches the literature by extending the complementarity theory to the domains of brand engagement, digital content marketing, and influencer 18 R. ODOOM marketing. Even with the negative amplification effect, the results shed light on the cognitive and emotional processes that come into play when individuals are exposed to content that evokes negative emotions. Understanding this phenom- enon is crucial for researchers and scholars, especially in psychology, sociology, and communication studies, as it helps in developing a more comprehensive model of human behaviour in online environments. Moreover, the theoretical appeal extends to the broader study of social media’s impact on society, as it highlights the potential dangers of amplifying negative emotions through content dissemination and how this can shape opinions, attitudes, and behaviours. Practical implications The empirical insights from this study have important practical implications for marketing managers. The findings highlight the significance of understanding the various focal elements within DCM that trigger brand engagement among con- sumers on social media. The study suggests that digital content with informative, commercial, entertaining, and emotional elements are predictors of brand engage- ment. However, it cautions that these elements are conditional and not invariant for all gratifying elements, particularly those related to negative emotions. While fear appeal may be effective in conventional advertising, on social media, such negative emotions may be unwarranted for digital content. Hence, brands execut- ing DCM campaigns on social media need to be cautious about how emotional elements are presented in their content. Furthermore, the study suggests that information-focused content, despite its potential ‘dryness’, can still effectively engage consumers when presented in a certain way or different context, even when consumers are familiar with the brand. Accordingly, marketing managers can leverage the identified elements of digital content marketing to provide valuable and meaningful brand experiences, strategically using digital content marketing as a tool for engagement. Likewise, the study highlights the amplification effect on consumer engagement when DCM is blended with SMIs’ content. This underscores the potential complementarity between influencer marketing and content marketing. Marketing managers have an opportunity to enhance their digital content marketing campaigns by leveraging the collaborative efforts of social media influencers and their branded content. By under- standing the niche styles and opinions of influencers, brands can create the right balance between their DCM campaigns and partner influencers to avoid any nega- tive contagion effect. Quite remarkably, the negative emotion focus amplification effect is so potent that it can even transform a (previously non-significant) negative relationship into a (now sig- nificant) negative relationship. This underscores the utmost importance, especially when employing SMIs, of generating and disseminating content that steers clear of evoking and conveying negative emotions such as fear, shame, and sadness. Finally, social media influencers can also strengthen their parasocial relationships with their audience by leveraging advanced digital tools to create branded content that enhances the psycho- logical connection with their followers. JOURNAL OF MARKETING COMMUNICATIONS 19 Limitations While our study provides some valuable insights for research, it is important to acknowledge some limitations. Firstly, our research examined these constructs across various social media platforms without focusing on any specific platform or type of digital content. It is important to recognize that each platform may have unique features and characteristics that could potentially impact the results. Therefore, future research should consider investigating these potential variations by narrowing the focus to specific social media platforms or experimenting with specific types of digital content. Additionally, the use of an online survey for data collection has inherent limitations in business research. Moreover, our sample recruitment did not instruct respondents to focus on any specific brands or real content from social media influencers before responding. This condition may have influenced the outcomes observed in our study. It is important to note that our research design was cross-sectional, which brings its own set of challenges and limitations. Furthermore, it is worth mentioning that our study did not involve experimental manipulation, and therefore, the relationships tested in this study do not establish causality between the variables. Lastly, given the mixed demographic details of our respondents, there may be empirical variations if the constructs are examined exclusively within different profile clusters. Despite these limitations, our study provides a foundation for further exploration and offers valuable insights into the complex dynamics of DCM, SMIs, and brand engagement. Future research can build upon these findings to delve deeper into specific platforms, content types, and causal relationships among the variables. Disclosure statement No potential conflict of interest was reported by the author(s). Funding This work was supported by the University of Ghana Business School [UGBS/RCC/2022-23/017]. Notes on contributor Raphael Odoom (PhD) is with the University of Ghana Business School and is currently a research associate at the Department of Marketing Management, University of Johannesburg. His research interests are in the areas of digital marketing, branding, and small business management. He has published in the International Journal of Contemporary Hospitality Management, Journal of Brand Management, European Business Review, Journal of Enterprise Information Management, Journal of Product and Brand Management, Marketing Intelligence and Planning, Qualitative Market Research: An International Journal, among others. 20 R. ODOOM References Abhishek, A., and M. Srivastava. 2021. “Mapping the Influence of Influencer Marketing: A Bibliometric Analysis.” Marketing Intelligence & Planning 39 (7): 979–1003. https://doi.org/10. 1108/MIP-03-2021-0085. AlRabiah, S., B. Marder, D. Marshall, and R. Angell. 2022. “Too Much Information: An Examination of the Effects of Social Self-Disclosure Embedded within Influencer eWOM campaigns.” Journal of Business Research 152:93–105. https://doi.org/10.1016/j.jbusres.2022.07.029 . Appel, G., L. Grewal, R. Hadi, and A. T. Stephen. 2020. “The Future of Social Media in Marketing.” Journal of the Academy of Marketing Science 48 (1): 79–95. https://doi.org/10.1007/s11747-019- 00695-1. Ashley, C., and T. Tuten. 2015. “Creative Strategies in Social Media Marketing: An Exploratory Study of Branded Social Content and Consumer Engagement.” Psychology & Marketing 32 (1): 15–27. https://doi.org/10.1002/mar.20761. Bagozzi, R. P., and Y. Yi. 1988. “On the evaluation of structural equation models.” Journal of the Academy of Marketing Science 16 (1): 74–94. Beard, F., B. Petrotta, and L. Dischner. 2021. “A History of Content Marketing.” Journal of Historical Research in Marketing 13 (2): 139–158. https://doi.org/10.1108/JHRM-10-2020-0052. Boerman, S. C., L. M. Willemsen, and E. P. Van Der Aa. 2017. ““This Post is sponsored”: Effects of Sponsorship Disclosure on Persuasion Knowledge and Electronic Word of Mouth in the Context of Facebook.” Journal of Interactive Marketing 38:82–92. https://doi.org/10.1016/j.intmar.2016.12. 002. Borchers, N. S., and N. Enke. 2021. “Managing Strategic Influencer Communication: A Systematic Overview on Emerging Planning, Organization, and Controlling Routines.” Public Relations Review 47 (3): 102041. https://doi.org/10.1016/j.pubrev.2021.102041 . Bu, Y., J. Parkinson, and P. Thaichon. 2021. “Digital Content Marketing as a Catalyst for E-WOM in Food Tourism.” Australasian Marketing Journal 29 (2): 142–154. https://doi.org/10.1016/j.ausmj. 2020.01.001. Campbell, C., and J. R. Farrell. 2020. “More Than Meets the Eye: The Functional Components Underlying Influencer Marketing.” Business Horizons 63 (4): 469–479. https://doi.org/10.1016/j. bushor.2020.03.003 . Christodoulides, G., N. Michaelidou, and N. T. Siamagka. 2019. “Social Media, Content Marketing and Engagement Strategies in B2B.” Industrial Marketing Management 81:87–88. https://doi.org/10. 1016/j.indmarman.2018.03.013. Chung, N., and H. Han. 2017. “The Relationship Among tourists’ Persuasion, Attachment and Behavioral Changes in Social Media.” Technological Forecasting and Social Change 123:370–380. https://doi.org/10.1016/j.techfore.2016.09.005 . Cocker, H. L., and J. Cronin. 2017. “Charismatic Authority and the YouTuber: Unpacking the New Cults of Personality.” Marketing Theory 17 (4): 455–472. https://doi.org/10.1177/ 1470593117692022. Content Marketing Institute. (2020). “Annual B2B Content Marketing Benchmarks, Budgets, and Trends: Insights for 2021 Report.” Available at https://tinyurl.com/3vjdn7ce . Delbaere, M., B. Michael, and B. J. Phillips. 2021. “Social Media Influencers: A Route to Brand Engagement for Their Followers.” Psychology & Marketing 38 (1): 101–112. https://doi.org/10. 1002/mar.21419. Devereux, E., L. Grimmer, and M. Grimmer. 2020. “Consumer Engagement on Social Media: Evidence from Small Retailers.” Journal of Consumer Behaviour 19 (2): 151–159. https://doi.org/10.1002/cb. 1800. Dolega, L., F. Rowe, and E. Branagan. 2021. “Going Digital? The Impact of Social Media Marketing on Retail Website Traffic, Orders and Sales.” Journal of Retailing and Consumer Services 60:102501. https://doi.org/10.1016/j.jretconser.2021.102501 . Eigenraam, A. W., J. Eelen, A. Van Lin, and P. W. Verlegh. 2018. “A Consumer-Based Taxonomy of Digital Customer Engagement Practices.” Journal of Interactive Marketing 44:102–121. https://doi. org/10.1016/j.intmar.2018.07.002. JOURNAL OF MARKETING COMMUNICATIONS 21 https://doi.org/10.1108/MIP-03-2021-0085 https://doi.org/10.1108/MIP-03-2021-0085 https://doi.org/10.1016/j.jbusres.2022.07.029 https://doi.org/10.1007/s11747-019-00695-1 https://doi.org/10.1007/s11747-019-00695-1 https://doi.org/10.1002/mar.20761 https://doi.org/10.1002/mar.20761 https://doi.org/10.1108/JHRM-10-2020-0052 https://doi.org/10.1016/j.intmar.2016.12.002 https://doi.org/10.1016/j.intmar.2016.12.002 https://doi.org/10.1016/j.pubrev.2021.102041 https://doi.org/10.1016/j.ausmj.2020.01.001 https://doi.org/10.1016/j.ausmj.2020.01.001 https://doi.org/10.1016/j.bushor.2020.03.003 https://doi.org/10.1016/j.bushor.2020.03.003 https://doi.org/10.1016/j.indmarman.2018.03.013 https://doi.org/10.1016/j.indmarman.2018.03.013 https://doi.org/10.1016/j.techfore.2016.09.005 https://doi.org/10.1177/1470593117692022 https://doi.org/10.1177/1470593117692022 https://tinyurl.com/3vjdn7ce https://doi.org/10.1002/mar.21419 https://doi.org/10.1002/mar.21419 https://doi.org/10.1002/cb.1800 https://doi.org/10.1002/cb.1800 https://doi.org/10.1016/j.jretconser.2021.102501 https://doi.org/10.1016/j.intmar.2018.07.002 https://doi.org/10.1016/j.intmar.2018.07.002 Ennen, E., and A. Richter. 2010. “The Whole is More Than the Sum of Its Parts—Or is It? A Review of the Empirical Literature on Complementarities in Organizations.” Journal of Management 36 (1): 207–233. https://doi.org/10.1177/0149206309350083 . Friestad, M., and P. Wright. 1994. “The Persuasion Knowledge Model: How People Cope with Persuasion Attempts.” Journal of Consumer Research 21 (1): 1–31. https://doi.org/10.1086/ 209380 . Gao, Q., and C. Feng. 2016. “Branding with Social Media: User Gratifications, Usage Patterns, and Brand Message Content Strategies.” Computers in Human Behavior 63:868–890. https://doi.org/ 10.1016/j.chb.2016.06.022 . Gavilanes, J. M., T. C. Flatten, and M. Brettel. 2018. “Content Strategies for Digital Consumer Engagement in Social Networks: Why Advertising is an Antecedent of Engagement.” Journal of Advertising 47 (1): 4–23. https://doi.org/10.1080/00913367.2017.1405751 . Giakoumaki, C., and A. Krepapa. 2020. “Brand Engagement in Self‐Concept and Consumer Engagement in Social Media: The Role of the Source.” Psychology & Marketing 37 (3): 457–465. https://doi.org/10.1002/mar.21312. Hakala, H. 2011. “Strategic Orientations in Management Literature: Three Approaches to Understanding the Interaction Between Market, Technology, Entrepreneurial and Learning Orientations.” International Journal of Management Reviews 13 (2): 199–217. https://doi.org/10. 1111/j.1468-2370.2010.00292.x. Hamilton, B. H., and J. A. Nickerson. 2003. “Correcting for Endogeneity in Strategic Management Research.” Strategic Organization 1 (1): 51–78. https://doi.org/10.1177/1476127003001001218. Hollebeek, L. D., and K. Macky. 2019. “Digital Content Marketing’s Role in Fostering Consumer Engagement, Trust, and Value: Framework, Fundamental Propositions, and Implications.” Journal of Interactive Marketing 45:27–41. https://doi.org/10.1016/j.intmar.2018.07.003. Hu, L., Q. Min, S. Han, and Z. Liu. 2020. “Understanding followers’ Stickiness to Digital Influencers: The Effect of Psychological Responses.” International Journal of Information Management 54:102169. https://doi.org/10.1016/j.ijinfomgt.2020.102169 . Jiménez-Castillo, D., and R. Sánchez-Fernández. 2019. “The Role of Digital Influencers in Brand Recommendation: Examining Their Impact on Engagement, Expected Value and Purchase Intention.” International Journal of Information Management 49:366–376. https://doi.org/10. 1016/j.ijinfomgt.2019.07.009 . John, L. K., O. Emrich, S. Gupta, and M. I. Norton. 2017. “Does “Liking” Lead to Loving? The Impact of Joining a Brand’s Social Network on Marketing Outcomes.” Journal of Marketing Research 54 (1): 144–155. https://doi.org/10.1509/jmr.14.0237 . Katz, E., J. G. Blumler, and M. Gurevitch. 1973. “Uses and Gratifications Research.” The Public Opinion Quarterly 37 (4): 509–523. https://doi.org/10.1086/268109 . Khan, I. 2022. “Do brands’ Social Media Marketing Activities Matter? A Moderation Analysis.” Journal of Retailing and Consumer Services 64:102794. https://doi.org/10.1016/j.jretconser.2021.102794 . Ki, C. W. C., L. M. Cuevas, S. M. Chong, and H. Lim. 2020. “Influencer Marketing: Social Media Influencers as Human Brands Attaching to Followers and Yielding Positive Marketing Results by Fulfilling Needs.” Journal of Retailing and Consumer Services 55:102133. https://doi.org/10.1016/j. jretconser.2020.102133. Ki, C. W. C., and Y. K. Kim. 2019. “The Mechanism by Which Social Media Influencers Persuade Consumers: The Role of consumers’ Desire to Mimic.” Psychology & Marketing 36 (10): 905–922. https://doi.org/10.1002/mar.21244. Knoll, J., and J. Matthes. 2017. “The Effectiveness of Celebrity Endorsements: A Meta-Analysis.” Journal of the Academy of Marketing Science 45 (1): 55–75. https://doi.org/10.1007/s11747-016- 0503-8. Koiso-Kanttila, N. 2004. “Digital Content Marketing: A Literature Synthesis.” Journal of Marketing Management 20 (1–2): 45–65. https://doi.org/10.1362/026725704773041122. Kumar, A., R. Bezawada, R. Rishika, R. Janakiraman, and P. K. Kannan. 2016. “From Social to Sale: The Effects of Firm-Generated Content in Social Media on Customer Behavior.” Journal of Marketing 80 (1): 7–25. https://doi.org/10.1509/jm.14.0249 . 22 R. ODOOM https://doi.org/10.1177/0149206309350083 https://doi.org/10.1086/209380 https://doi.org/10.1086/209380 https://doi.org/10.1016/j.chb.2016.06.022 https://doi.org/10.1016/j.chb.2016.06.022 https://doi.org/10.1080/00913367.2017.1405751 https://doi.org/10.1002/mar.21312 https://doi.org/10.1002/mar.21312 https://doi.org/10.1111/j.1468-2370.2010.00292.x https://doi.org/10.1111/j.1468-2370.2010.00292.x https://doi.org/10.1177/1476127003001001218 https://doi.org/10.1016/j.intmar.2018.07.003 https://doi.org/10.1016/j.ijinfomgt.2020.102169 https://doi.org/10.1016/j.ijinfomgt.2019.07.009 https://doi.org/10.1016/j.ijinfomgt.2019.07.009 https://doi.org/10.1509/jmr.14.0237 https://doi.org/10.1086/268109 https://doi.org/10.1016/j.jretconser.2021.102794 https://doi.org/10.1016/j.jretconser.2020.102133 https://doi.org/10.1016/j.jretconser.2020.102133 https://doi.org/10.1002/mar.21244 https://doi.org/10.1002/mar.21244 https://doi.org/10.1007/s11747-016-0503-8 https://doi.org/10.1007/s11747-016-0503-8 https://doi.org/10.1362/026725704773041122 https://doi.org/10.1509/jm.14.0249 Lee, M. T., and C. Theokary. 2021. “The Superstar Social Media Influencer: Exploiting Linguistic Style and Emotional Contagion Over Content?” Journal of Business Research 132:860–871. https://doi. org/10.1016/j.jbusres.2020.11.014 . Lou, C., and S. Yuan. 2019. “Influencer Marketing: How Message Value and Credibility Affect Consumer Trust of Branded Content on Social Media.” Journal of Interactive Advertising 19 (1): 58–73. https://doi.org/10.1080/15252019.2018.1533501. Luo, X. 2002. “Uses and Gratifications Theory and E-Consumer Behaviors: A Structural Equation Modeling Study.” Journal of Interactive Advertising 2 (2): 34–41. https://doi.org/10.1080/15252019. 2002.10722060. Luo, M. M., S. Chea, and J. S. Chen. 2011. “Web-Based Information Service Adoption: A Comparison of the Motivational Model and the Uses and Gratifications Theory.” Decision Support Systems 51 (1): 21–30. https://doi.org/10.1016/j.dss.2010.11.015 . Mathew, V., and M. Soliman. 2021. “Does Digital Content Marketing Affect Tourism Consumer Behavior? An Extension of Technology Acceptance Model.” Journal of Consumer Behaviour 20 (1): 61–75. https://doi.org/10.1002/cb.1854. Müller, J., and F. Christandl. 2019. “Content is King–But Who is the King of Kings? The Effect of Content Marketing, Sponsored Content & User-Generated Content on Brand Responses.” Computers in Human Behavior 96:46–55. https://doi.org/10.1016/j.chb.2019.02.006. Oh, C., Y. Roumani, J. K. Nwankpa, and H. F. Hu. 2017. “Beyond Likes and Tweets: Consumer Engagement Behavior and Movie Box Office in Social Media.” Information & Management 54 (1): 25–37. https://doi.org/10.1016/j.im.2016.03.004. Plume, C. J., and E. L. Slade. 2018. “Sharing of Sponsored Advertisements on Social Media: A Uses and Gratifications Perspective.” Information Systems Frontiers 20 (3): 471–483. https://doi.org/10. 1007/s10796-017-9821-8. Podsakoff, P. M., S. B. MacKenzie, and N. P. Podsakoff. 2012. “Sources of Method Bias in Social Science Research and Recommendations on How to Control It.” Annual Review of Psychology 63 (1): 539–569. https://doi.org/10.1146/annurev-psych-120710-100452 . Rattan, J. (2019), “101 Different Types of Digital Content”. Available at: https://tinyurl.com/ywh5fk6d . Rihl, A., and C. Wegener. 2019. “YouTube Celebrities and Parasocial Interaction: Using Feedback Channels in Mediatized Relationships.” Convergence: The International Journal of Research into New Media Technologies 25 (3): 554–566. https://doi.org/10.1177/1354856517736976. Rowley, J. 2008. “Understanding Digital Content Marketing.” Journal of Marketing Management 24 (5–6): 517–540. https://doi.org/10.1362/026725708X325977. Scheer, L. K., and L. W. Stern. 1992. “The Effect of Influence Type and Performance Outcomes on Attitude Toward the Influencer.” Journal of Marketing Research 29 (1): 128–142. https://doi.org/10. 1177/002224379202900111 . Sheng, S., K. Z. Zhou, and J. J. Li. 2011. “The Effects of Business and Political Ties on Firm Performance: Evidence from China.” Journal of Marketing 75 (1): 1–15. https://doi.org/10.1509/ jm.75.1.1 . Sprott, D., S. Czellar, and E. Spangenberg. 2009. “The Importance of a General Measure of Brand Engagement on Market Behavior: Development and Validation of a Scale.” Journal of Marketing Research 46 (1): 92–104. https://doi.org/10.1509/jmkr.46.1.92 . Stieglitz, S., and L. Dang-Xuan. 2013. “Emotions and Information Diffusion in Social Media— Sentiment of Microblogs and Sharing Behavior.” Journal of Management Information Systems 29 (4): 217–248. https://doi.org/10.2753/MIS0742-1222290408 . Tafesse, W. 2016. “An Experiential Model of Consumer Engagement in Social Media.” Journal of Product & Brand Management 25 (5): 424–434. https://doi.org/10.1108/JPBM-05-2015-0879. Tellis, G. J., D. J. MacInnis, S. Tirunillai, and Y. Zhang. 2019. “What Drives Virality (Sharing) of Online Digital Content? The Critical Role of Information, Emotion, and Brand Prominence.” Journal of Marketing 83 (4): 1–20. https://doi.org/10.1177/0022242919841034 . Torres, P., M. Augusto, and M. Matos. 2019. “Antecedents and Outcomes of Digital Influencer Endorsement: An Exploratory Study.” Psychology & Marketing 36 (12): 1267–1276. https://doi. org/10.1002/mar.21274. JOURNAL OF MARKETING COMMUNICATIONS 23 https://doi.org/10.1016/j.jbusres.2020.11.014 https://doi.org/10.1016/j.jbusres.2020.11.014 https://doi.org/10.1080/15252019.2018.1533501 https://doi.org/10.1080/15252019.2002.10722060 https://doi.org/10.1080/15252019.2002.10722060 https://doi.org/10.1016/j.dss.2010.11.015 https://doi.org/10.1002/cb.1854 https://doi.org/10.1016/j.chb.2019.02.006 https://doi.org/10.1016/j.im.2016.03.004 https://doi.org/10.1007/s10796-017-9821-8 https://doi.org/10.1007/s10796-017-9821-8 https://doi.org/10.1146/annurev-psych-120710-100452 https://tinyurl.com/ywh5fk6d https://doi.org/10.1177/1354856517736976 https://doi.org/10.1362/026725708X325977 https://doi.org/10.1177/002224379202900111 https://doi.org/10.1177/002224379202900111 https://doi.org/10.1509/jm.75.1.1 https://doi.org/10.1509/jm.75.1.1 https://doi.org/10.1509/jmkr.46.1.92 https://doi.org/10.2753/MIS0742-1222290408 https://doi.org/10.1108/JPBM-05-2015-0879 https://doi.org/10.1177/0022242919841034 https://doi.org/10.1002/mar.21274 https://doi.org/10.1002/mar.21274 Van Doorn, J., K. N. Lemon, V. Mittal, S. Nass, D. Pick, P. Pirner, and P. C. Verhoef. 2010. “Customer Engagement Behavior: Theoretical Foundations and Research Directions.” Journal of Service Research 13 (3): 253–266. https://doi.org/10.1177/1094670510375599. Wall, A., and C. Spinuzzi. 2018. “The Art of Selling-Without-Selling: Understanding the Genre Ecologies of Content Marketing.” Technical Communication Quarterly 27 (2): 137–160. https:// doi.org/10.1080/10572252.2018.1425483. Wang, W. L., E. C. Malthouse, B. Calder, and E. Uzunoglu. 2019. “B2B Content Marketing for Professional Services: In-Person versus Digital Contacts.” Industrial Marketing Management 81:160–168. https://doi.org/10.1016/j.indmarman.2017.11.006. Whiting, A., and D. Williams. 2013. “Why People Use Social Media: A Uses and Gratifications Approach.” Qualitative Market Research: An International Journal 16 (4): 362–369. https://doi. org/10.1108/QMR-06-2013-0041. Zhang, J., Y. Sung, and W. N. Lee. 2010. “To Play or Not to Play: An Exploratory Content Analysis of Branded Entertainment in Facebook.” American Journal of Business 25 (1): 53–64. https://doi.org/ 10.1108/19355181201000005. Zhou, S., M. Blazquez, H. McCormick, and L. Barnes. 2021. “How Social Media influencers’ Narrative Strategies Benefit Cultivating Influencer Marketing: Tackling Issues of Cultural Barriers, Commercialised Content, and Sponsorship Disclosure.” Journal of Business Research 134:122–142. https://doi.org/10.1016/j.jbusres.2021.05.011. 24 R. ODOOM https://doi.org/10.1177/1094670510375599 https://doi.org/10.1080/10572252.2018.1425483 https://doi.org/10.1080/10572252.2018.1425483 https://doi.org/10.1016/j.indmarman.2017.11.006 https://doi.org/10.1108/QMR-06-2013-0041 https://doi.org/10.1108/QMR-06-2013-0041 https://doi.org/10.1108/19355181201000005 https://doi.org/10.1108/19355181201000005 https://doi.org/10.1016/j.jbusres.2021.05.011 Abstract Introduction Related literature DCM and consumer brand engagement on social media Conceptual framework and hypotheses Information focus Entertainment focus Emotion focus Commercial focus Social media influencers’ brand content Methodology Questionnaire and measurement items Data and sample Common method bias Results and analyses Evaluation of measurement model Moderated hierarchical regression tests of hypotheses Discussions Theoretical implications Practical implications Limitations Disclosure statement Funding Notes on contributor References