Using message strategy to drive consumer behavioral engagement on social media

Wondwesen Tafesse (School of Business and Economics, UiT Norges arktiske universitet, Tromso, Norway)
Anders Wien (School of Business and Economics, UiT Norges arktiske universitet, Tromso, Norway)

Journal of Consumer Marketing

ISSN: 0736-3761

Publication date: 14 May 2018

Abstract

Purpose

This study aims to examine how message strategy influences consumer behavioral engagement in social media. To this end, the study develops a comprehensive typology of branded content in social media and tests for its effect on consumer behavioral engagement.

Design/methodology/approach

A sample of brand posts derived from the official Facebook pages of top corporate brands was double-coded using an elaborate coding instrument. Message strategy was operationalized using three main message strategies (i.e. informational, transformational and interactional) and their paired combinations. Consumer behavioral engagement was operationalized using consumer actions of liking and sharing brand posts. Proposed relationships were tested with MANCOVA and univariate ANOVAs.

Findings

Results indicate that the transformational message strategy is the most powerful driver of consumer behavioral engagement, while no significant difference is observed between the informational and the interactional message strategies. Further, complementing the informational and interactional message strategies with the transformational message strategy markedly enhances their effectiveness.

Practical implications

Useful managerial guidance to develop effective message strategies is offered. In particular, the importance of transformational messages, both as a standalone and a complementary message strategy, is underscored. By mastering and deploying transformational messages more frequently in their social media communication, marketers could improve their effectiveness.

Originality/value

Drawing on a theory-driven typology, this study sheds light on how message strategy shapes consumer behavioral engagement in a social media context. Importantly, the study documents pioneering empirical evidence regarding the effect of combined message strategies on consumer behavioral engagement.

Keywords

Citation

Tafesse, W. and Wien, A. (2018), "Using message strategy to drive consumer behavioral engagement on social media", Journal of Consumer Marketing, Vol. 35 No. 3, pp. 241-253. https://doi.org/10.1108/JCM-08-2016-1905

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Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


Introduction

Marketers are increasingly connecting with customers and fans through social media. Social media represent a dynamic new space to reach a large number of people, interact with them and leverage their voices for greater marketing impact (Lipsman et al., 2012). Social media marketing is a deeply engaging form of communication that enables brands to connect with customers at a personal level, and vice versa (Berthon et al., 2012; Gensler et al., 2013). Despite its potential, however, social media marketing is fraught with difficulties. In particular, marketers are challenged to demonstrate the impact of social media at a time when brands are declining in organic reach and user engagement (Hubspot, 2016). This trend is generally attributed to the vast volume of content being produced on social media. Social networks are continuously tweaking their algorithms to deliver the most relevant content, often by suppressing content from third-party publishers such as brands. Inevitably, these changes have brought the issue of content creation to the forefront of social media marketing. Brands must now create relevant and engaging content to overcome their systemic disadvantage and thrive on social media (CMI, 2015).

Prior research has examined how brands on social media can enhance consumer engagement using content strategy (Araujo et al., 2015; Kim et al., 2015; Swani et al., 2016), thus identifying specific message and media strategies that are associated with heightened levels of consumer engagement. However, the insight that emerged from this work is far from consistent. In particular, theory-based approaches that comprehensively define and operationalize branded content in social media are lacking (Tafesse and Wien, 2017). Branded content is conceptualized in a myriad of ways, giving rise to conflicting findings as to which message and media strategies are the most effective in stimulating consumer engagement. The present study aims to address this gap by developing a theoretically grounded typology of branded content in social media and testing its effect on consumer engagement.

The study’s context is brand pages, which represent an interactive platform used by brands to connect with customers and fans on social media (Lipsman et al., 2012). At the heart of brand pages are brand posts – a frequent and concise update that brands communicate daily to their customers and fans. Brand posts represent a rich form of communication that enables a variety of brand messages to be conveyed to customers and fans (Ashley and Tuten, 2015; Tafesse and Wien, 2017). Building on the advertising literature, the study develops three main message strategies that typically underpin brand posts: informational, transformational and interactional. The informational message strategy presents factual product and services information in clear and concrete terms; the transformational message strategy associates the experience and identity of the focal brand with desirable psychological characteristics; and the interactional message strategy cultivates ongoing interactions with customers (Puto and Wells, 1984; Laskey et al., 1989). The study further considers the possibility of brand posts simultaneously communicating multiple message strategies. Because brand posts support multiple media types, including text, photo, video and website links, marketers can convey distinct messages via each medium (Araujo et al., 2015). Thus, in addition to the three main message strategies, the study considers their paired combinations (i.e. informational and transformational, informational and interactional and transformational and interactional). Subsequently, the study links the proposed message strategies to consumer behavioral engagement, which is conceptualized in terms of consumer actions of liking and sharing brand posts. These actions represent an active form of brand engagement in which consumers take extra steps to interact with brand posts beyond mere exposure (Gummerus et al., 2012; Kabadayi and Price, 2014).

The findings advance the literature by developing and testing a theoretically grounded typology of branded content in social media, which offers a comprehensive framework for its systematic analysis. The study also quantifies the relative impact of different message strategies on consumer behavioral engagement. In particular, it presents some of the first empirical evidence regarding the effect of combined message strategies on consumer behavioral engagement. Together, the findings generate helpful managerial insights to develop effective messaging approaches on social media.

Literature review

Consumer behavioral engagement

Consumer engagement is a motivational state that leads consumers to a heightened involvement with interactive, brand-related activities and experiences, often, but not exclusively, in online environments (Brodie et al., 2011; Harmeling et al., 2016). Consumer engagement is a multi-dimensional concept manifested in various forms of emotional, cognitive and behavioral responses with a brand or firm focus (Hollebeek et al., 2014). The present study is particularly interested in the behavioral dimension of consumer engagement.

Consumers are behaviorally engaged when they make proactive efforts with a brand or firm focus, beyond transactions (Van Droom et al., 2010). Behavioral engagement is an extra-role behavior in which consumers make voluntary resource contributions (e.g. knowledge, experience, time, network resources and social influence) to a brand or firm (Jaakkola and Alexander, 2014; Van Doorm et al., 2010). It is driven by consumers rather than firm motivations and encompasses different exogenous resource contributions to a firm’s marketing functions (Harmeling et al., 2016; Jaakkola and Alexander, 2014). For instance, online word-of-mouth (WOM) behavior (e.g. blogging, Web posting and social media interactions) contributes to a firm’s marketing communication effort through consumer-to-consumer communication, whereas customer feedback contributes to product innovation (Gummerus et al., 2012; Jaakkola and Alexander, 2014). Harmeling et al. (2016) characterizes behaviorally engaged consumers as “pseudo marketers” who contribute to the effectiveness of a firm’s core marketing functions.

Jaakkola and Alexander (2014) delineate two broad patterns of behavioral engagement: consumer involvement in product development and innovation and consumer involvement in WOM behavior. First, behaviorally engaged consumers can help improve or develop a firm’s offerings by providing constructive feedback, ideas and information. Second, they can influence other consumers’ brand perceptions through WOM and other forms of consumer-to-consumer interaction. Consumer behavioral engagement can occur organically, in response to personal product experience or a firm’s marketing communication, or through a firm’s facilitation – often involving engagement initiatives that encourages consumer resource contribution to the firm (Vivek et al., 2012). Organic consumer engagement is generally accepted as more memorable, trustworthy and behaviorally consequential than firm-initiated consumer engagement (Harmeling et al., 2016).

On social media, consumers’ WOM-related engagement behavior often assumes primacy (Gummerus et al., 2012). Accordingly, our focus here is on consumer actions of liking, sharing and commenting on brand posts as typical manifestation of consumer behavioral engagement. These actions require consumers to take tangible steps beyond mere brand post exposure, such as clicking on the like or share button or typing a comment (Gummerus et al., 2012; Kabadayi and Price, 2014). Prior research indicates that involvement with the content or a feeling of affinity to the brand drive consumers to like, share and comment on brand posts (Kabadayi and Price, 2014; Pereira et al., 2014). Consumers take these actions because they find the specific brand posts intrinsically stimulating or because of a feeling of deeper connection to the brand (Beukeboom et al., 2015; Wallace et al., 2014). These actions are instrumental in the diffusion of brand messages in social media (Swani et al., 2016). As a growing number of people like, share and comment on brand posts, they achieve viral status, creating exponential message reach and brand exposure (Berger and Milkman, 2012; Lipsman et al., 2012). Consistent with this view, we used actual counts of brand post likes and brand post shares to quantify consumer behavioral engagement. We excluded comments because mere comment count (without considering sentiment) offers limited managerial value and the study’s manual coding approach made it impossible to decipher the brand sentiment inherent in a large volume of consumer comments.

Consumer behavioral engagement and message strategy

Given the importance of consumer behavioral engagement in social media, there have been several attempts to understand its influencing factors. Table I summarizes representative studies that relate message strategy to consumer behavioral engagement in social media.

It is evident that message strategy is conceptualized in numerous ways. While earlier studies developed a simplistic content typology of information, entertainment and transactions (De Vries et al., 2012; Cvijikj and Michahelles, 2013), more recent studies developed relatively sophisticated typologies that differentiate emotional, informational and brand-related message themes (Ashley and Tuten, 2015; Araujo et al., 2015). However, even these recent typologies do not fully capture the complexity of branded social media content. For instance, marketers typically incorporate multiple message themes within individual brand posts. Support for multiple media types, including text, photo, video and website links, enable marketers to convey distinct messages via each medium. For instance, while a Facebook photo displays a branded product, the associated text can make a functional product claim, pose a personal question to fans or promote an upcoming brand event. In this way, marketers often communicate multiple messages via individual brand posts; yet, this aspect is rarely captured in the literature.

Second, the proposed content typologies lack consistent conceptual development. Their piecemeal nature makes it difficult to draw clear conclusions regarding the impact of message strategy on consumer behavioral engagement. Certain studies identify content categories as critical drivers of consumer behavioral engagement, but these findings fail to replicate in other studies. Entertainment, informational and emotional content categories are among victims of this inconsistency (Table I). Similarly, the proposed content typologies, with few exceptions (Araujo et al., 2015), do not appear to have emerged from a rigorous coding approach, such as the use of a double-coding procedure when human coders are involved (Kolbe and Burnett, 1991). The absence of consistent theoretical development combined with less than rigorous coding procedures introduce a measure of subjectivity, rendering the resulting conclusions ambiguous.

While message typologies have long been developed by advertising literature based on content analysis of TV and print commercials (Laskey et al., 1989; Puto and Wells, 1984), social media literature has largely ignored this research. The message typologies developed in the advertising literature would have provided a solid basis to study branded content in social media, as the basic underlying creative principles of traditional and online advertising (i.e. rational vs emotional appeals) are the same (Ashley and Tuten, 2015; Golan and Zaidner, 2008). The message typologies have also been validated across a variety of off-line and online contexts and are well integrated into advertising theory and practice (Golan and Zaidner, 2008). Likewise, the formulation of the message typologies is broad enough to allow for a wide assortment of branded social media content to be covered. Accordingly, we draw on the advertising message typologies to develop our typology of branded content in social media.

Message strategy

Message strategy is a guiding principle that determines the content domain of branded content, such as that of a piece of advertising or a brand post (Puto and Wells, 1984). It aligns the nature and character of branded content with consumers’ specific needs, thereby bridging the gap between what marketers want to say and what consumers need to hear (Laskey et al., 1989; Taylor, 1999). Message strategy involves designing marketing communication that increases the likelihood of attaining desired effects in the target audience (Puto and Wells, 1984). Because of its importance for advertising results, message strategy retains marketers’ interest (Ashley and Tuten, 2015).

The advertising literature has proposed several message strategy typologies. In this study, we build on the complementary studies of Puto and Wells (1984), Laskey et al. (1989) and Taylor (1999) to develop three main message strategies that typically underpin branded content in social media: informational, transformational and interactional.

The informational message strategy presents factual product and services information in clear and concrete terms (Puto and Wells, 1984). Informational messages enable consumers to objectively assess the benefits, functional attributes and proper applications of products and services (Laskey et al., 1989; Puto and Wells, 1984). The informational message strategy is rationally oriented, informing consumers about how products and services can solve their functional problems or fulfill their unmet, functional needs (Puto and Wells, 1984). The transformational message strategy emphasizes the symbolic and hedonic attributes of products and services (Puto and Wells, 1984) and associates the experience and identity of the focal brand with a set of desirable psychological characteristics (Laskey et al., 1989; Puto and Wells, 1984). This strategy infuses brands with feelings and rich symbolic and experiential brand meanings (Laskey et al., 1989; Taylor, 1999). The transformational message strategy is essentially affect-based, making use of emotional, hedonic and symbolic brand cues to create transcendental brand meaning and experiences (Puto and Wells, 1984). The interactional message strategy is absent from the advertising typologies, which is not surprising considering the unidirectional nature of traditional advertising. The likes of TV and newspapers do not allow two-way interactivity, which, on the contrary, is a defining feature of social media (Berthon et al., 2012; Gensler et al., 2013). The interactional message strategy cultivates ongoing customer interactions through the rich interactive affordances of social media. Interactional messages enable brands to connect with their customers at a personal level through one-to-one and many-to-many conversations while also facilitating feelings of community identification by inciting customers to talk with one another. This interactive strategy requires the brand to engage in active conversation with customers, in keeping with social media’s culture of interactivity.

In addition to developing purely informational, purely transformational and purely interactional messages, marketers use these message strategies in combination. The paired combinations of the three main message strategies are therefore considered as well, that is, informational and transformational; informational and interactional; and transformational and interactional.

Hypotheses development

We draw on research on the social transmission of content to address whether and how the proposed message strategies influence consumer behavioral engagement. Information transmission is a defining feature of human interaction. People share information about events, stories, ideas and other people in their daily social interactions (Berger, 2014). The advent of the internet, and more significantly, social media sites, bolstered this intrinsic human predisposition to share information through interpersonal communication (Stephen et al., 2010).

In a marketing context, the social transmission of, and engagement with, content influences both consumers and brands. Content transmission influences product adoption, sales and several other brand-related outcomes (Berger and Milkman, 2012; Cabosky, 2016). The marketing literature has identified five primary motivations that shape the social transmission of, and engagement with, content: emotional, self-image, social, hedonic and functional. The main emotional motivations are the needs to reinforce positive feelings, reduce dissonance and make sense of one’s personal experience (Berger and Milkman, 2012). The main image-centric motivation is the need to project and affirm one’s self-image (Jahn and Kunz, 2012; Wallace et al., 2014). The main social motivation is the need to belong to a social group (Alexandrov et al., 2013; Kim et al., 2014). The main hedonic motivations are the desire for entertainment, escape, variety and cognitive stimulation (Hamilton et al., 2016; Muntinga et al., 2011). Finally, the main functional motivation is the need for information (Lovett et al., 2013).

Studies broadly suggest that alignment between content characteristics and consumer motivation is a significant source of content transmission and behavioral engagement in social media. In this connection, Zhu and Chen (2015, p. 336) argue that marketers’ ability to understand and satisfy consumers’ intrinsic motivations is critical for social media effectiveness. The authors asserted:

[…] to develop their marketing strategy and design appropriate advertising appeals, it is critical that marketers identify and understand the needs and motivations behind social media usage. This enables marketers to communicate with their target audience on a personal, meaningful level via social media.

Additionally, a number of empirical studies have provided detailed insights about specific content types and their effect on content transmission and behavioral engagement. For instance, Berger and Milkman (2012) find a strong association between the emotional valence of online content (i.e. positive emotions) and content virality. Yuki (2015) shows that brand posts that make people “look good” and “feel happy” receive a higher degree of shares on Facebook. Tafesse (2016) reports that brand pages rich in consumer experiences attract more likes and shares on Facebook. Using multiple social media platforms as a setting, Ashley and Tuten (2015) show that branded content that deploys experiential, image and exclusivity themes attains higher levels of consumer engagement.

Together, the above findings demonstrate that social media content that empowers consumers to express their emotions, enhance their self-image and derive hedonic experiences is superior in stimulating content transmission and behavioral engagement. This type of transformational content possesses emotional, image-centric and hedonic brand cues that produce favorable affective responses, in turn prompting content transmission and behavioral engagement (Berger and Milkman, 2012). Moreover, transformational content holds greater transformational value. Harmeling et al. (2016) theorize that transformational (affective) consumer engagement initiatives trigger a heightened sense of psychological brand connection and activate self-transformation and incorporation of the brand into the self. The authors claim that “experiential engagement initiatives often generate long-lasting memories and shifts in beliefs and attitudes […] fostering emotional attachment to the firm” (Harmeling et al., 2016, p. 9).

Building on the above, we anticipate higher levels of behavioral engagement from brand posts that use transformational rather than informational and interactional messages. Transformational messages are consistent with consumers’ emotional, self-image and hedonic motivations to transmit and engage with content and can be expected to inspire higher levels of behavioral engagement. On the other hand, interactional messages mainly emphasize on social connection and community identification, consistent with the social motivation of content transmission (i.e. the need to belong to a social group). Yet, the absence of explicit affective stimuli, such as emotional, self-image and hedonic brand cues, might weaken their ability to generate robust behavioral engagement. Finally, we anticipate the least amount of behavioral engagement from brand posts that use informational messages, which lack the emotional, self-image and social drivers of behavioral engagement and may even be perceived as mere sales pitches because of their exclusive focus on company products and services. In addition, not all brand information can possess elements of novelty and complexity, which often moves people to engage with such content (Berger and Milkman, 2012; Lovett et al., 2013). Building on the preceding arguments, we propose the following three hypotheses:

H1.

Brand posts that use transformational messages will create a higher level of behavioral engagement than brand posts that use informational messages.

H2.

Brand posts that use transformational messages will create a higher level of behavioral engagement than brand posts that use interactional messages.

H3.

Brand posts that use interactional messages will create a higher level of behavioral engagement than brand posts that use informational messages.

Combined brand posts, that is, brand posts that use multiple message strategies simultaneously, are an important feature of brand communication on social media (Araujo et al., 2015). The ability to use multiple media types simultaneously, each of which could be used to convey distinct messages, enable individual brand posts to carry multiple messages. Therefore, understanding the effect of such combined brand posts on consumer behavioral engagement represents a valuable contribution to the literature (Tafesse and Wien, 2017). In developing our prediction, we relied on the same insight that underpinned earlier predictions – alignment between consumer motivation and message strategy. However, we presumed here that message strategies, when presented together, operate in an additive fashion. In other words, the multiple messages embedded within individual brand posts are anticipated to reinforce and complement each other in shaping consumers’ behavioral engagement. Because marketers seek to maximize consumer engagement with each individual brand post, they are unlikely to develop inconsistent or conflicting messages. Following this premise, we expect greater behavioral engagement from a combination of transformational and interactional messages than from a combination of informational and interactional messages, as the additive effect of the former combination exceeds that of the latter. Because transformational and interactional messages individually constitute strong drivers of consumer behavioral engagement, their impact is expected to intensify when they complement one another. This particular combination provides consumers with a powerful set of brand cues that are consistent with their emotional and social motivations to engage with brands on social media. Therefore, the relevant hypothesis is as follows:

H4.

Brand posts that use a combination of transformational and interactional messages will create a higher level of behavioral engagement than brand posts that use a combination of informational and interactional messages.

Method

Sampling decisions

Researchers studying social media must first determine the specific platform (or platforms) they seek to investigate. In this study, Facebook was selected for its popularity among individual users and brands alike. Facebook’s unparalleled success in the marketplace, with over 1.5 billion active monthly users and millions of active brand pages, offers a unique opportunity to gain broadly applicable insights (Smallwood, 2016).

The next step was to generate a sample of brand pages from which individual brand posts could be derived. For this purpose, InterBrand’s Best Global Brands, an annual brand ranking that features top performing global brands from various industries, was use ed. External brand rankings offer a population of brand pages from which an objective and transparent sample could be generated (Swani et al., 2016). From InterBrand’s Best Global Brands 2014, the top 20 brands were selected. As a second step, the number of brand posts to analyze from each brand page had to be determined. This decision has to balance the need for generality with the cost of data collection. Consistent with prior literature, brand posts that cover a four-week period were sampled from each brand page (May 1-31, 2015). A four-week window on social media is long enough for a rich variety of posts to be published (Ashley and Tuten, 2015). Using this approach, a sample of 290 brand posts was derived for final analysis. Table II summarizes key characteristics of the brand pages from which the final sample of brand posts were derived.

Coding procedure

Our coding procedure draws on qualitative content analysis (QCA), which involves “subjective interpretation of the content of text data through the systematic classification process of coding and identifying themes and patterns” (Hsieh and Shannon, 2005, p. 1278). QCA extracts manifest and latent meanings following a systematic and transparent data processing procedure (Zhang and Wildemuth, 2009). The intent is to perform both deductive and inductive coding – the two essential elements of QCA – to generate empirically derived brand post categories, which would then be used to operationalize the theoretically derived message strategies. The QCA proceeded in four interrelated steps.

First, two of this study’s authors reviewed the relevant literature and identified six existing brand post categories: functional, emotional, brand resonance, experiential, social cause and customer relation. Second, the authors inductively coded a convenience sample of brand posts (n = 371) derived from a sample of brand pages that is comparable with, but distinct from, the final sample. The coders carefully studied the text of each brand post and associated information (e.g. photos, videos and links) to infer the main message. Keywords, themes and ideas that seemed to characterize the brand posts were highlighted and discussed. When the main message corresponded to one or more of the deductive categories, the brand posts would be coded accordingly. If not, new categories would be proposed inductively. In this way, four additional brand post categories were identified: educational, current event, personal and brand community. Thus, the two coding procedures together identified ten brand post categories. Table III summarizes the defining characteristics of these brand posts categories[1].

Once the brand post categories were fully specified, they were subsequently validated by applying a double-coding procedure on the final sample of brand posts (n = 290) by means of a detailed coding instrument. As recommended, the two coders worked in complete isolation and made independent coding decisions (Kolbe and Burnett, 1991). On completion of the double coding, inter-coder reliability was calculated using Perreault and Leigh’s (1989) Ir[2]. As reported in Table III, the two coders achieved robust agreement levels on all ten brand post categories (Ir > 0.8), demonstrating the reliability of the proposed categories. The few cases of coding disagreements were resolved through consensus.

The fourth and final step comprised a process of operationalizing the three theoretically derived message strategies by assigning the ten empirically derived brand post categories to them. Brand post categories that share similar or related message themes were assigned to individual message strategies. Accordingly, functional and educational brand posts, characterized by their factual and informative content, operationalized the informational message strategy. A brand post belonging to any one or both of these two categories was coded as using the informational message strategy. Emotional, experiential, brand resonance and cause-related brand posts, characterized by their affective and symbolic brand cues, operationalized the transformational message strategy. A brand post that belonged to any one or a combination of these four categories was coded as using the transformational message strategy. Finally, current event, personal, brand community and customer relationship brand posts, characterized by their conversational appeal, operationalized the interactional message strategy. A brand post that belonged to any one or a combination of these four categories was coded as using the interactional message strategy. The multiple message strategies were likewise operationalized by considering the type of combined message themes embedded within individual brand posts. For instance, when a brand post possessed functional (i.e. informational) and experiential (i.e. transformational) message themes, it would be coded as informational and transformational. When a brand post possessed current-event (i.e. interactional) and educational (i.e. informational) message themes, it would be coded as informational and interactional. All combined brand posts were coded in this way.

In addition to message strategy, we considered three relevant co-variates for our model: industry, Facebook fan-base and media types. Industry was coded into the seven major categories supplied by InterBrands ranking itself: technology, automotive, consumer products, electronics, fashion, financial and retail. Facebook fan-base was coded into five groups based on the number of followers subscribed to the sample brand pages at the time of coding: under 1 million; 1-5 million; 5-10 million; 10-50 million; and above 50 million. Finally, media type was coded into four common media strategies: text and photo; text and video; text, photo and website links; and text, video and website links. While industry and Facebook fan-bases were coded at the brand page level, media type was coded at the brand post level.

Findings

Frequencies and descriptive statistics

The frequency statistics for the four main variables of the study are summarized in Table IV, which confirms our main argument that marketers typically convey multiple messages via individual brand posts. Approximately half of the brand posts in the final sample deployed more than one message strategy. The most common combination is between informational and transformational messages (n = 61), followed closely by transformational and interactional messages (n = 51), while informational and interactional messages were less frequently combined (n = 21). The descriptive statistics for brand post likes (mean = 4,411, SD = 13,188, max = 124,418, Min = 20) and brand post shares (mean = 255, SD = 663, max = 6,668, min = 1) indicate that the sample brand posts garnered a substantial amount of behavioral engagement, revealing the successful application of social media marketing by InterBrand’s global brands.

Hypothesis testing

MANCOVA was used to test the hypotheses. As the two dependent variables (i.e. brand post likes and brand post shares) are conceptually related, MANCOVA is an appropriate choice. However, both dependent variables are not normally distributed, which is a central assumption of MANCOVA. In fact, brand post likes and brand post shares tend to follow a Poisson distribution. Therefore, before performing the MANCOVA test, brand post likes and brand post shares were log-transformed to normalize the data. The MANCOVA test performed afterwards confirmed significant main effects for message strategy: F (10, 560) = 4.1, p < 0.001, Wilk’s λ = 0.87, η = 0.07; and the three covariates, industry: F (2, 560) = 15, p < 0.001, Wilk’s λ = 0.9, η = 0.1; Facebook fan-base: F (2, 560) = 29, p < 0.001, Wilk’s λ = 0.83, η = 0.17; and media types: F (2, 560) = 16, p < 0.001, Wilk’s λ = 0.9, η = 0.1.

In view of the significant multivariate findings, the data were further probed using univariate analyses to uncover significant differences. For this purpose, brand post likes and brand post shares were summed and subsequently log-transformed to construct a single behavioral engagement variable. Summing brand post likes and shares does not alter their basic form nor managerial implication, as the two metrics are expressed using identical measurement units (i.e. counts), and marketers seek to score high on both metrics. Once the univariate analyses were estimated, the resulting mean values and their confidence intervals were back-transformed for meaningful interpretation. Results of the univariate tests are summarized in Table V.

The ANOVA test returned a significant main effect for message strategy [F (5, 284) = 12.2, p < 0.01]. Planned comparison tests revealed that brand posts that used transformational messages created a higher level of behavioral engagement than those that used informational messages (MTrans = 1,300 vs MInfo = 242, p < 0.01), thus fully supporting H1. Similarly, brand posts that used transformational messages created a higher level of behavioral engagement than those that used interactional messages (MTrans = 1,300 vs MInteract = 321, p < 0.01), thus fully supporting H2. However, H3 was not supported, as brand posts that used interactional messages did not create a higher level of behavioral engagement than those that used informational messages (MInteract = 321 vs MInfo = 242, p = 0.6). Finally, in support of H4, brand posts that used a combination of transformational and interactional messages rather than a combination of informational and interactional messages created a higher level of behavioral engagement (MTransInteract = 1,556 vs MInfoInteract = 247, p < 0.01). Although not formally hypothesized, we also tested the effect of brand posts that used a combination of informational and transformational messages. Planned comparison tests revealed that these posts did not create a significantly higher level of behavioral engagement than those that used a combination of informational and transformational messages (MTransInteract = 1,556 vs MInfoTrans = 973, p = 0.14). However, brand posts that used a combination of informational and transformational messages created a significantly higher level of behavioral engagement than those that used a combination of informational and interactional messages (MInfoTrans = 973 vs MInfoInteract = 247, p < 0.01). Thus, it appears that a combination of informational and transformational messages is as effective in driving consumer behavioral engagement as a combination of transformational and interactional messages. Overall, the results established the transformational message strategy as the most potent driver of consumer behavioral engagement, both as an individual and as a complementary message strategy.

Finally, the co-variates were also probed with univariate ANOVAs. The ANOVA for industry category returned a significant main effect [F (6, 283) = 18.68, p < 0.01]. Tukey’s post hoc test indicated that brands in the consumer products, fashion and automotive industries garnered a significantly higher level of behavioral engagement than those in the technology, consumer electronics, retail and financial industries. Because the former group of industries market products that are more hedonic, self-expressive and broadly appealing, their comparative success is not surprising. Similarly, the ANOVA for Facebook fan-base returned a significant main effect [F (4, 285) = 23.93, p < 0.01] and, as might be expected, brand pages with larger fan-bases achieved a significantly higher level of behavioral engagement than those with smaller fan-bases. The former can reach more consumers organically, which in turn drives greater behavioral engagement. Finally, the ANOVA for media type returned a significant main effect [F (3, 286) = 14.34, p < 0.01]. Tukey’s post hoc test revealed that text and photos generated a higher level of behavioral engagement than both text and videos (MTextPhoto = 1,790 vs MTextVideo = 1,012, p < 0.05) and text, photo and links (MTextPhoto = 1,790 vs MTextPhotoLink = 473, p < 0.01). In addition, text and videos generated a higher level of behavioral engagement than text, video and links (MTextVideo = 1,012 vs MTextVideoLink = 245, p < 0.01) and text, photo and links (MTextVideo = 1,012 vs MTextPhotoLink = 473, p < 0.01). These findings demonstrate the effectiveness of photos in generating behavioral engagement, while evincing the dampening effect of links.

Discussion

The purpose of this study was to develop a comprehensive typology of branded content in social media and test its effect on consumer behavioral engagement. The proposed typology is unique in the sense that it accommodates the possibility of individual brand posts communicating multiple message strategies simultaneously. Because brand posts support multiple media, marketers can convey different messages via individual brand posts (Araujo et al., 2015). The proposed typology is built around this concept.

The findings make three substantive contributions to the social media literature. First, the study presented a truly comprehensive typology of branded content in social media. Considering the largely ad hoc manner in which branded content have been categorized in the literature, the typology proposed here provides a theoretically grounded framework to systematically study (i.e. analyze, code and categorize) branded content in social media. Moreover, by incorporating the interactional message strategy, the typology extended the traditional dichotomy between informational and transformational advertising (Puto and Wells, 1984). It thus updates the traditional advertising typology by adding a new message strategy, which reflects the culture of interactivity on social media. This development enhances the applicability of the traditional advertising typologies for the digital era by accounting for the growing phenomena of user interactivity and consumer empowerment on social media and other online environments (Berthon et al., 2012; Gensler et al., 2013).

Second, the findings established the instrumentality of the transformational message strategy in driving consumer behavioral engagement. Because transformational messages possess emotional (e.g. emotional posts), symbolic (e.g. brand resonance and social causes) and hedonic brand cues (e.g. experiential posts), they elicit favorable affective responses, which in turn motivate engagement behavior (Berger and Milkman, 2012). Transformational messages also hold greater potential for consumer transformation. Such content creates a strong emotional connection with consumers and, owing to their symbolic and experiential value, facilitates the incorporation of the brand into consumers’ self-perception and identity (Harmeling et al., 2016). The findings also add to earlier empirical work, which emphasized on emotional cues as an essential precursor to content transmission in online environments (Berger and Milkman, 2012; Yuki, 2015). However, while prior empirical work primarily measured emotional cues, the present study goes a step further by incorporating transformational content, which covers emotional, symbolic and hedonic brand cues.

Finally, the study documented the effect of combined brand posts on consumer behavioral engagement. These posts, which use multiple message strategies simultaneously, are integral to brand communication on social media. Indeed, about half of the brand posts in our sample used multiple message strategies. Despite their prevalence, however, combined brand posts have not been tested in an empirical study. Our findings reveal that informational and interactional message strategies are relatively less effective individually. However, both become impactful when complemented with the transformational message strategy. In other words, non-transformational messages become more effective when complemented with transformational messages, accentuating the complementary effect of the transformational message strategy. This is a critical finding in the sense that the complementary value of transformational messages has not been revealed in prior literature. Araujo et al. (2015) tested the interaction effect of informational and emotional messages on consumer retweeting behavior. However, they did not incorporate interactional messages. By testing the combined effects of three distinct message strategies, the present study offers a more complete picture of how combined brand posts influence consumer behavioral engagement.

Managerial and research implications

The present study offers useful managerial guidance for effective social media communication. First, the study found that the transformational message strategy is superior to both the interactional and the informational message strategy in stimulating consumer behavioral engagement. This underscores the importance of the transformational message strategy in a social media context. Accordingly, marketers should build their creative skills vis-a-vis the transformational message strategy, which primarily includes emotional, experiential and brand resonance posts. By grasping and deploying these brand posts more frequently, marketers can improve their engagement metrics on social media. In this respect, the definition and common themes presented in Table III could serve as a relevant source of managerial insight.

Second, informational and interactional messages, when complemented with transformational messages, generated higher levels of behavioral engagement. Individually, however, both messages are relatively less effective, implying that marketers can drive consumer behavioral engagement by complementing informational and interactional messages with transformational messages. For instance, instead of posting an exclusively functional message, marketers could complement it with brand resonance (e.g. brand image and brand heritage) or with experiential messages (e.g. sensory cues and action-inducing cues). Similarly, instead of posting an exclusively interactional message, marketers could enrich it with emotional messages (e.g. emotive words, emotional stories, jokes and trivia) or with brand resonance (e.g. brand image and brand personality). These types of combined messages enable marketers to appeal to consumers’ multiple motivations for brand engagement on social media. In addition, combined messages support multiple marketing objectives. For instance, while transformational messages help to establish an emotional connection with consumers, interactional messages stimulate conversations among customers, while informational messages spark product interest. By combining multiple messages, therefore, marketers can simultaneously pursue multiple marketing objectives. Accordingly, social media managers should master the art of designing combined brand posts. A useful approach would be to experiment with different combinations of brand messages, measure relevant metrics afterwards and adjust follow-up approaches. One can also learn much by studying competitors’ content strategies.

We conclude the paper by exploring limitations and suggesting some possible avenues for future research. First, we investigated liking and sharing behaviors as important manifestation of consumer behavioral engagement and identified specific message strategies that drive these behaviors. However, social media managers are interested in other forms of social media metrics as well, including brand sentiment, website traffic and sales leads (Smallwood, 2016). Accordingly, we encourage future researchers to link the proposed typology of branded content to these metrics, some of which are publicly unavailable and must be assembled from a firm’s internal social media dashboard. Second, it is worthwhile to test the proposed typology of brand posts on new social media platforms. To the extent that different social media platforms foster distinctive user culture, what succeeds in one platform may not necessarily succeed in another (Cabosky, 2016). Therefore, we call on researchers to extend the proposed typology of branded content to new social media platforms. Finally, our sample is skewed toward large, global brands, whose social media marketing operation is generally well-resourced. Therefore, the findings reported here may not generalize to small- and medium-sized firms. We have already found out that sales promotion, although identified as an important message strategy in the literature, is hardly perceptible on large, global brand pages. Therefore, there is potential for extending the proposed typology by, among others, incorporating sales promotion as a fourth message strategy.

Summary findings of selected studies

Studies Message strategy Consumers’ behavioral engagement Significant findings
De Vries et al. (2012) Entertainment
Informational
Transactional
Number of likes and comments Entertainment was negatively related to number of likes and comments; while information did not affect both
Cvijikj and Michahelles (2013) Entertainment
Information
Remuneration
Number of likes, comments and shares Entertainment generated more likes, comments and shares; information generated more likes and comments; while remuneration generated more comments
Araujo et al. (2015) Brand
Emotional
Informational
Number of retweets Informational content generated more retweets; while emotional and brand content did not
Ashley and Tuten (2015) Resonance, animation, user-image appeal, exclusivity appeal, functional appeal, experiential appeal, emotional appeal, social causes and incentives to share content Klout Score and Engagement Score Resonance, animation, experiential appeals, social causes and incentives were significantly correlated with Klout Score; while experiential appeal and incentives were positively correlated with Engagement Score
Kim et al. (2015) Task-oriented
Interaction-oriented
Self-oriented
Number of likes, comments and shares Task oriented messages generated more likes, shares and comments relative to interaction- and self-oriented messages
Tafesse (2015) Entertainment
Informational
Transactional
Number of likes and shares Entertaining content generated more likes and shares than informational content; while informational content generated more likes and shares than transactional content
Yuki (2015) Social currency
Emotional
Functional (practical usefulness)
Story telling
Number of shares Content that make people “look good” and “feel happy”; and content that are practically useful and tell emotional stories were shared more frequently on Facebook
Taecharungroj (2016) Information-sharing
Emotion-evoking
Action-inducing
Number of retweets and favorites Action-inducing tweets generated more retweets and favorites than emotion-evoking tweets; while emotion-evoking tweets generated more retweets and favorites than information-sharing tweets

Characteristics of sample brand pages

Brands No. of analyzed brand posts (n = 290) Facebook fan-base Industry category
Google 16 18,000,000 Technology
Coca Cola 12 91,000,000 Consumer products
IBM 12 447,000 Technology
Microsoft 16 6,700,000 Technology
GE 23 1,300,000 Technology
Samsung 14 1,200,000 Consumer electronics
Toyota 12 3,4000,000 Automotive
McDonalds 13 57,000,000 Consumer products
Mercedes Benz 14 4,200,000 Automotive
BMW 17 2,100,000 Automotive
Intel 21 25,000,000 Technology
Disney 11 46,000,000 Consumer products
Cisco 17 710,000 Technology
Amazon 10 25,000,000 Retail
Oracle 11 514,000 Technology
HP 11 3,600,000 Consumer electronics
Louis Vuitton 15 17,000,000 Fashion
Honda 14 3,600,000 Automotive
H&M 15 22,000,000 Fashion
American Express 16 5,600,000 Financial

Proposed typology of message strategies

Proposed message strategy Brand post categories Inter-coder reliability (Ir) Definition and common message themes
Informational
(average Ir = 0.87)
Functional posts 0.82 Functional posts highlight the functional attributes of company products and services. These posts typically promote company products and services along dimensions of performance, quality, affordability and style/design
Common themes: product functional claims, product reviews, awards, green credentials, and so forth
Educational posts 0.91 Educational posts seek to educate and inform consumers. These posts help consumers acquire new skills on proper ways of applying company products and services or discover new information about broader industry trends and developments
Common themes: do it yourself tips, instructions, blogposts, external articles and technical interviews with employees
Transformational
(average Ir = 0.84)
Emotional posts 0.79 Emotional posts evoke consumers’ emotions. These posts typically use emotion-laden language, inspiring stories or humor and jokes to arouse affective responses, such as fun, excitement, wonder, and so forth
Common themes: emotionally expressive posts, emotional storytelling, jokes and trivia
Brand resonance posts 0.83 Brand resonance posts direct attention to the core promise and identity of the focal brand. These posts differentiate and favorably position the brand by highlighting elements of its core identity, such as brand image, brand personality, brand association and branded products
Common themes: brand image (i.e. brand logo, brand slogan, brand character, etc.), photos of branded products, celebrity association, and brand heritage
Experiential posts 0.82 Experiential posts evoke consumers’ sensory and behavioral responses. These posts highlight the sensory and embodied qualities of the focal brand, often by associating it with pleasurable consumer experiences
Common themes: sensory stimulation (i.e. visual, auditory, taste, odor, etc.), physical stimulation (i.e. physical actions, performances, activities, etc.), and brand events (product launches, festivals, fan events, sponsored events, etc.)
Social causes 0.91 Cause-related posts highlight socially responsive programs supported by the focal brand. These posts promote worthy social causes and initiatives and encourage customers and fans to support them
Interactional
(average Ir = 0.82)
Current event posts 0.83 Current-event posts comment on themes that capture active talking points among the target audience, such as cultural events, holidays, anniversaries, and the weather/season. These posts initiate timely conversations with consumers using current events
Common themes: cultural events (i.e. sport, film, TV shows), holidays, special days and anniversaries, and the weather
Personal posts 0.83 Personal posts focus on consumers’ personal relationships, preferences and/or experiences. These posts invoke personally meaningful themes to initiate deeply personal conversations with consumers
Common themes: friends, family, personal preferences, anecdotes and future plans
Brand community posts 0.81 Brand community posts promote and reinforce the brand’s online community. These posts foster a sense of community identification and engagement by recruiting new members and eliciting participation from existing members
Common themes: encouraging fans to become members of the brand’s online community, acknowledging fans (e.g. mentioning their name, tagging them), and using/soliciting user-generated content
Customer relation posts 0.81 Customer relation posts solicit information and feedback about customers’ needs, expectations and experiences. These posts seek to deepen the impact of customer relationships by encouraging customer feedback, reviews and testimonies, among others
Common themes: customer feedback, customer testimony and customer reviews and customer services

Frequency statistics

Variables N (%)
Message strategy
Informational 66 23
Transformational 80 28
Interactional 11 4
Informational and transformational 61 21
Informational and interactional 21 7
Interactional and transformational 51 18
Industry
Technology 116 40
Automotive 57 20
Consumer products 36 12
Electronics 25 9
Fashion 30 30
Financial 16 6
Retail 10 3
Facebook fan-base
Under 1 million 40 14
1-5 million 105 36
5-10 million 32 11
10-50 million 88 30
Above 50 million 25 9
Media types
Text and photo 64 22
Text and video 98 34
Text, photo and website links 87 30
Text, video and website links 41 14

ANOVA summary results

df Mean square F P < Back-transformed values Sig. differences at p < 0. 05
Mean CI (95%)
Lower bound Upper bound
Message strategy 284 34.64 12.2 0.01
Informational 242 178 330 Transformational > Informational, Interactional; TransInteract > InfoInteract;
TransInfo > InfoInteract
Transformational 1,300 880 1,900
Interactional 321 82 1,249
Informational and transformational 973 590 1,620
Informational and interactional 247 154 399
Interactional and transformational 1,556 916 2,644
Industry 283 46.3 18.68 0.01
Technology 433 327 572 CP > Auto, Tech, Electronics, Financial, Retail
Auto > Tech, Electronics, Financial, Retail;
Fashion > Auto, Tech, Electronics, Financial, Retail;
Tech > Financial
Automotive 1,075 685 1,686
Consumer products 4,230 2,392 7,480
Electronics 237 159 354
Fashion 2,416 1,064 5,486
Financial 134 83 215
Retail 340 221 523
Facebook fan-base 285 61.6 23.93 0.01
Under 1 million 191 129 279 Above 50 million > Under 1 million, 1-5 million, 5-10 million, 10-50 million; 10-50 million > Under 1 million, 1-5 million, 5-10 million; 1-5 million > Under 1 million
1-5 million 523 380 721
5-10 million 284 194 416
10-50 million 1,720 1,188 2,490
Above 50 million 3,828 1,720 8,604
Media types 286 42.61 14.34 0.01
Text and photo 1,790 1,153 2,779 TextPhoto >TextVideo, TextPhotoLink, TextVideoLink;
TextVideo > TextPhotoLink, TextVideoLink;
TextPhotoLink > TextVideoLink
Text and video 1,012 692 1,466
Text, photo and website links 473 324 685
Text, video and website links 245 174 347

Notes

1

Although sales promotion (e.g. direct sales calls, price discounts AND competitions) was identified as a major message strategy in the literature, it was omitted from the final model because of insufficient observations. As it turned out, global brands do not post overtly promotional messages on their brand pages. However, in more localized brand pages, sales promotion might represent a more integral strategy and as such may need to be considered as a fourth message strategy.

2

Perreault and Leigh’s Ir, which is a more conservative inter-coder reliability coefficient than per cent agreement, is obtained using the following formula: Ir = {[(Fo/N) − (1/K)][k/(k − 1)]}0.5, for Fo/N > 1/K, where Fo = observed frequency of agreement, N = total number of judgments and K = number of coding categories.

References

Alexandrov, A., Lilly, B. and Babakus, E. (2013), “The effects of social- and self- motives on the intentions to share positive and negative word of mouth”, Journal of the Academy of Marketing Science, Vol. 41 No. 5, pp. 531-546.

Ashley, K. and Tuten, T. (2015), “Creative strategies in social media marketing: an exploratory study of branded social content and consumer engagement”, Psychology & Marketing, Vol. 32 No. 1, pp. 15-27.

Araujo, T., Neijens, P. and Vliegenthart, R. (2015), “What motivates consumers to re-tweet Brand content? The impact of information, emotion, and traceability on pass-along behavior”, Journal of Advertising Research, Vol. 55 No. 3, pp. 284-295.

Berger, J. (2014), “Word of mouth and interpersonal communication: a review and directions for future research”, Journal of Consumer Psychology, Vol. 24 No. 4, pp. 586-607.

Berger, J. and Milkman, K.L. (2012), “What makes online content viral”, Journal of Marketing Research, Vol. 49 No. 2, pp. 192-205.

Berthon, P.R., Pitt, L.F., Plangger, K. and Shapiro, D. (2012), “Marketing meets web 2.0, social media, and creative consumers: implications for international marketing strategy”, Business Horizons, Vol. 55 No. 3, pp. 261-271.

Beukeboom, C.J., Kerkhof, P. and De Vries, M. (2015), “Does a virtual like cause actual liking? How following a brand’s Facebook updates enhances Brand evaluations and purchase intention”, Journal of Interactive Marketing, Vol. 32, pp. 26-36.

Cvijikj, I.P. and Michahelles, F. (2013), “Online engagement factors on Facebook Brand pages”, Social Network Analysis and Mining, Vol. 3 No. 4, pp. 843-861.

Content Marketing Institute (CMI) (2015), B2B Content Marketing: 2015 Benchmarks, Budgets, and Trends – North America, Content Marketing Institute, available at: http://contentmarketinginstitute.com/wp-content/uploads/2014/10/2015_B2B_Research.pdf (accessed 27 June 2016).

De Vries, L., Gensler, S. and Leeflang, P.S.H. (2012), “Popularity of brand posts on brand fan pages: an investigation of the effects of social media marketing”, Journal of Interactive Marketing, Vol. 26 No. 2, pp. 83-91.

Gensler, S., Volckner, F., Liu-Thompkins, Y. and Wiertz, C. (2013), “Managing brands in social media environment”, Journal of Interactive Marketing, Vol. 27 No. 4, pp. 242-256.

Golan, G.J. and Zaidner, L. (2008), “Creative strategies in viral advertising: an application of Taylor’s six-segment message strategy wheel”, Journal of Computer-Mediated Communication, Vol. 13 No. 4, pp. 959-972.

Gummerus, J., Liljander, V., Weman, E. and Pihlstrom, M. (2012), “Customer engagement in a Facebook brand community”, Management Research Review, Vol. 35 No. 9, pp. 857-877.

Hamilton, M., Kaltcheva, V.D. and Rohm, A.J. (2016), “Hashtags and handshakes: consumer motives and platform use in brand-consumer interactions”, Journal of Consumer Marketing, Vol. 33 No. 2, pp. 135-144.

Harmeling, C.M., Moffett, J.W., Arnold, M.J. and Carlson, B.D. (2016), “Toward a theory of customer engagement marketing”, Journal of the Academy of Marketing Science, Vol. 45 No. 3, pp. 312-335.

Hollebeek, L.D., Glynn, M.S. and Brodie, R.J. (2014), “Consumer Brand engagement in social media: conceptualization, scale development and validation”, Journal of Interactive Marketing, Vol. 28 No. 2, pp. 149-165.

Hubspot (2016), “Why don’t my Facebook fans see my posts? The decline of organic Facebook reach”, available at: http://blog.hubspot.com/marketing/facebook-declining-organic-reach#sm.00000aduw8rtidw4pr91pgent83hf (accessed 8 November 2016).

Hsieh, H. and Shannon, S.E. (2005), “Three approaches to qualitative content analysis”, Qualitative Health Research, Vol. 15 No. 9, pp. 1277-1288.

Jaakkola, E. and Alexander, M. (2014), “The role of customer engagement behavior in value co-creation: a service system perspective”, Journal of Service Research, Vol. 17 No. 3, pp. 247 -261.

Jahn, B. and Kunz, W. (2012), “How to transform consumers into fans of your brand”, Journal of Service Management, Vol. 23 No. 3, pp. 344-361.

Kabadayi, S. and Price, K. (2014), “Consumer-Brand engagement on Facebook: liking and commenting behaviors”, Journal of Research in Interactive Marketing, Vol. 8 No. 3, pp. 203-223.

Kim, D., Spiller, L. and Hettche, M. (2015), “Analyzing media types and content orientations in Facebook for global brands”, Journal of Research in Interactive Marketing, Vol. 9 No. 1, pp. 4-30.

Kim, E., Sung, Y. and Kang, H. (2014), “Brand followers’ Retweeting behavior on twitter: how brand relationships influence brand electronic word of mouth”, Computers in Human Behavior, Vol. 37 No. 8, pp. 18-25.

Kolbe, R.H. and Burnett, M.S. (1991), “Content-analysis research: an examination of applications with directives for improving research reliability and objectivity”, Journal of Consumer Research, Vol. 18 No. 2, pp. 243-250.

Laskey, H.A., Day, E. and Crask, M.R. (1989), “Typology of main message strategies for television commercials”, Journal of Advertising, Vol. 18 No. 1, pp. 36-41.

Lipsman, A., Mudd, G., Rich, M. and Bruich, S. (2012), “The power of like: how brands reach (and influence) fans through social-media marketing”, Journal of Advertising Research, Vol. 52 No. 1, pp. 40-52.

Lovett, M.J., Peres, R. and Shachar, R. (2013), “On brands and word of mouth”, Journal of Marketing Research, Vol. 50 No. 4, pp. 427-444.

Muntinga, D.G., Moorman, M. and Smit, E.G. (2011), “Introducing COBRAs: exploring motivations for Brand-related social media use”, International Journal of Advertising, Vol. 30 No. 1, pp. 13-46.

Pereira, H.G., Fátima Salgueiro, M. and Mateus, I. (2014), “Say yes to Facebook and get your customers involved! Relationships in a world of social networks”, Business Horizons, Vol. 57 No. 6, pp. 695-702.

Perreault, W.D. and Leigh, L.E. (1989), “Reliability of nominal data based on qualitative judgements”, Journal of Marketing Research, Vol. 26 No. 2, pp. 135-148.

Puto, C.P. and Wells, W.D. (1984), “Informational and transformational advertising: the differential effects of time”, Advances in Consumer Research, Vol. 11, pp. 638-643.

Smallwood, B. (2016), “Resisting the siren call of popular digital media measures: Facebook research shows no link between trends online metrics and ad effectiveness”, Journal of Advertising Research, Vol. 56 No. 2, pp. 126-131.

Stephen, A.T., Dover, Y. and Goldenberg, J. (2010), “A comparison of the effects of transmitter activity and connectivity on the diffusion of information over online social networks”, INSEAD Working Paper No. 2010/35/MKT, available at: http://ssrn.com/abstract=1609611

Swani, K., Brown, B.P. and Milne, G.R. (2016), “Should tweets differ from B2B and B2C? An analysis of fortune 500 companies’ twitter communications”, Industrial Marketing Management, Vol. 43 No. 5, pp. 873-881.

Taecharungroj, V. (2016), “Starbucks’ marketing communications strategy on twitter”, Journal of Marketing Communications, Vol. 23 No. 6, pp. 1-20.

Tafesse, W. (2015), “Content strategies and audience response on facebook Brand pages”, Marketing Intelligence & Planning, Vol. 33 No. 6, pp. 927-943.

Tafesse, W. (2016), “An experiential model of consumer engagement in social media”, Journal of Product & Brand Management, Vol. 25 No. 5, pp. 424-434.

Tafesse, W. and Wien, A. (2017), “A framework for categorizing social media posts”, Cogent Business & Management, Vol. 4 No. 1, pp. 1-22.

Taylor, R.E. (1999), “A six-segment message strategy wheel”, Journal of Advertising Research, Vol. 39 No. 1, pp. 7-17.

Trackmaven (2016), “The content marketing paradox revisited: time for a reboot?”, available at: http://trackmaven.com/resources/content-marketing-paradox-revisited/ (accessed 8 November 2016).

Van Droom, J., Lemon, K.N., Mittal, V., Nass, S., Pick, D., Pirner, P. and Verhoef, P.C. (2010), “Customer engagement behavior: theoretical foundations and research directions”, Journal of Service Research, Vol. 13 No. 3, pp. 253-266.

Vivek, S.D., Beatty, S.E. and Morgan, R.M. (2012), “Customer engagement: exploring customer relationships beyond purchase”, Journal of Marketing Theory and Practice, Vol. 20 No. 2, pp. 122-146.

Wallace, E., Buil, I. and Chernatony, L.D. (2014), “Consumer engagement with self-expressive brands: brand love and WOM outcomes”, Journal of Product & Brand Management, Vol. 23 No. 1, pp. 32-42.

Yuki, T. (2015), “What makes brands’ social content shareable on Facebook? An analysis that demonstrates the power of online trust and attention”, Journal of Advertising Research, Vol. 55 No. 4, pp. 458-470.

Zhang, Y. and Wildemuth, B. (2009), “Qualitative analysis of content”, in Wildemuth, B.. (Ed.), Applications of Social Research Methods to Questions in Information and Library Science, Libraries Unlimited, Westport, CT, pp. 308-319.

Further reading

Brodie, R.J., Hollebeek, L., Juric, B. and Ilic, A. (2013a), “Customer engagement: Conceptual domain, fundamental propositions, and implications for future research”, Journal of Service Research, Vol. 14 No. 3, pp. 252-271.

Brodie, R.J., Ilic, A., Juric, B. and Hollebeek, L. (2013b), “Consumer engagement in a virtual Brand community: An exploratory study”, Journal of Business Research, Vol. 66 No. 1, pp. 105-114.

Davis, R., Piven, I. and Breazeale, M. (2014), “Conceptualizing the Brand in social media community: The five sources model”, Journal of Retailing and Consumer Services, Vol. 21 No. 4, pp. 468-481.

Kim, J., McMillan, S.J. and Hwang, J. (2005), “Strategies for the super bowl of advertising: An analysis of how the web is integrated into campaigns”, Journal of Interactive Advertising, Vol. 6 No. 1, pp. 46-60.

Quan-Haase, A. and Young, A.L. (2010), “Uses and gratification of social media: A comparison of facebook and instant messaging”, Bulletin of Science Technology and Society, Vol. 30 No. 5, pp. 350-361.

Yuksel, M., Milne, G.R. and Miller, E.G. (2016), “Social media as complementary consumption: The relationship between consumer empowerment and social interactions in experiential and informative contexts”, Journal of Consumer Marketing, Vol. 33 No. 2, pp. 111-123.

Supplementary materials

JCM_35_3.pdf (12.2 MB)

Corresponding author

Wondwesen Tafesse can be contacted at: wondwesen.tafesse@uit.no