Firms seek to interact with customers in various ways, beyond simple transactions (Pansari and Kumar 2018), and thus exhibit a “deliberate effort to motivate, empower, and measure a customer’s voluntary contribution to its marketing functions, beyond a core, economic transaction” (Harmeling et al., 2017, p. 312). The strategies deployed to stimulate such customer engagement are varied and creative, such as when Lay’s conducts idea contests and asks customers to propose new chip flavors or when Sprite hosted multisensory, live concerts on a New York corner. These strategies also take place on various platforms (e.g., digital vs. physical); Anheuser-Busch spreads its $200 billion annual investment in customer engagement marketing across multiple platforms (Harmeling et al., 2017).

Ultimately though, to be effective, customer engagement (CE) must enhance marketing outcomes such as purchase frequency (ScottGould 2022), an outcome that some observers question (Beckers et al., 2018). Despite the need to clarify which strategies improve customer engagement and when it evokes positive marketing outcomes, previously proposed frameworks pertaining to the antecedents or effectiveness of engagement strategies (e.g., van Doorn et al., 2010; Vivek et al., 2018) tend to be conceptual. Although Harmeling et al. (2017) also identify two primary engagement strategies (task-based and experiential), they do not address different platforms.

More broadly, we find few studies that test for the influences of distinct platform characteristics on the effectiveness of engagement strategies or their marketing outcomes. Whereas Santini et al. (2020) compare the effects on three digital platforms (blogs, Facebook, Twitter), they do not include engagement strategies or platform characteristics,Footnote 1 so one cannot establish whether the effectiveness of engagement strategies depends on the platform and/or which platform characteristics encourage positive marketing outcomes.

Thus, we still lack a unifying framework that provides clear insights into how firms can optimize their investments in engagement strategies. In response, we undertake a meta-analysis of empirical research that links different strategies with CE and integrate insights from both CE marketing and platform theories. In line with an emerging theory of CE marketing (Harmeling et al., 2017), we distinguish two main strategies: task-based engagement marketing initiatives that call for customers’ participation in structured, often incentivized tasks to prompt their voluntary contributions (e.g., Lay’s chips idea contests; Harmeling et al., 2017) and experiential engagement marketing initiatives that represent attempts to provide pleasurable experiences for customers and thereby motivate their voluntary contributions (e.g., Sprite’s corner concerts; Harmeling et al., 2017). In accordance with platform theory, we also outline the contexts in which CE marketing takes place. Some platforms facilitate interactions with customers better than others (Wichmann et al., 2022); to explain why, we consider factors such as the intensity of interactions that the platforms support (continuous vs. spot), their richness (rich vs. lean), their status (digital vs. physical), and whether the platforms allow the firm or customer to initiate interactions. Table 1 provides examples of typical CE marketing activities across different platforms.

Table 1 Examples of CE marketing actions on different engagement platforms
Full size table

In turn, we can determine that the two strategies mainly affect three CE dimensions, which resonates with the extant conceptualization of CE as a multidimensional construct. Specifically, customer engagement encompasses customers’ volitional investments of their cognitive (e.g., knowledge), emotional (e.g., brand enthusiasm), and behavioral (e.g., skills) resources in interactions with some engagement object (e.g., firm, brand). As Hollebeek et al. (2019, p. 174) explain, because engaged customers tend to “invest more resources in brand interactions than their less engaged counterparts,” they also typically provide deeper and broader contributions to the relationship. As we show, these engaged customers usually invest cognitive resources first, by thinking about and attending to the firm and its brands (Dessart et al. 2016). Then they invest emotional resources, get excited about the firm and its brands, and derive pleasure from the interaction (Hollebeek et al. 2014). Finally, they invest behavioral resources, including referrals and product development ideas.

In addition to the two engagement strategies (task-based and experiential), our meta-analytic framework accounts for two traditional marketing strategies (product performance and brand associations) (Harmeling et al., 2017), which enables us to conduct a comparative assessment of the effectiveness of engagement marketing versus traditional marketing. With our multivariate conceptualization of CE, we also clarify how the strategies influence each of the three CE dimensions, to establish a comprehensive assessment of their effectiveness. With this assessment, we investigate when firms benefit most from engaging customers, such that the CE they evoke translates into marketing outcomes. Finally, we account for platform characteristics, as potential moderators of the relationships of the different strategies with CE dimensions and then with marketing outcomes.

To validate this proposed framework, we employ a meta-analysis of 5,005 correlations reported by 434,233 customers in 395 samples. Meta-analyses can combine and compare results across studies, identify boundaries, and suggest directions for further research (Grewal et al., 2018). Accordingly, we use the vast meta-analytic database we collected to clarify which engagement initiatives are more effective in driving CE and in what conditions. The results suggest that, on average, task-based initiatives are more effective than experiential initiatives, with stronger effects on behavioral and emotional dimensions. Both CE-specific marketing and traditional marketing can stimulate CE, though differences arise with the different CE dimensions. In addition, we specify the increased relevance of task-based initiatives on platforms that support continuous interactions but the dominant influence of experiential initiatives on platforms that support spot interactions. Furthermore, task-based initiatives are more effective on platforms that enable lean rather than rich interactions. In contrast, we find no differences for digital versus physical platforms or for firm- versus customer-initiated interactions, so both strategies appear equally effective in these settings.

Turning to the outcomes of firms’ efforts to engage customers, our analyses suggest that all three CE dimensions relate to behavioral outcomes and intentions, but emotional and cognitive CE exert indirect effects through behavioral CE. Overall, firms benefit from engaging customers, but the positive results depend on the platform used. In detail, behavioral and emotional CE have weaker effects on behavioral intentions on digital (cf. physical) platforms but stronger effects for customer- (cf. firm-) initiated interactions. We find weaker effects of emotional CE on behavioral outcomes for continuous (cf. spot) interactions; the results for rich versus lean interactions vary by the marketing outcome being measured. Finally, cognitive CE exhibits similar effectiveness across all the different platform characteristics. In addition to advancing CE marketing theory and demonstrating the usefulness of integrating platform theory, our findings thus give managers insights into how to leverage different CE strategies and platforms to attain improved marketing outcomes.

Conceptual background

Multidimensional conceptualization of CE

Engagement implies a subject who is engaged and an object with which this subject engages, as initially presented in organizational behavior research pertaining to employee engagement (Kahn, 1990) and informed by social identity theory (Ashforth & Mael, 1989). In marketing contexts, the engaged subject refers to existing or prospective customers, and the engagement object might be a brand or firm (Hollebeek & Macky, 2019). Marketing scholars also apply various labels to engagement (e.g., customer brand engagement, digital customer engagement; van Doorn et al., 2010). In turn, different conceptualizations of CE are available (Clark et al., 2020; Web Appendix B). In general, this variety can be summarized in two schools of thought: a behavioral conceptualization that focuses on behavioral CE exclusively (Kumar et al., 2019) or a multidimensional conceptualization that spans behavioral and psychological CE components (Clark et al., 2020). But even within these schools of thought, authors emphasize different substantive content (e.g., which dimensions comprise CE) and structures (e.g., relationships among CE dimensions).

That is, behavioral conceptualizations tend to suggest that recommendation, influence, and feedback behaviors drive firm-related outcomes and performance (Pansari & Kumar, 2017; van Doorn et al., 2010). However, scholars who adopt a value-based perspective broaden this scope to include transactional (e.g., customer purchases) in addition to nontransactional (e.g., word-of-mouth) contributions (Beckers et al., 2018). In line with the narrow conceptualization, we argue that only behaviors that go beyond transactions represent CE (van Doorn et al., 2010), so the goal should be to stimulate nontransactional, behavioral CE. Following this reasoning, in considering behavioral CE, we focus on nontransactional behaviors; transactional behaviors represent outcomes.

The multidimensional view of CE instead has been informed by organizational research (Rich et al., 2010), in which employees’ work engagement comprises cognitive, emotional, and behavioral CE (Hollebeek et al., 2019). As we noted previously, in consumer settings, CE refers to investments of resources, such as cognitive (e.g., knowledge), emotional (e.g., enthusiasm), and behavioral (e.g., skills) resources in specific interactions with a firm or brand. Engaged customers invest cognitive resources first, followed by emotional and behavioral investments (Hollebeek et al., 2014; Oliver, 1999). Attitudinal theories also provide relevant predictions of how cognitive and emotional constructs can influence behavior (Fishbein & Ajzen, 1975). Thus, we anticipate that cognitive CE relates to emotional CE, which influence behavioral CE.

CE marketing

Prior studies have traced the evolution of marketing, from transactional to relationship to CE marketing (Harmeling et al., 2017; Verhoef et al., 2010). That is, initial firm relationships with customers prioritized transactions (e.g., purchases), but firms’ focus on such transactional value creation shifted, prompting efforts that seek to establish and maintain customer relationships (Palmatier & Steinhoff, 2019; Pansari & Kumar, 2018). Customers appreciate connections with firms, which can occur through various platforms, as well as interacting with other customers (Pansari and Kumar 2018). Beckers et al. (2018) predict deeper relationships with engaged customers, who interact with the former at various points, beyond the point of purchase. During such interactions, customers form opinions about and become attached to the firm and its brands (Fuchs et al., 2010). Some engagement studies refer to the service-dominant (S-D) logic (Vargo & Lusch, 2004); this stream of literature emphasizes value co-creation and customers’ nontransactional contributions (Brodie et al., 2011; Payne et al., 2008). Even as the interrelationship of CE and the S-D logic has been established (Brodie et al., 2011), only recently have scholars begun developing integrated, conceptual frameworks (Hollebeek et al., 2019).

Regarding the antecedents of CE, van Doorn et al. (2010) propose three categories: customer (e.g., identity, consumption goals), firm (e.g., brand characteristics, firm reputation), and context (e.g., competitive factors). Harmeling et al. (2017) focus on firm strategies and consolidate insights from different literature streams to define and classify CE-specific marketing strategies, based on whether the strategies (1) deliberately motivate customers to invest resources and engage with the firm (i.e., CE marketing) or (2) organically evoke CE, without deliberate effort by the firm (i.e., traditional marketing strategies). In addition, they clarify the two types of engagement strategies that we cited previously. Due to their promising outcomes, task-based initiatives were the focus of many early studies. They assign structured tasks to customers (e.g., write a review, refer a friend), which customers must invest mental and physical effort to complete. In Fuchs and Schreier’s (2011) study of how task-based initiatives influence participation in new product development, they identify customers’ sense of psychological ownership, stemming from the effort they invest, as a source of more favorable evaluations of the firm (see also Van Dyne & Pierce, 2004). In contrast, experiential initiatives often feature playful events, rather than work-like tasks, that can induce a sense of self-transformation and encourage customers to incorporate the brand into their self-concept (Markus & Kunda, 1986). These initiatives strengthen psychological and emotional connections. For example, Cova and Pace (2006) show how Ferrero’s experiential events, such as Nutella Parties, prompted customers’ identification with the “my Nutella Community.” However, relatively fewer studies have examined this type of strategy.

We also acknowledge that traditional marketing strategies might organically stimulate Harmeling et al. (2017) stress the importance of product performance and brand associations, with the prediction that when customers have excellent experiences with an offering’s performance or the brand, they want to support the firm (e.g., through word-of-mouth), beyond purchasing (Verleye et al., 2014; Wallace et al., 2014).

Thus, in our proposed framework, we include task-based and experiential engagement strategies, together with traditional marketing strategies, so that we can compare their effectiveness. In some ways, our framework follows Harmeling et al.’s (2017), but we note several key differences. Their framework features behavioral CE only, it ignores platform differences, and excludes marketing outcomes. Moreover, we analyze the influence of a larger set of antecedents (Web Appendix C). In line with suggestions that CE can influence marketing outcomes, including firm performance (Kumar & Pansari, 2016) and customers’ behavioral intentions (Santini et al., 2020), we include these considerations in our framework.

Platform theory

Platforms enable and facilitate interactions between two or more parties, such as customers and firms (Rangaswamy et al., 2020; Wichmann et al., 2022). They function to sell goods and services, support socializing, and exchange information (Bonina et al., 2021). Platform theory distinguishes physical and digital platforms (Breidbach & Brodie, 2016; Wirtz et al. 2019). The Consumer Electronic Show is a physical platform that allows exhibitors and potential customers to interact directly, whereas Amazon or eBay provide digital platforms that facilitate online seller–buyer interactions (Breidbach & Brodie, 2016). Wirtz et al. (2019) also differentiate types of digital platforms, such as search (e.g., Bing), communication (e.g., WhatsApp), social media (e.g., Facebook), and matching (e.g., TaskRabbit) platforms. In their literature review, Rangaswamy et al. (2020) stress that despite the many studies of platforms in general, few of them elucidate the role of marketing (e.g., Bhargava & Rubel, 2019; Lamberton & Rose, 2012; Rosario et al., 2016). Thus, we need more research that takes a marketing perspective on platforms and tests their characteristics.

In customer engagement literature, studies often refer to “engagement platforms” that enable customer–firm or customer–customer interactions (Blasco-Arcas et al., 2016; Breidbach & Brodie, 2016). Digital engagement platforms tend to include review sections, brand development co-creation spaces, and social network elements (Blasco-Arcas et al., 2016). They allow customers to engage with a focal object (e.g., brand) during the purchase process, so firms in turn can actively drive customer engagement through their CE marketing efforts, such as encouraging customers to create content and exchange with others (Wichmann et al., 2022). Yet we find few studies examining the influence of platform characteristics on the effectiveness of engagement strategies, possibly because most research in this domain features a single sample on one platform and thus cannot undertake a comparative assessment. Santini et al. (2020) compare CE effects across blogs, Facebook, and Twitter, but they do not address engagement strategies or platform characteristics.

Finally, platform theory also suggests that some platforms are better suited to facilitate interactions than others. Specifically, prior literature identifies four main platform characteristics, which we accordingly include as moderators in our framework. That is, firms might leverage platforms with varying interaction intensity (continuous vs. spot) and richness (rich vs. lean) (Kaplan & Haenlein, 2010; Sawhney et al., 2005). They might also use digital or physical platforms to access customers, and they can consider whether the firm or customers initiate interactions the platforms they use (Beckers et al., 2018; Meire et al., 2019).

Meta-analytic framework

Like Blut et al. (2021), we choose not to derive formal, main effects hypotheses; instead, we present a summary and outline how our findings help resolve discrepancies in prior research (e.g., differential effects of strategies on CE). We briefly explain the underlying mechanisms of different CE strategies. Because of their novelty though, we derive hypotheses for the moderating effects of platforms characteristics (see Fig. 1).

Fig. 1
figure 1

Meta-analytic framework

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Antecedents: CE marketing initiatives

The influences of task-based versus experiential initiatives on CE likely move through two distinct mechanisms: psychological ownership and self-transformation.Footnote 2 First, because task-based initiatives require customers to invest some mental effort to perform a specific task (e.g., idea competition), they may induce a sense of psychological ownership (Harmeling et al., 2017), defined as “possessive feelings that some [engagement] object is ‘MINE’” (Van Dyne & Pierce, 2004, p. 440). This sense of ownership also evokes a feeling of responsibility toward the engagement object (Morewedge et al., 2021). Therefore, customers feel motivated to live up to their responsibility by supporting the firm, that is, by investing their cognitive, emotional, and behavioral resources in interactions with it.

Second, experiential initiatives, which tend to be multisensory, highly participatory, and shared (Harmeling et al., 2017), can lead to self-transformation and shifted customer beliefs and attitudes (Schouten et al., 2007), such that customers incorporate the brand that has provided the experience into their self-concept (Markus & Kunda, 1986). Once a firm or brand is incorporated into customers’ self-perceptions, they likely support it by investing cognitive, emotional, and behavioral resources in interactions (Harmeling et al., 2017).

Prior research indicates that it is easier to induce psychological ownership feelings through task-based initiatives than self-transformation through experiential initiatives (Harmeling et al., 2017). Self-transformation processes are complex and difficult to trigger because a customer’s self is relatively stable and resistant to change (Markus & Kunda, 1986). Accordingly, we anticipate that task-based initiatives have stronger effects on all three CE dimensions than experiential initiatives do.

Antecedents: Traditional marketing

Product performance (i.e., product-related experiences with the core offering) and brand associations (i.e., brand-related experiences with the core offering) can stimulate CE. Perceptions of the firm’s core offering, developed over time, often get stored in customers’ minds (Anderson & Bower, 2014; Harmeling et al., 2017; Keller, 1993), and when these positive experiences are recalled, customers are more likely to engage with the firm. Such product and brand knowledge may trigger cognitions, emotions, and behaviors (Anderson & Bower, 2014), signaling their positive links to the three dimensions of CE (Dessart et al., 2016). That is, due to their past positive experiences, customers should be motivated to invest cognitive, emotional, and behavioral resources into interactions with the firm or brand.

Finally, we predict differential effectiveness of CE versus traditional marketing. If customers can identify an underlying motive for firms’ strategies, they find the marketing efforts less credible and convincing (Roehm & Brady, 2007). The CE initiatives deliberately exist to push customers’ resource contributions, but traditional marketing strategies prioritize positive product and brand experiences, with engagement as an organic outcome (Harmeling et al., 2017). Thus, traditional strategies should be more credible and effective for driving CE.

Consequences of CE

The separate CE dimensions likely evoke different marketing outcomes, which might include behavioral intentions toward a firm (e.g., intention to repurchase; van Doorn et al., 2010) or behavioral outcomes (e.g., actual repurchases; Beckers et al., 2018). Engaged customers invest their cognitive, emotional, and behavioral resources into specific interactions and perceive their efforts to support the firm as rewarding, such that they should be eager to remain in a relationship with the firm. We also expect dependencies between intentions and behavior, as suggested by attitudinal theories (Fishbein & Ajzen, 1975) and prior engagement literature (Hollebeek et al., 2014).

Platform characteristics as moderators

Platform characteristics arguably might inform both the formation of CE and its translation into marketing outcomes. We propose that all platform characteristics likely moderate the translation of CE into marketing outcomes, but only two of them matter for the formation of CE. Furthermore, though the effectiveness of task-based initiatives depends on customers’ understanding of the assigned task, experiential initiatives are more effective when customers experience unusual, emotionally intense, interactive events (Harmeling et al., 2017). As we discuss next, prior literature implies that the intensity and richness of platform interactions moderate the effects of CE initiatives (Kaplan & Haenlein, 2010).

Intensity of platform interaction

Continuous interactions differ from spot interactions in their frequency and repetitiveness (Sawhney et al., 2005). Regarding this platform characteristic, we posit that task-based initiatives exert stronger effects on CE dimensions in continuous than in spot interactions, whereas experiential initiatives may have weaker effects. For example, customers can interact continuously with Polaroid on its brand-owned platform, such as by uploading pictures, sharing content, and referring friends (www.polaroid.com). Through these ongoing interactions, customers gain a better understanding of any assigned tasks and learn more, which—according to CE marketing theory (Harmeling et al., 2017)—should induce stronger feelings of ownership. But when Absolut Vodka introduced “out-of-this-world” experiential initiatives, it sought to evoke immediate emotional and psychological reactions to each event. These transformative experiences likely increased customers’ sense of emotional intensity, which fuel self-evaluative cognitive processes (Harmeling et al., 2015). If they were to repeat such spectacular interactions frequently though, it would become more difficult to induce emotions due to wear-out effects (Sharot et al., 2004). It is difficult to surprise and excite customers on an ongoing basis (Maguire et al., 2011). Thus, experiential initiatives likely require spot interactions to drive CE, whereas task-based initiatives benefit from continuous interactions.

H1

The positive relationships of task-based initiatives with CE dimensions are stronger for continuous than for spot interactions.

H2

The positive relationships of experiential initiatives with CE dimensions are weaker for continuous than for spot interactions.

In turn, we predict stronger relationships between CE dimensions and marketing outcomes in spot compared with continuous interactions. Santini et al. (2020) explain that the strength of CE effects on marketing outcomes depends on the personal relevance of the engagement object to customers. When personal relevance is greater, customers likely consider their investments in interacting with the engagement object when they reflect on their future behavioral intentions or behaviors (Santini et al., 2020). The engagement object is more salient in spot than in continuous interactions. That is, as Johnston and Lane (2021, p. 2) explain, spot interactions occur “over short periods of time when an organization seeks to affect or connect with its stakeholders around a focal topic (e.g., issue or decision).” Because these spot interactions limited in time and specific to some particular issue or topic, they are more instrumental too (Johnston & Lane, 2018). For example, the Ice Bucket Challenge created vast awareness of the need for more research into ALS through intensive communication on social media within just three months. Customers likely reflect on the importance of this engagement object (Santini et al., 2020), so the effects of CE dimensions on marketing outcomes should be stronger for spot than for continuous interactions.

H3

The positive relationships of CE dimensions with (a) behavioral intentions and (b) behavioral outcomes are weaker for continuous than for spot interactions.

Richness of interaction

The platform interaction richness aligns with the broader concept of media richness, which establishes that “media differ in the degree of richness they possess—that is, the amount of information they allow to be transmitted in a given time interval” (Kaplan & Haenlein, 2010, p. 61). Rich platform interactions differ from lean ones in the number and diversity of informational cues they transmit (e.g., videos vs. images vs. text; Cao et al., 2021; Dennis & Kinney, 1998). Whereas wikis and Twitter are relatively lean engagement platforms, social games and social worlds (e.g., Facebook’s metaverse) represent rich engagement platforms (Kaplan & Haenlein, 2010). Media richness theory acknowledges the importance of considering the capacity of different communication media to process “rich” information but does not offer predictions about the effects of these differences (Dennis & Kinney, 1998). Thus, we propose two rival hypotheses.

First, rich media might enhance communication effectiveness, because the greater amount and diversity of information transmitted improve understanding of the message (Vickery et al., 2004). Therefore, rich media should generally outperform lean media in terms of communication effectiveness. With respect to CE initiatives, both task-based and experiential initiatives could have stronger effects in rich interactions, in which companies communicate diverse information cues that help customers understand task-based initiatives, and if customers understand their assigned task, they likely develop feelings of ownership (Harmeling et al., 2017). Moreover, rich interactions may help customers incorporate the firm into their self-perceptions in response to experiential initiatives (Kaplan & Haenlein, 2010). Experiential events gain transformational capacities when they are unusual, emotionally intense, and interactive (Harmeling et al., 2017). Rich interactions offer more opportunities to communicate what makes an event unusual, emotionally intense, and interactive.

Second, rich media are not always better than lean media (Dennis & Kinney, 1998). According to the task–media fit hypothesis, unstructured tasks require rich media that can carry different types of messages, but structured tasks require lean media (Shirani et al., 1999). Task performance improves when there is a “match between information requirements of the task and a medium’s ability to convey information richness” (Suh, 1999, p. 297). If a medium is too rich for the task, it may distract customers and lead to inefficiency, because some of the rich information provided is not essential for effective communication (Suh, 1999). With respect to CE, task-based initiatives involve requests for structured tasks (e.g., retweet a hashtag) that guide customers’ voluntary resource contributions (Harmeling et al., 2017). To send such specific instructions, leaner media may be better suited. McGrath and Hollingshead (1993) stress, in their task–media fit framework, that leaner media (e.g., text-based) facilitate structured tasks, such as generating new product ideas and plans, better than richer media (e.g., virtual world). If customers get distracted by rich information that hinders their understanding of the assigned task, they are less likely to develop feelings of ownership. Thus, we acknowledge the argument that task-based initiatives may be less effective in rich interactions. We do not propose a rival hypothesis for experiential initiatives. According to Harmeling et al. (2017), experiential initiatives resemble unstructured play, and thus richer media still would be better suited according to the task–media fit hypothesis.

H4

The positive relationships of (a) task-based initiatives and (b) experiential-based initiatives with CE dimensions are stronger for rich than for lean interactions.

H5

The positive relationships of task-based initiatives with CE dimensions are weaker for rich than for lean interactions.

Finally, the richness of the platform interaction should influence the translation of CE into marketing outcomes, because rich interactions might disrupt the effects of CE dimensions on marketing outcomes. The diverse informational cues they transmit tend to increase the complexity of customers’ decision-making (Isen, 2001; Malhotra, 1984). In such a situation, customer’s own sense of engagement, becomes less important.

H6

The positive relationships of CE dimensions with (a) behavioral intentions and (b) behavioral outcomes are weaker for rich than for lean interactions.

Digital versus physical platform

Platform theory suggests that digital platforms (e.g., social networking sites) differ from physical platforms (e.g., outdoor events). Lieberman and Schroeder (2020) explain that, relative to offline interactions, online interactions provide more opportunities to form new social ties, disseminate information widely, and achieve anonymity. However, the effects of digital versus physical platforms on CE are uncertain, so we propose two rival hypotheses.

On the one hand, CE dimensions should display stronger effects on marketing outcomes on digital platforms, where CE is more relevant, due to the structural differences between digital and physical platforms (Lieberman & Schroeder, 2020). On digital platforms, customers can access information about the engagement object and enter into exchanges with others easily (Lieberman & Schroeder, 2020; Santini et al., 2020). For example, CE gained great popularity with the rise of social media, such as Twitter, Instagram, and Facebook, which facilitate the formation and maintenance of social networks (Lieberman & Schroeder, 2020). Through facilitated exchanges and information dissemination on digital platforms, customers learn about the importance of the engagement object (Santini et al., 2020), so CE should drive customers’ future marketing outcomes on digital platforms.

On the other hand, customers often rely on stereotypes when assessing computer-mediated communication (Jacobson, 1999) and perceive exchanges on digital platforms as shallow or lacking in depth (Taylor, 2022), as well as less meaningful than real-world exchanges. For example, after the peak of the COVID-19 pandemic, many firms encouraged employees to return to offices, citing the greater quality of real-world interactions compared with virtual interactions. Furthermore, Taylor and Kent (2014, p. 393) stress that “[p]osting comments on a social media site is no substitute for calling someone on the telephone, or meeting others, to discuss an issue.” Accordingly, exchanges about a focal issue (or CE effects) on a physical platform may be perceived as more meaningful than exchanges about the same issue on a digital platform.

H7

The positive relationships of CE dimensions with (a) behavioral intentions and (b) behavioral outcomes are stronger for digital than for physical platforms.

H8

The positive relationships of CE dimensions with (a) behavioral intentions and (b) behavioral outcomes are weaker for digital than for physical platforms.

Initiator of interaction

As proposed by platform theory, we also consider the effects of the initiator of platform interactions, which may moderate the translation of CE dimension into marketing outcomes. Vivek et al. (2012, p. 132) explain that “either the provider (or organization or firm) or the customer may initiate the interaction”; we propose stronger effects of CE dimensions on marketing outcomes for customer-initiated interactions, due to their reflection of the importance of the focal issue to customers (Beckers et al., 2018). Usually, customers initiate an interaction when experiencing an urge to exchange about an issue with great relevance for them (Paluch & Blut, 2013). Comparing customer- with firm-initiated brand communities, Li et al. (2019) argue that the instigator influences the importance of the engagement object. When customers make decisions, they tend to consider personally relevant factors, which in turn inform whether they decide to remain in a relationship with the firm. Thus, the CE dimensions should display stronger relationships with marketing outcomes when the interactions are initiated by the customer than the firm.

H9

The positive relationships of CE dimensions with (a) behavioral intentions and (b) behavioral outcomes are stronger for customer- than firm-initiated interactions.

Traditional marketing strategies

The effects of product performance and brand associations, as traditional marketing efforts, also might vary with platform characteristics. Our focus explicitly is on CE initiatives, so we do not derive hypotheses for these traditional marketing strategies. Instead, we briefly explain a rationale for these parallel moderating effects. For both product performance and brand associations, we anticipate stronger effects of continuous versus spot interactions. Citing knowledge structures, Harmeling et al. (2017) note that repetition strengthens cognitive bonds, which improves information recall (Anderson & Bower, 2014). Repetition, as facilitated by continuous interactions, helps customers activate and consolidate memories (Pezzulo et al., 2014), such that they are more likely to recall past positive experiences, which can drive CE. In addition, product performance and brand associations both might exert weaker effects on CE dimensions in rich interactions, because their diverse informational cues distract customers (Dennis & Kinney, 1998), who then are less likely to pay attention to any specific cue that evokes their past positive experience with the firm. Because customers are less likely to recall past positive experiences in rich interactions, compared with lean ones, they are less likely to engage. We do not expect any differences for digital versus physical platforms or for different initiators of the interaction.

Control variables

We include some methodological considerations in our framework, due to our need to account for the diversity of the studies we collected in our meta-analysis. First, we check for differences between customer surveys and other research designs. Surveys usually produce smaller effect sizes than experiments, because it is more difficult to eliminate potential confounds (Grewal et al., 2018). Second, the data collection approaches adopted by our source articles might involve single or multiple industries. With more diverse industries, the range of constructs of interest increases, which influences the magnitude of the effect sizes (Geyskens et al., 1998). Third, we consider the quality of the publication outlet. High-quality journals have more rigid mechanisms to control for factors that might inflate effect sizes (e.g., common method variance) (Hunter & Schmidt, 2004). Fourth, publication status serves as a moderator; we distinguish between published and unpublished contributions. Insignificant effects are less likely to be published in journals, due to publication bias (Hunter & Schmidt, 2004). Fifth, we include the study year, because customers’ greater experience over time with social media and other technology may foster their engagement (Hollebeek et al., 2014).

Method

Search strategy and inclusion criteria

We searched for CE studies in several electronic databases (ABI/INFORM, ProQuest, Web of Science, EBSCO Information Services), using combinations of keywords such as “customer engagement,” “consumer engagement,” and “customer brand engagement.” We complemented these efforts with additional searches through Google Scholar, to identify conference proceedings and dissertations not published in journals. We also identified studies that cited van Doorn et al. (2010), as an influential contribution in this research domain, and reviewed the reference lists of all those studies, as well as those of other review articles, such as Santini et al. (2020) review. Finally, we searched for and included unpublished data sets.

Four criteria determine whether studies enter the meta-analysis. First, we required that they be empirical studies at the customer level and thus excluded conceptual articles (Hollebeek et al., 2019), qualitative studies (Hollebeek, 2013), and empirical studies that investigate engagement at the firm level (Gambetti et al., 2012). Second, the articles had to measure the CE construct; if they refer to CE but do not measure it, we exclude them. Third, eligible articles contain sufficient data to calculate effect size statistics (e.g., correlation coefficients, regression weights, t-values) for at least one relationship between CE and another construct. Fourth, we excluded studies of political engagement, civic engagement, or digital literacy, to ensure the study contexts involve firms and their products. We obtained 350 usable studies for the meta-analysis.

Effect size measures and data coding

We used correlation coefficients as effect sizes because they are independent of the scale and reported in most studies. If this information was not available, we converted other statistical information into correlations (Peterson & Brown, 2005). In line with conventional standards for meta-analyses, we averaged the effect sizes of samples that reported more than one correlation for the same relationship, to avoid giving any sample too much weight in subsequent analyses (Palmatier et al., 2006). After averaging the effect sizes, the data set included 5,005 correlations, reported in 395 independent samples extracted from 350 studies. Of the 395 samples, 325 were published in journals and 70 in conference proceedings, dissertations, or unpublished works; 10 were published between 2005 and 2010, 101 between 2011 and 2015, and 284 between 2016 and 2020. The cumulative sample size was 434,233. In more detail, coders initially extracted 7,323 effect sizes, which were averaged into 5,005, of which 1,612 effect sizes involve constructs in Fig. 1, and the rest pertain to the antecedents discussed in the Web Appendix C.

Three coders extracted necessary information and calculated the effect sizes. They also classified the effects according to the construct definitions in Table 2 and examined the scale items to classify the effect sizes. They reached 97% agreement and discussed any disagreements jointly. The coders extracted information about sample sizes, construct reliabilities, study characteristics that reflect the contextual setting of the study, and method characteristics. With dummy codes, they gauge the four focal moderators: interaction intensity (1 = continuous; 0 = spot), richness (1 = rich; 0 = lean), platform type (1 = digital; 0 = physical), and initiator (1 = customer; 0 = firm). They also dummy-coded the control variables: data collection (1 = single industry; 0 = multiple), research design (1 = survey; 0 = other), and publication status (1 = published; 0 = unpublished). We extracted the study year from the articles and assessed publication outlet quality according to the ABS journal list, with ratings ranging from 1 (low quality) to 4 (high quality).

Table 2 Construct definitions, aliases, and representative studies
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Integrating effect sizes and multivariate analyses

We used the widely adopted random effect approach suggested by Hunter and Schmidt (2004) to integrate effect sizes. First, we corrected the effect sizes for artifacts, including measurement error in the dependent or independent variable, then divided the correlations by the square root of the product of the respective reliabilities of the two constructs of interest. Second, we weighted the measurement error-corrected correlations by the sample size to correct for sampling errors. Third, with 95% confidence intervals for each sample size–weighted and artifact-adjusted correlation, we checked the power of our statistical tests (Muncer et al., 2003). We also calculated credibility intervals, which indicate the distribution of effect sizes (Hunter & Schmidt, 2004). Wide credibility intervals suggest variation in effect sizes and the need for moderator analyses to account for unexplained variance. Fourth, to assess the homogeneity of the effect size distribution, we used the χ2 test of homogeneity (Hunter & Schmidt, 2004). Fifth, as a check for potential publication bias, we used Rosenthal’s (1979) fail-safe N (FSN), which indicates the number of studies with null results that would be required to lower a significant relationship to a barely significant level (p = 0.05). Rosenthal (1979) suggests a tolerance level, such that the results are robust when FSNs are greater than 5 ⋅ k + 10, where k equals the number of correlations. As another check for publication bias, we used funnel plots with effect sizes on one axis and sample sizes on the other; an asymmetric plot would indicate potential publication bias. Sixth, we calculated the shared variances between constructs and the binomial effect size display (BESD), to determine the practical relevance of the meta-analytic findings. A high BESD between CE and behavioral intentions indicates the likelihood of more favorable outcomes from one group (e.g., engaged customers) relative to a reference group (e.g., disengaged customers) (Grewal et al., 2018). Following these checks, we analyzed the data using a multilevel moderator analysis and structural equation modeling. Web Appendix D contains more information about the multivariate analyses.

Results

Descriptive results

Table 3 contains the results of the effect size integration. In the meta-analytic framework in Fig. 1, CE is a multidimensional construct, but to acknowledge alternative CE conceptualizations (Web Appendix E), we report the results for a unidimensional and higher-order conceptualization too. The effect size integration results consistently suggest the use of our proposed CE conceptualization, due to the observable differences among CE dimensions. Thus, we only discuss the results of the multidimensional approach. The shared variances among CE dimensions are rather low (29–48%), which points to three separate, lower-order constructs rather than one higher-order or unidimensional conceptualization (Edwards, 2001). We test for differences using multilevel modeling, and the results support the proposed conceptualization (Web Appendix F).

Table 3 Descriptive results for CE antecedents and consequences
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CE marketing initiatives

As we detail in Table 3, both task-based and experiential initiatives exert significant effects on all CE dimensions, though these effects differ by dimension. The task-based initiatives have weak effects on behavioral CE (sample-weighted, reliability-adjusted average correlation [rwc] = 0.21, p < 0.05) but stronger effects for cognitive CE (rwc = 0.37, p < 0.05; ZBCE−CCE = 9.00, p < 0.01) and emotional CE (rwc = 0.48, p < 0.05; ZBCE−ECE = 21.31, p < 0.01). We find larger effect sizes of experiential initiatives for cognitive CE (rwc = 0.38, p < 0.05; ZBCE−CCE = 10.42, p < 0.01) and emotional CE (rwc = 0.38, p < 0.05; ZBCE−ECE = 10.67, p < 0.01) than for behavioral CE (rwc = 0.12, p < 0.05). These latter findings may suggest indirect effects of both initiatives, through cognitive and emotional CE. Comparing both engagement initiatives, we find further differences. Task-based initiatives, on average, are more effective in driving CE than experiential initiatives, according to their stronger effect sizes on behavioral CE (rwctask = 0.21, rwcexp. = 0.12; Ztask−exp. = 11.44, p < 0.01) and emotional CE (rwctask = 0.48, rwcexp. = 0.38; Ztask−exp. = 4.16, p < 0.01), though we find no differences for cognitive CE.

Traditional marketing

Both traditional marketing strategies relate significantly to CE. Product performance relates more strongly to emotional CE (rwc = 0.60, p < 0.05) than to cognitive CE (rwc = 0.50, p < 0.05; ZCCE−ECE = 9.28, p < 0.01) or behavioral CE (rwc = 0.47, p < 0.05; ZBCE−ECE = 14.02, p < 0.01).Footnote 3 Brand associations exhibit positive links to all three CE dimensions, with stronger effects for cognitive CE (rwc = 0.58, p < 0.05; ZBCE−CCE = 19.59, p < 0.01) and emotional CE (rwc = 0.59, p < 0.05; ZBCE−ECE = 17.55, p < 0.01) than for behavioral CE (rwc = 0.41, p < 0.05). When we compare the effect sizes of the CE-specific (task-based and experiential) initiatives with traditional marketing strategies (product performance and brand associations), we consistently find stronger effects sizes for the latter (all p < 0.01).

Consequences of CE

All CE dimensions relate to behavioral intentions, including behavioral CE (rwc = 0.49, p < 0.05), cognitive CE (rwc = 0.50, p < 0.05), and emotional CE (rwc = 0.55, p < 0.05). They also relate to behavioral outcomes: behavioral CE (rwc = 0.24, p < 0.05), cognitive CE (rwc = 0.21, p < 0.05), and emotional CE (rwc = 0.28, p < 0.05). In summary, firms benefit from engaging customers, and all CE dimensions matter.

The results of the effect size integration are robust to publication bias; the FSNs exceed the suggested tolerance levels (Rosenthal, 1979). The funnel plots do not indicate publication bias either. However, the significant Q-tests and wide credibility intervals suggest substantial variance in effect sizes and the need for moderator analyses. According to the power tests, our statistical analyses have sufficient power (> 0.5). The high shared variances (R2) and high BESDs also suggest that the antecedents we examine have great practical relevance for explaining CE, and CE is important for explaining marketing outcomes.

Structural equation modeling results

We used structural equation modeling (SEM) to test the meta-analytic framework and mediating effects; Web Appendix G contains the correlation matrix we used for this SEM. The fit of the model is good, according to the results in Table 4.

Table 4 Testing the meta-analytic framework of CE
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Regarding CE marketing initiatives, task-based (γ = 0.18, p < 0.05) and experiential (γ = 0.21, p < 0.05) initiatives relate to cognitive CE, with no difference between them (p > 0.05). For emotional CE, task-based (γ = 0.19, p < 0.05) and experiential (γ = 0.09, p < 0.05) initiatives exert positive effects, and the path coefficient is stronger for task-based than experiential initiatives (γtask = 0.19 vs. γexper. = 0.09, t = 5.35, p < 0.01). Neither task-based nor experiential initiatives relate to behavioral CE (both p > 0.05).

With respect to traditional marketing, product performance (γ = 0.11, p < 0.05) and brand associations (γ = 0.40, p < 0.05) relate to cognitive CE, but when we combine them in the SEM, product performance loses relevance as driver of cognitive CE. Product performance displays weaker effects than task-based initiatives (γproduct = 0.11 vs. γtask = 0.18, t = 2.54, p < 0.01) and experiential initiatives (γproduct = 0.11 vs. γexper. = 0.21, t = 3.90, p < 0.01), while the effects of brand associations are stronger than those of task-based initiatives (γbrand = 0.40 vs. γtask = 0.18, t = 9.55, p < 0.01) and experiential initiatives (γbrand = 0.40 vs. γtask = 0.21, t = 8.06, p < 0.01). For emotional CE, both product performance (γ = 0.19, p < 0.05) and brand associations (γ = 0.17, p < 0.05) exert positive effects. Whereas we find no difference in the effects of these traditional marketing strategies with task-based initiatives (both p > 0.05), the effects of product performance (γproduct = 0.19 vs. γexper. = 0.09, t = 4.78, p < 0.01) and brand associations (γbrand = 0.17 vs. γexper. = 0.09, t = 3.95, p < 0.01) are stronger than those of experiential initiatives. Product performance has a significant effect (γ = 0.17, p < 0.05) on behavioral CE, but brand associations do not (p > 0.05).

Regarding the CE dimensions, emotional CE (β = 0.30, p < 0.05) and cognitive CE (β = 0.25, p < 0.05) relate to behavioral CE. We also uncover the expected link between cognitive CE and emotional CE (β = 0.39, p < 0.05).

Regarding the consequences of CE, none of the CE dimensions has a direct effect on behavioral outcomes (all p > 0.05). Instead, behavioral CE relates to behavioral intentions (β = 0.29, p < 0.05), which influences behavioral outcomes (β = 0.26, p < 0.05). In addition, task-based (γ = 0.25, p < 0.05) and experiential (γ = 0.20, p < 0.05) initiatives relate to behavioral intentions, but not to behavioral outcomes. Whereas product performance (γ = 0.05, p < 0.05) relates to behavioral intentions, brand associations relate to both behavioral intentions (γ = 0.26, p < 0.05) and behavioral outcomes (γ = 0.28, p < 0.05). Thus, the results suggest direct effects of different strategies on marketing outcomes and indirect effects through CE (Web Appendix H).

We acknowledge the possibility of reverse causality, such that behavioral CE might induce cognitive and emotional CE (Sussman & Gifford, 2019), such as through self-perception processes or misattribution of arousal. The results of testing this alternative model provide support for the proposed conceptualization (Web Appendix I).

Moderator test results

In Table 5, we summarize the moderating influences of the four platform characteristics in terms of explaining when different CE initiatives improve CE and when increases in CE lead to more positive marketing outcomes. Although we only propose that two platform characteristics moderate CE formation, we test for the influence of all of them.

Table 5 Moderator analysis results
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Intensity of platform interaction

The moderating effects of the intensity of the interaction involve both the antecedents and consequences of CE dimensions. As predicted, the effects of task-based initiatives on emotional CE (H1; b = 0.26, p < 0.05) are greater for continuous than spot interactions. In line with our predictions, we observe weaker effects of experiential initiatives on behavioral CE for continuous interactions (H2; b = − 0.20, p < 0.05). The effect sizes of emotional CE on behavioral outcomes also diminish for continuous interactions (H3b; b = − 0.26, p < 0.05), but no differences arise for behavioral intentions, so we cannot confirm H3a.

Richness of platform interaction

This moderator is relevant for both the consequences and the antecedents of CE dimensions too. We find weaker effects of task-based initiatives on behavioral CE for rich interactions (b = − 0.12, p < 0.05), in line with our predictions in H5 but contrary to H4a. That is, our findings align with the task–media fit hypothesis. We do not find any moderating effect for the relationships of experiential-based initiatives with CE dimensions and thus must reject H4b. Among the consequences, the effect size of emotional CE on behavioral intentions is greater for rich interactions (b = 0.08, p < 0.05), contrary to H6a. Information provided in rich interactions seems to reassure customers and motivate them to maintain their relationship with a firm. In line with our predictions in H6b, emotional CE has weaker effects on behavioral outcomes in rich interactions (b = − 0.35, p < 0.05).

Platform type

This moderator seems more important for the consequences of CE than for its antecedents. As expected, we observe no differences between digital and physical platforms regarding the antecedents of the CE dimensions. But two significant effects emerge in this analysis, in line with our predictions: The effects of behavioral CE (b = − 0.16, p < 0.05) and emotional CE (b = − 0.14, p < 0.05) on behavioral intentions are weaker for digital platforms, in support of H8a but contrary to H7a. This finding is in line with customers’ negative views of digital versus physical interactions. Yet because we do not observe differences for behavioral outcomes, we cannot confirm H8b and H7b.

Initiator of platform interaction

For the initiator of platform interaction moderator, we again find no significant effects for antecedents of CE. As expected, the moderating effects are more pertinent to the consequences of CE. Behavioral CE displays stronger effects on behavioral intentions (b = 0.08, p < 0.05) if the customer initiates the interaction, and the results are similar for emotional CE (b = 0.18, p < 0.05), in support of H9a. But in contrast with H9b, we do not find differences for behavioral outcomes.

Traditional marketing strategies

In additional post hoc analyses, we test for the moderating effects of platform characteristics on traditional marketing strategies. Regarding intensity, we observe weaker effects of brand associations on emotional CE (b = − 0.34, p < 0.05), in contrast with our predictions; that is, brand associations appear are more effective in spot interactions. We find no differences for product performance. For richness, we find weaker effects of product performance on behavioral CE in rich interactions (b = − 0.12, p < 0.05), in line with our predictions. No difference emerges for brand associations. As expected, no influences result from the platform type or initiator of the platform interaction.

Control variables

The results of the moderator tests remain consistent when we control for method moderators. For a few relationships, the effect sizes are weaker in single- compared with multi-industry studies, and some relationships appear stronger if the studies rely on surveys. The results are in line with the subgroup analysis (Web Appendix J).

Discussion

With the general assumption that their firm can benefit from engaging customers, managers invest in CE marketing, using different engagement strategies deployed on various engagement platforms. With a meta-analysis and data from 395 samples, involving 434,233 customers, we develop and test a unifying framework to help them optimize these investments. In particular, the results give managers insights into which strategies improve CE and when greater CE prompts more positive marketing outcomes.

When do task-based, experiential, or traditional marketing initiatives improve CE?

Both task-based and experiential initiatives relate positively to cognitive and emotional CE, but the effects on behavioral CE are indirect, through other CE dimensions. Experiential initiatives tend to be less effective in driving cognitive and behavioral CE than are task-based initiatives (Tables 3 and 4), likely because triggering self-transformation processes through experiential initiatives is inherently difficult. Furthermore, experiential initiatives are less effective than traditional marketing strategies in driving emotional CE, but task-based initiatives exhibit similar effectiveness. Both CE-specific initiatives drive cognitive CE better than product performance does, but they are not as effective as brand associations. Thus, strategies that deliberately stimulate CE can be as effective as strategies that rely on organic inducements of engagement.

By incorporating insights from platform theory, we also can detail which platform characteristics determine the effectiveness of different engagement strategies. Through this investigation of the moderating effects of the intensity of interaction, richness, platform types, and initiator, we establish contextual explanations for when engagement strategies are more effective for driving CE. Thus, we provide a general overview of the distinctly moderated relationships between engagement strategies and CE dimensions.

First, the intensity and richness of the platform interactions are relevant, whereas we do not find moderating effects of platform type and the initiator on the formation of CE. The platform and the initiator do not function as contextual differentiators, unlike the platform’s underlying traits. The lack of a moderating effect of the platform type might reflect the widespread use of digital platforms for engagement initiatives, such that many firms already use multiple channels to engage customers (Meire et al., 2019). Notably, both interaction intensity and richness exert mixed influences across emotional and behavioral CE.

Second, engagement strategies might be less susceptible to moderation by platform traits. But when the moderation effects emerge, they have divergent implications for different strategies. For example, continuous interactions strengthen the relationship of task-based initiatives with emotional CE, but they weaken the relationship of experiential initiatives with behavioral CE. The former strengthening effect implies that customers perform better on tasks when they interact more with the firm. The latter effect resonates with predictions of wear-out effects; experiential initiatives become less effective over time, because unusual events that increase emotional intensity cannot be redundant or frequent. Our finding that the effects of task-based initiatives on behavioral CE are weaker during rich interactions is in line with the task–media fit hypothesis, which suggests that structured tasks require lean media.

Third, the importance of traditional marketing strategies varies with the intensity and richness of the platform interaction. Even if product performance and brand association both rely on the activation of memories to influence CE, the triggers may differ. Specifically, product performance is more important for driving behavioral CE in lean than in rich interactions; lean interactions help customers focus on specific cues that activate their previous positive product experiences. Brand associations are more important for driving emotional CE in spot interactions than continuous interactions; it seems that wear-out effects reduce customer attention and recall.

When does greater CE evoke more positive marketing outcomes?

The meta-analysis results suggest that, on average, firms benefit from engaging customers. The CE dimensions influence unique marketing outcomes, and we can establish pathways to behavioral outcomes, through intentions. In addition, emotional and cognitive CE exert indirect effects through behavioral CE. Failing to acknowledge the mediating effects can lead to underestimates of the effectiveness of CE as a predictor of behavioral outcomes. This finding may help explain the mixed results in prior studies regarding the effects of the CE dimensions on various outcome variables.

Regarding the moderators we test, we note some important differences in the results related to the formation of CE and its translation into firm outcomes. The intensity of the platform interaction is mainly relevant for the formation of CE, whereas the platform type and initiator are mainly relevant for translating CE into marketing outcomes. The richness of the interaction seems more broadly influential, in that it moderates both the formation and translational pathways. Specific to the links between CE dimensions and marketing outcomes, we need to consider additional moderating effects, related to the platforms’ type and traits. In some conditions, behavioral CE even can function as a sole predictor of marketing outcomes; in others, emotional CE exerts direct effects. For example, emotional CE is more important in driving marketing outcomes during customer-initiated interactions but twice less important in continuous or rich interactions.

In seeking to establish such moderating effects, we identify and define some that have not been tested before. For example, behavioral and emotional CE display weaker effects on behavioral intentions for digital than physical platforms; their effects also are weaker for firm- than customer-initiated interactions. Negative views of digital versus real-world exchanges undermine the effectiveness of engagement for driving marketing outcomes in digital environments, but in customer-initiated interactions, CE has greater personal relevance and thus more powerful effects. We find that emotional CE exhibits weaker effects on behavioral outcomes for continuous than spot interactions and for rich than lean interactions. Because spot interactions are constrained in time and topic, the engagement object is more salient in such interactions, enhancing its personal relevance. Customer decision-making is more complex in rich interactions, so engagement also loses relevance as a predictor of marketing outcomes, because it is just one of many cues to consider. Finally, cognitive CE displays similar effectiveness across different platforms. Overall, these insights highlight the usefulness of integrating platform theory with CE marketing theory. Table 6 summarizes the results of our hypotheses testing.

Table 6 Summary of hypotheses test results
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Managerial implications

We summarize the implications of our findings for managers investing in CE marketing in Table 7. Our results can guide managers when they must choose between the two engagement strategies and when investing their CE marketing budgets across platforms in ways that increase the chances that their firm will benefit from engaging customers.

Table 7 Managerial implications
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First, managers should realize that task-based initiatives, on average, are more effective than experiential initiatives and acknowledge the difficulty of triggering self-transformation in customers, especially compared with the relative ease of inducing ownership feelings through task-based initiatives. Firms can allocate their resources across engagement strategies that deliberately seek to motivate customers to invest resources and engage with the firm and traditional marketing strategies that organically lead to engagement. Both can stimulate CE, though differently across CE dimensions. In this sense, the approaches can complement each other, and managers should leverage them accordingly.

Second, regarding distinct conditions in which to prioritize different engagement strategies, we recommend that managers map their engagement strategies onto the characteristics of the platforms they are considering, using the matrix displayed in Table 1. By doing so, they can derive tactical marketing and communication plans, as well as align their budget allocations across different engagement platforms. In general, managers should use task-based initiatives on platforms that support continuous interactions, because they facilitate customer learning (see the platforms in columns A and B in Table 1). Task-based initiatives also are more effective on platforms that support lean interactions rather than those that feature distracting and unnecessary information (columns B and D). Thus, managers have to be more careful when choosing rich engagement platforms, such as social games and social worlds, for task-based initiatives (e.g., Facebook’s metaverse; Kaplan & Haenlein, 2010). If managers instead seek to encourage customers’ self-transformations, they should install experiential initiatives on platforms that support spot interactions, during which customers have not yet fully reflected on the firm or its brand (columns C and D). No differences were observed for the other two moderators, so managers can employ either kind of initiative on digital or physical platforms. They can also employ either kind of initiative in relation to customer- or firm-initiated interactions.

Third, on average, firms benefit from engaging customers. The three CE dimensions relate to behavioral outcomes and intentions, though emotional and cognitive CE display indirect effects through behavioral CE. Thus, practitioners should continuously measure and monitor their effects. By applying CE funnels or conversion tables, they can determine the percentage of customers who are engaged on each CE dimension.

Fourth, managers should use all the classification criteria we include in our meta-analysis when assessing returns on CE efforts: intensity, richness, platform type, and interaction initiator. They generally can expect stronger effects of the CE dimensions on marketing outcomes with spot interactions (columns C and D, Table 1), on physical platforms (rows 3 and 4), and for customer-initiated interactions (rows 2 and 4). The effects for rich versus lean interactions vary by outcome variable. Thus, managers should carefully map the three CE dimensions on platform characteristics to choose the most effective platform in terms of translating CE into their desired marketing outcomes.

Research agenda

In presenting several avenues for further research, we emphasize issues related to the effectiveness of engagement strategies, firm benefits of engaging customers, and method- and data-related issues (Table 8).

Table 8 Research agenda
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First, more detailed research is needed to gauge the effectiveness of engagement strategies, including measures of the specific mechanism (e.g., psychological ownership, self-transformation) by which experiential and task-based initiatives relate to CE dimensions. Scholars also might differentiate types of task-based initiatives (e.g., financial rewards vs. social recognition) or experiential initiatives (e.g., crowdsourcing events vs. brand fests), then assess their distinct interactions with traditional marketing strategies (e.g., idea competition accompanied by a branding campaign). Combining both sets of strategies may enhance their effectiveness. Regarding platform differences, we also suggest extended research into platform characteristics. One research objective might be to explore how to design task-based initiatives that are effective in spot interactions. In some cases, task-based initiatives might benefit from rich media, and experiential initiatives can benefit from lean media. Perhaps specific types of task-based and experiential strategies work better on digital platforms than physical platforms, or vice versa. Certain engagement strategies also might become effective, depending on the customer’s specific motivation for initiating an interaction. Beyond deepening understanding of the platform characteristics that we study, scholars could consider other moderators too, such as multichannel environments (mixed digital–physical platforms), in which customers can engage with the same firm across multiple platforms.

Second, we call for more research into the benefits for firms, across more marketing outcomes. Alternative outcome variables of CE might pertain to the firm level (e.g., sales, profitability), instead of the customer level. Continued studies also should consider the costs of engagement strategies to gauge firm profitability (return on investment). Furthermore, we conceptualize CE as a three-dimension, multivariate construct; further research on emotion–cognition interactions could provide more nuanced, less dichotomous views on these CE dimensions (Pessoa, 2018), as supported by the theories outlined in Web Appendix K. For example, a process model of emotion regulation suggests that people regulate their emotions through four underlying processes (situation selection, situation modification, attentional deployment, and cognitive change; Gross, 1998). Regarding platform differences, scholars could build on our framework to test for potential segment-specific preferences for digital or physical platforms, such as generational differences (e.g., Generation Z). Another interesting question involves how firms might initiate interactions to increase their personal relevance to customers, which should make CE more influential. In in-depth considerations of different types of richness (e.g., content, format) and their effects on CE effectiveness, media richness literature also might be useful. Finally, we encourage scholars to identify other factors that might influence the effectiveness and impacts of engagement strategies on marketing outcomes. For example, emerging technologies (e.g., virtual reality, social robots) and their unique characteristics likely have distinct effects.

Third, CE marketing literature would benefit from more diverse methodologies. Most existing studies use cross-sectional data, but marketing strategies can lose effectiveness over time, due to wear-out effects. Longitudinal research designs can specify the relevance of such strategies over time. In addition, we find that emotional and cognitive CE relate to behavioral CE. By moving beyond the widespread use of surveys, researchers conducting experiments could check further for reverse causality (i.e., can behavior induce emotional and cognitive bonds?). More qualitative approaches also might help reveal the reasons for some of our surprising findings. We hope this agenda for advancing CE research proves inspiring.