The value relevance of digital marketing capabilities to firm performance

In our conceptual framework, we distinguish between DMCs and CMCs. Drawing on literature-based and interview-based insights, we define DMCs as a firm’s ability to use digital technology–enabled² processes to interact with customers and partners in a targeted, measurable, and integrated way to create new forms of value without regard for distance or time (Kannan & Li, 2017; McIntyre & Virzi, 2019; Sridhar & Fang, 2019). CMCs refer to a firm’s ability to use marketing-mix-related processes of product/service management and development, pricing and sales management, and market research and communication management to achieve desired marketing outcomes (Morgan 2012; Morgan et al., 2018). As Table 2 shows, DMCs and CMCs differ in terms of (1) scalability, (2) measurability, (3) interconnectivity, and (4) adaptability.

First, DMCs are characterized by a higher degree of scalability than CMCs. Scalability is the degree to which a firm is able to generate increasing returns in the marketplace with an under proportionate input of additional resources (Nielsen & Lund, 2018; Zhang et al., 2015). Although CMCs have high revenue potential, they incur significantly higher deployment costs than DMCs (Sridhar & Fang, 2019). This view is confirmed by Goldfarb and Tucker (2019, p. 281), who note that compared with classic marketing, “[d]igital marketing is inherently different … due to a reduction of five categories of costs: Search, reproduction, transportation, tracking, and verification.” DMCs foster the implementation of highly scalable content-based (i.e., selling digital content), information-based (i.e., selling customer information), or advertising-based (i.e., selling the customers’ attention in the form of online advertising) revenue models and combinations thereof. In this way, DMCs have the potential to generate higher revenues at comparably lower costs than CMCs through the provision of digital goods that are non-rival and have low or even zero deployment costs (Kannan & Li, 2017; Lambrecht et al., 2014). Regarding the high scalability potential of DMCs, the marketing manager of a B2B internet firm emphasized the role of digital content:

In general, content is the most cost-effective marketing channel, simply because once you’ve got a piece of content, which performs well, you can repurpose it with little effort, or just leave it as is and it will continue to generate increasing traffic, leads, email subscribers and so on, just because it is out there. Since the content is providing value, more and more customers will engage with your firm and eventually become paying customers. This is particularly what we observe with SaaS companies in our industry, which experience high growth.

Second, DMCs are characterized by a higher degree of measurability than CMCs. Measurability is the degree to which a firm is able to generate and assess information related to its activities in the marketplace (Homburg et al., 2012a; O'Sullivan & Abela, 2007). Increasing pressure on marketing to demonstrate its value to the firm has led to the development of sophisticated marketing performance measurement systems that help firms measure the efficiency and effectiveness of CMCs (O'Sullivan & Abela, 2007). Nevertheless, the measurability of CMCs is generally lower than that of DMCs. This is because DMCs are inherently data-driven, thus providing managers with higher volume, variety, and velocity of data than CMCs (Johnson et al., 2017; Li et al., 2021). While this imposes new challenges for marketing analytics, it still enables companies to generate more information about and better evaluate the performance of marketing activities (Wedel & Kannan, 2016). Overall, DMCs enable more precise targeting of individual customers and more accurate attribution of causes and effects of marketing activities to individual customers, which is particularly evident in the advertising context (Bayer et al., 2020). As a group brand manager from the consumer goods industry noted:

Digital marketing is more data-driven than traditional marketing. For example, social media gives you the opportunity to analyze the performance of every single post, how many likes has it achieved, how good is it in terms of range, how is the interaction with our customers, and so on. In the past, we had a TV spot, we did survey-based market research, or invited customers to our studio to gather market feedback. In the social media area, you get feedback from customers in real-time, learn from it, and improve your marketing activities.

Third, DMCs are characterized by a higher degree of interconnectivity than CMCs. Interconnectivity is the degree to which a firm is able to establish and leverage interactive linkages with existing and new customers and partners in the marketplace (Bellamy et al., 2014; Liu et al., 2020). A “key focus for DMCs is to enhance linkages with customers, suppliers and channel partners” (Wang, 2020, p. 559), thus providing new sources of value creation through novel interaction possibilities and access to new markets within and outside a firm’s current industry boundaries. In contrast with CMCs (e.g., customer relationship management [CRM]), which primarily rely on firm-initiated interactions, DMCs (e.g., social media) enable new ways of firm- and customer-initiated interactions (Li et al., 2021; Malthouse et al., 2013). This empowers both—firms and customers—to better leverage their relationships by more actively listening to and engaging with each other. For example, the chief digital officer of a home appliances manufacturer illustrated the importance of mobile marketing for gaining unique access to customers, which was heretofore impossible:

[In the old world], we sell the product to the retailer and the retailer sells it to a customer. We don’t know our customers in the old world, we don’t really need to focus on interacting with them, we don’t have any opportunities to do something good for them…. In the digital world, we have a product that can be connected via a mobile application and where I as a manufacturer see exactly what the customer does with it. In other words, I know much more than the retailer, much more than any market research, and much more than product development. I am suddenly able to personally interact with the customer and find out how to do something good for him.

Fourth, DMCs are characterized by a higher degree of adaptability than CMCs. Adaptability is the degree to which a firm is able to rapidly sense and respond to changes in the marketplace (Day, 2011; Guo et al., 2018). While CMCs certainly allow firms to sense and respond to market opportunities and threats, the time span between market change and firm response is typically shorter for DMCs. For example, compared with A/B testing, which enables firms to adapt mobile apps or online advertising in real time, conducting survey-based market research and then adapting product features or offline advertising campaigns are typically very time-consuming (Guo et al., 2018). Day (2011, p. 183) recognizes the limited adaptability of CMCs by alluding to the need to develop DMCs that “are adaptive and enable the firm to adjust its strategies to fit fast-changing markets.” For example, the COVID-19 pandemic has shown that DMCs are critical to adapt to large-scale social distancing and continue the relationship with customers (The CMO Survey, 2021). As a regional marketing manager from a consumer goods firm noted:

The main difference that I see is in the speed… Digital marketing is much faster and much more adaptable than traditional marketing. In traditional marketing if you were doing market research, all the research and analysis took a long time, whereas in digital marketing you can generate and act on market insights overnight.

RBT relies on two key assumptions (Kozlenkova et al., 2014). First, the resource heterogeneity assumption posits that some firms are more skilled than others because they possess unique bundles of resources, defined as stocks of a firm’s tangible and intangible assets (Amit & Schoemaker, 1993; Morgan 2012). Second, the resource immobility assumption argues that, owing to the difficulty of obtaining and trading resources, firms vary in the ability to acquire and deploy resources, ultimately resulting in performance differences (Amit & Schoemaker, 1993; Barney, 1991). According to RBT, a firm’s capabilities are among the most critical drivers of firm performance (Kozlenkova et al., 2014).

Following prior marketing capability research (Feng et al., 2017; Morgan et al., 2009a, b), we aim to examine the direct effects of DMCs and CMCs on firm profitability (Fig. 1). Specifically, we consider return on assets (ROA), which indicates how effective a firm is in deploying its resources to generate profits (Katsikeas et al., 2016). As marketing capabilities enable firms to achieve competitive advantage through the effective transformation of resource inputs into outputs (Kozlenkova et al., 2014), ROA is of clear theoretical relevance for our study from an RBT perspective. Indeed, ROA is a dominant measure of firm performance in marketing capability research (e.g., Feng et al., 2015; Leonidou et al., 2013; Morgan et al., 2009b; Vorhies et al., 2011; Vorhies & Morgan, 2005).

In addition to positing the individual value relevance of different capabilities to firm performance, RBT argues that capabilities can interact with one another (Kozlenkova et al., 2014). Capabilities are complementary when a positive interaction exists or the marginal benefit of one capability increases as the level of the other capability increases. By contrast, capabilities are substitutive when a negative interaction exists or the marginal benefit of one capability decreases as the level of the other capability increases (King et al., 2008; Moorman & Slotegraaf, 1999). From a managerial perspective, the ultimate goal of modern marketing is to create complementarities between DMCs and CMCs to take full advantage of a firm’s capability portfolio (Armstrong et al., 2020). However, DMCs and CMCs can function as complements or substitutes, as the head of digital technologies from a consumer goods company noted:

It depends on how you do it [chuckles]. If I do it badly, digital and classic channels are in opposite directions to each other, they cannibalize each other, and I try to save in one channel what I lost in the other. Ideally, they complement each other, of course, because I also try to develop a holistic view of the customer’s decision-making, purchasing, and usage process, and then control and support this accordingly via classic and digital channels.… The most important thing is to really have a holistic view on the customer.

We adopt the contingent view of RBT (e.g., Aragón-Correa & Sharma, 2003) to select relevant contingencies that may either enable or prevent firms from realizing the full potential of their capability portfolio (see Fig. 1). We further support this selection with managerial perspectives from our in-depth interviews (see Table 3). Following Wade and Hulland (2004), we argue that the effective deployment of DMCs and CMCs is dependent on (1) organizational contingencies and (2) environmental contingencies.

Regarding organizational contingencies, we consider the three core components of market orientation: customer orientation, competitor orientation, and interfunctional coordination (Narver & Slater, 1990). This choice is for two main reasons. First, we aim to shed more light on the relevance of customer in relation to competitor orientation (Moorman & Day, 2016), given that both orientations may be effective in the digital age. On the one hand, customers are more informed, empowered, and demanding than ever before; on the other hand, firms face fierce competition from lower barriers to market entry, the dramatic rise of start-ups and digital firms, and the declining sustainability of competitive advantage (Vial, 2019). To cope with these challenges, managers may put a premium on a customer or competitor orientation, thereby affecting a firm’s ability to leverage its capability portfolio. Second, a recent survey shows that digital marketing practices (e.g., social media marketing, mobile marketing) are led by the marketing function in the majority of firms, whereas for classic marketing practices (e.g., new product development, pricing, sales management), this is the case only in a minority of firms (The CMO Survey, 2019).³ Consequently, firms need to interfunctionally coordinate their resources between business functions responsible for DMCs and CMCs to create superior value from their capability portfolio in the marketplace.

Regarding environmental contingencies, we consider the dynamism in a firm’s market environment. We chose environmental dynamism because in rapidly changing environments in the digital age, a comprehensive capability portfolio comprising digital and classic marketing practices may be critical in achieving superior performance (Day, 2011).

We consider the combination of primary manager data and secondary performance data from an objective database as particularly appropriate to test our hypotheses (Morgan et al., 2019). In fact, a key informant approach can provide the most direct measures of marketing capabilities (Danneels, 2016) and has a long tradition in marketing and management research (Conant et al., 1990; Menon et al., 1999; Morgan et al., 2018; Vorhies & Morgan, 2003).

In the first phase, we identified potential respondents for our survey through the online career platform XING. To favor generalizability of our findings, we did not constrain our population of interest to specific industries. Informed by our interviews, we deemed senior and top-level managers with primary responsibility for their firms’ general or digital marketing, sales, customer service, or business activities as competent key informants. We made several efforts to encourage participation and increase response rates (Hulland et al., 2018). To ensure clarity and minimize completion costs, we pretested the design of the survey instrument with four independent academic experts and 18 graduate students with several years of professional experience. In addition, via personalized e-mail we invited potential key informants to participate in our large-scale online survey and followed up with two waves of reminders. Finally, as an incentive we offered informants a study report and a choice between an Amazon.com voucher and a donation to a charity organization. During the five-week response period, we reached 2533 potential respondents and received 382 completed questionnaires (15% response rate). We discarded four observations because key informants failed quality checks.

In the second phase, we collected secondary performance data from Bureau van Dijk’s flagship financial database ORBIS to mitigate common method variance (Podsakoff et al., 2003). We were able to collect independent data for ROA for the majority of firms in our sample (n = 273) to test our hypotheses. Owing to less comprehensive public disclosure requirements, however, we could not obtain financial performance data for several family-, foundation-, or state-owned firms. In the model specification section, we account for a potential selection bias of these sub-samples. Web Appendix B details the sample characteristics.

We employ regression analyses to test our hypotheses. Details of the model specification appear in Web Appendix D. Before testing the hypothesized relationships, we checked for non-response bias, key informant bias, and key potential sources of endogeneity. Overall, we have strong indications that these threats are unlikely to bias the results of our study.

In Table 6, we report the results of the hierarchical ROA model estimation process, which includes control variables (Model 1), main effects (Models 2 and 3), and three-way interactions (Model 4). We mean-centered all variables before creating the interaction terms. We also checked for multicollinearity, which does not seem to threaten the validity of our results because variance inflation factors for the regression models are below the commonly accepted threshold value of 10 (Hair et al., 2010).

The results of Model 3 reveal a positive relationship between DMCs and ROA (b = .19, p < .05), in support of H1. Similarly, H2 is confirmed (b = .16, p < .05), as the main effect of CMCs on ROA is statistically significant. As Table 6 indicates, the increase in R-square from Model 2 to Model 3 due to adding DMCs is statistically significant (ΔR² = .02, p < .05), which shows that DMCs contribute to firm performance beyond the influence of CMCs. Though not hypothesized, Model 4 shows that the two-way interaction effect of DMCs and CMCs on firm performance is non-significant (b = −.03, p > .10). As indicated by the positive three-way interaction in Model 4, customer orientation positively moderates the interaction effect of DMCs and CMCs (b = .35, p < .05), in support of H3. However, the opposite is true for competitor orientation, as indicated by the negative three-way interaction among DMCs, CMCs, and competitor orientation in Model 4, in support of H4b (b = −.44, p < .01). As such, H4a is not supported. Similarly, H5 is not supported, as the three-way interaction among DMCs, CMCs, and interfunctional coordination in Model 4 is not significant (b = −.06, p > .10). Finally, Model 4 reveals that the three-way interaction among DMCs, CMCs, and environmental dynamism is significantly positive (b = .16, p < .05), in support of H6.

To illustrate the significant three-way interaction effects, we follow Schoonhoven’s (1981) partial derivative approach previously applied in marketing research (e.g., Noordhoff et al., 2011; Wathne & Heide, 2004) and evaluate the interaction effect of DMCs and CMCs across the range of values of the moderators. Figure 2 depicts the respective relationships. As Panel A indicates, the interaction effect of DMCs and CMCs is significantly negative at low values (b_{–1.5 SD} = −.45, p < .05; b_{–1 SD} = −.31, p < .10) and significantly positive at high values (b_{1 SD} = .24, p < .05; b_max = .29, p < .05) of customer orientation.⁹ Panel B indicates that the interaction effect of DMCs and CMCs on ROA is significantly positive at low values (b_{–1.5 SD} = .50, p < .05; b_{–1 SD} = .32, p < .05) and significantly negative at high values (b_{1 SD} = −.39, p < .05; b_{1.5 SD} = −.57, p < .01) of competitor orientation. Finally, Panel C shows that the interaction effect of DMCs and CMCs is negative at low values (b_{–1.5 SD} = −.25, p < .05; b_{–1 SD} = −.17, p > .10) and significantly positive at high values (b_{1 SD} = .11, p < .10; b_{1.5 SD} = .18, p < .05) of environmental dynamism.

Although scholars and practitioners have repeatedly stressed the importance of DMCs for firms to remain competitive in increasingly digital market environments, these capabilities have so far received inadequate research attention. By drawing on a mixed-methods approach, we contribute to and extend prior marketing research in three important ways.

First, we provide a new perspective on the differences between DMCs and CMCs by synthesizing literature- and interview-based insights. Our qualitative inquiry reveals that DMCs and CMCs differ in terms of (1) scalability, (2) measurability, (3) interconnectivity, and (4) adaptability (see Table 2). In this way, we provide a conceptual distinction between DMCs and CMCs and extend existing knowledge on marketing capabilities.

Second, our study broadens and deepens the understanding of whether DMCs contribute to firm performance beyond the effects of CMCs. In contrast with most previous studies (see Table 1), we considered both constructs and conceptualized them across a broad set of marketing activities to foster the generalizability of our results. By drawing on a multi-industry, multisource dataset collected at two points in time, we provide strong empirical support for the value relevance of DMCs to firm profitability. Thus, we add to the marketing literature by contributing to Verhoef and Bijmolt’s (2019) call for research to identify the key capabilities that drive firm performance in the digital age.

Third, our study represents a pioneering empirical effort by developing a contingency perspective on the interaction between DMCs and CMCs. Prior studies in the advertising context reveal that digital and classic marketing practices can have a non-significant (e.g., Bayer et al., 2020), positive (e.g., Kumar et al., 2017), or negative (e.g., Sridhar et al., 2016) interaction effect on firm performance. Given these conflicting empirical results, we theoretically argue that DMCs and CMCs can jointly benefit or hurt firm profitability, depending on moderating contingencies. Empirically, we show that the two-way interaction effect of DMCs and CMCs on firm performance is non-significant (see Table 6, Model 4). The results further reveal that this interaction effect may become positively or negatively significant under certain organizational and environmental contingencies, underscoring the importance of considering three-way interactions when investigating capability complementarity and substitution effects.

Regarding organizational contingencies, this study reveals that customer orientation is critical to unlock the contingent value of DMCs and CMCs, as their simultaneous deployment has a positive influence on firm profitability when customer orientation is high. By contrast, our additional analyses in the hypotheses testing section reveal that the simultaneous deployment of DMCs and CMCs has a negative impact on firm profitability when customer orientation is low (see also Fig. 2, Panel A). Previous research suggests that firms must not only possess distinctive capabilities to achieve superior performance but also a matching organizational orientation to leverage those capabilities (e.g., Newbert, 2007; Wiklund & Shepherd, 2003). In fact, a firm’s organizational orientation reflects its “guiding principles that influence a firm’s marketing and strategy-making activities” (Noble et al., 2002, p. 25)—such as the deployment of marketing capabilities (Menon et al., 1999). Following this line of reasoning, we assume that firms with a low customer orientation may be ineffective in utilizing their marketing capability portfolios in a profitable manner. For instance, these firms may not have incorporated the underlying systems, processes, or metrics to create an in-depth understanding of customers’ needs, behaviors, and buying patterns (e.g., Jayachandran et al., 2005; Morgan et al., 2005; Shah et al., 2006). Lacking this in-depth customer understanding, however, firms are ineffective in identifying the combinations of digital and classic channels and touchpoints that match the preferences and behaviors of distinct customer segments at different stages of the buying process (Lemon & Verhoef, 2016). As a result, firms with low customer orientation may repeatedly fail to reach customers through their preferred channel and touchpoint configurations, thus weakening customer relationships and ultimately firm profitability. In this way, firms’ marketing capability portfolios remain underutilized, such that their development and maintenance costs exceed their revenue contributions.

Furthermore, we find that competitor orientation hampers the deployment of DMCs and CMCs, resulting in lower levels of firm profitability (see also Fig. 2, Panel B). This finding is consistent with previous empirical studies confirming a negative impact of competitor orientation on firm profitability (e.g., Armstrong & Collopy, 1996). The empirical analyses thus argue against the positive side and in favor of the negative side of competitor orientation in deploying DMCs and CMCs. This is presumably because (1) competitive imitation of capability configurations is likely to fail due to the social complexity and causal ambiguity of market-based resources and (2) competitive differentiation gives firms a false incentive to increase complexity by adding new or preserving existing configurations of DMCs and CMCs to beat the competition.

Recently, Moorman and Day (2016, p. 25) raised the question whether the increasing emphasis on customer orientation and de-emphasis on competitor orientation in the digital age “endanger the performance effects of market-oriented culture” or “unleash an even stronger performance effect.” By identifying divergent moderating effects of customer orientation (i.e., positive effect) and competitor orientation (i.e., negative effect) on the interaction between DMCs and CMCs, we contribute to answering this research question.

Moreover, we find no support for the profitability-enhancing impact of interfunctional coordination on the interaction between DMCs and CMCs. We believe that this may be for two main reasons. First, previous research points out that higher levels of interfunctional coordination may incur significant costs associated with the cross-functional management of resources (Lee et al., 2015). These costs may offset the potential benefits of interfunctional coordination in terms of greater integration and reduced complexity between a firm’s DMCs and CMCs, such that the moderating impact of interfunctional coordination is non-significant. Second, there may be other types of interfunctional coordination through which firms achieve integration between DMCs and CMCs that we did not capture in our study. We used Narver and Slater’s (1990) measure of interfunctional coordination, which captures the value creation emphasis of a firm’s integration activities (Im & Nakata, 2008). Other types of interfunctional coordination may emphasize further integration activities related to the compatibility of information systems, quality of exchanged information, or harmony of relationships between functions responsible for DMCs and CMCs (e.g., Song et al., 2000).

Regarding environmental contingencies, we show that the simultaneous deployment of DMCs and CMCs is particularly valuable when firms face rapid changes in customer preferences, competitor actions, and technologies in their market environment. Specifically, in dynamic environments, the benefits of a broad capability portfolio in terms of increased marketplace differentiation and adaptation appear to outweigh the associated development and maintenance costs, thus increasing firm profitability. In stable environments, however, the simultaneous deployment of DMCs and CMCs hurts firm profitability, as outlined previously in our hypotheses testing section (see also Fig. 2, Panel C). This is likely because under these conditions, firms can achieve sufficient pay offs from a smaller number and less differentiated channel and touchpoint configurations. One important reason for this may be that customers less frequently shift between digital and classic channels and touchpoints across different stages of the buying process, as customer needs tend to be homogeneous and change slowly. Similarly, longer technology life cycles and a relatively stable competitive landscape allow companies with a narrower, less complex capability portfolio to adapt sufficiently quickly to marketplace changes (Eyuboglu et al., 2017). In other words, in stable environments, firms have no need to possess broader, more complex capability portfolios, and therefore the associated development and maintenance costs are unjustified.

In summary, our research confirms the “untested proposition … that the capabilities for making and implementing marketing mix decisions [i.e., CMCs] will become more effective and timely when guided by adaptive marketing capabilities [i.e., DMCs]” (Day, 2011, p. 194). In contrast with this bright side, we show that the simultaneous deployment of DMCs and CMCs also has a dark side, as it may hurt profitability.

As with any research, our study is subject to limitations, from which avenues for future research emerge. First, as managers must understand how to build DMCs to achieve higher payoffs, we encourage future research to investigate their antecedents. At the top management level, what is the role of the CMO in driving DMCs, and what are the potential coordination issues with other C-level executives (e.g., chief information officer)? At the employee level, which leadership styles (e.g., transformational vs. transactional) and incentives (e.g., individual vs. team) are most beneficial? When should firms develop DMCs internally and when should they outsource their development to agencies?

Second, additional research should identify positive and negative mediating mechanisms linking DMCs and CMCs with performance (e.g., customer, innovation, firm). Are there different pathways to performance for DMCs and CMCs, and if so, how are these influenced by external and internal contingencies?

Third, further research should extend the set of moderators influencing the interaction between DMCs and CMCs. In particular, we encourage scholars to investigate the role of more stable (e.g., functional, multidivisional structure) and more fluid (e.g., team, network structure) (Lee et al., 2015) organizational structures to provide insights into how different structural types and characteristics affect the deployment of DMCs and CMCs.

Fourth, we relied on a single key informant design and took great care to avoid potential key informant bias. Nevertheless, this research design may pose limitations. Thus, additional research using a secondary key informant design is desirable. Such a design may be particularly helpful in investigating potential thought-world differences or other sources of organizational tension between DMCs and CMCs (Leeflang et al., 2014).

Finally, although our data are not strictly cross-sectional, as we implemented a time lag between the primary and secondary data, future research could extend our findings by drawing on a longitudinal design. A promising avenue for future research is to examine how the performance effects of DMCs and CMCs develop over time and assess related contingencies that may amplify or inhibit their impact on firm performance. In this regard, we encourage scholars to consider the development and deployment costs of DMCs and CMCs and their impact on performance over time (Schilke et al., 2018).

Although managers are aware that digital technologies can change a firm’s value creation, roughly two out of three marketing executives still tend to focus on “managing the present” rather than “preparing for the future” (The CMO Survey, 2019). Our empirical findings offer important implications that may advance managerial thinking about DMCs and CMCs.

We clearly demonstrate the financial accountability of DMCs using a cross-industry sample and thus empower managers to justify digital marketing investments to senior management. In particular, the study results reveal that DMCs significantly contribute to accounting-based (e.g., ROA) and market-based (i.e., Tobin’s q) firm profitability metrics—over and above the impact of CMCs. Thus, not only do DMCs help firms more effectively deploy their resources and achieve higher profit levels, but they also seem to be perceived by investors as a strong signal that the firm is shifting to a forward-looking stance.

Regarding organizational contingencies, managers should be aware that their firms’ orientation constitutes a powerful steering instrument to attain possible joint financial returns from DMCs and CMCs. We find that a customer orientation enables firms to unlock the value-creating potential of DMCs and CMCs while a competitor orientation hampers their interaction effect and ultimately destroys financial value. With these divergent results on the contingent role of customer and competitor orientation, we confirm that “treating the concept of market orientation as an aggregate construct of equally important behavioral orientations can be misleading and limit its strategic value for management practice” (Lukas & Ferrell, 2000, p. 244). Regarding environmental contingencies, managers should recognize that the simultaneous deployment of DMCs and CMCs is particularly profitable for firms operating in rapidly changing environments. By contrast, in stable environments, the costs of maintaining strong DMCs and CMCs across a broad set of marketing activities seem unjustified.

Finally, the results from our post hoc analyses reveal that B2B firms profit less from the simultaneous deployment of DMCs and CMCs than B2C firms. We contend that these performance differences may occur for three reasons (Lilien, 2016): (1) because of the significantly smaller number of potential customers in B2B markets, B2B firms have a lower need for large-scale data analysis in the context of customer segmentation, targeting, and positioning based on DMCs; (2) B2B customers typically expect a significantly higher degree of personal interaction, which puts a premium on personal selling and limits the value potential of deploying DMCs on top of CMCs; and (3) B2B buying processes are highly complex and involve multiple decision-makers, which leads to higher marketing complexity. Another explanation for the performance differences between B2B and B2C firms may be that, in general, B2B firms have less digital marketing knowledge and skills than B2C firms (The CMO Survey, 2021). Furthermore, B2B firms are more likely than B2C firms to suffer from fragmented digital marketing strategies, confusion about ownership of digital roles, and lack of key performance metrics for digital initiatives (Harrison et al., 2017). Consequently, B2B marketing organizations may still be less able than their B2C counterparts to effectively integrate their digital and classic channels and touchpoints and provide customers with seamless experiences. Taken together, although DMCs can be a source of competitive advantage for both B2B and B2C firms—as shown by the positive and significant main effect of DMCs on firm profitability—firms operating in B2C markets may profit more from the simultaneous deployment of DMCs and CMCs.