{"paper_id":"32b51775-729e-4dca-8e9d-fa15f9d10acd","body_text":"Quality versus Quantity in Digital Marketing: Evidence from Italian Companies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Quality versus Quantity in Digital Marketing: Evidence from Italian Companies Ettore Battiloro, Massimiliano Giacalone, Gianfranco Piscopo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8407297/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The diffusion of digital technologies has reshaped marketing activities, fostering data-driven digital marketing strategies. However, empirical research often relies on descriptive indicators that fail to capture the latent and multidimensional nature of digital marketing adoption. This study addresses this limitation by proposing an integrated statistical framework for the analysis of digital marketing practices. The framework combines principal component analysis, cluster analysis and non-parametric combination (NPC) ranking to examine adoption patterns across firms and sectors over two time periods. The empirical results reveal persistent heterogeneity in digital marketing strategies and a gradual shift from tool-oriented adoption toward analytics-intensive and capability-based approaches. Although overall adoption increases over time, firms and sectors follow differentiated trajectories rather than converging toward a single model. Methodologically, the study highlights the benefits of integrating complementary multivariate techniques to enhance interpretability and robustness. From an applied perspective, the findings show how quantitative evidence can support strategic decision-making in digital marketing, bridging quality and quantity dimensions. Digital marketing Multivariate statistical analysis Principal component analysis Data-driven decision-making Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Digital transformation has emerged as one of the most influential processes shaping contemporary economic systems, profoundly altering organizational structures, production processes and competitive dynamics. Within this broad transformation, marketing activities have experienced a particularly significant evolution, driven by the diffusion of digital technologies and the exponential growth of data availability. As firms increasingly interact with customers through digital channels, marketing has progressively shifted from a predominantly communication-oriented function to a data-intensive and analytically driven strategic domain (Wedel & Kannan, 2016; Varadarajan, 2022). The increasing availability of digital traces generated by online interactions—such as website visits, social media engagement, e-commerce transactions and customer relationship management systems—has expanded firms’ potential to monitor, measure and influence consumer behavior in real time. This development has fostered the emergence of what is commonly referred to as data-driven digital marketing , a paradigm in which strategic and tactical decisions are informed by systematic data analysis rather than intuition or experience alone (Kumar et al., 2021). In this context, analytics capabilities are no longer considered auxiliary tools but core strategic resources that enable firms to personalize offerings, optimize channel selection and enhance customer engagement. Despite the widespread recognition of the strategic relevance of digital marketing, empirical evidence suggests that firms’ adoption of digital marketing practices remains highly heterogeneous. Previous research highlights substantial differences across firms and sectors in terms of technological intensity, analytical capabilities and strategic integration of digital tools (Hanelt et al., 2021; Kraus et al., 2022). Such heterogeneity indicates that digital marketing adoption is not a linear or uniform process but rather a complex phenomenon shaped by multiple interrelated dimensions. A growing body of literature has attempted to measure digital marketing adoption using descriptive indicators, such as the presence of corporate websites, the use of social media platforms or the implementation of e-commerce solutions (Tiago & Veríssimo, 2014). While these indicators provide useful information on the diffusion of digital tools, they suffer from important limitations. First, they fail to account for the multidimensional structure of digital marketing strategies, which encompass not only technological adoption but also organizational and analytical components. Second, descriptive measures are often unable to capture latent patterns and interdependencies among variables that may drive strategic outcomes. Recent reviews of the digital marketing and digital transformation literature emphasize the need for more rigorous quantitative approaches capable of addressing these limitations (Cioppi et al., 2023). In particular, scholars have called for the integration of multivariate statistical techniques that allow researchers to reduce complexity, identify latent constructs and classify firms into meaningful groups. However, despite the availability of well-established statistical tools, their application in digital marketing research remains relatively limited and fragmented. From a methodological standpoint, the underutilization of multivariate analysis represents a missed opportunity. Techniques such as principal component analysis (PCA), cluster analysis and non-parametric ranking procedures are particularly well suited to the analysis of complex, high-dimensional datasets typical of digital marketing contexts (Hair et al., 2019). When used in isolation, these methods provide valuable insights; when integrated within a coherent analytical framework, they have the potential to generate a structured and comprehensive understanding of digital marketing adoption patterns. Against this background, the present study aims to contribute to both digital marketing research and quantitative methodology by proposing an integrated statistical framework for the analysis of digital marketing adoption. Specifically, the study combines PCA, cluster analysis and the Non-Parametric Combination (NPC) test to investigate firms’ digital marketing practices across two distinct time periods. This longitudinal perspective enables the assessment of changes in latent structures, firm profiles and sectoral rankings over time. The contribution of this paper is twofold. From a methodological perspective, it demonstrates how complementary multivariate techniques can be jointly employed to enhance interpretability and robustness in the analysis of digital marketing phenomena. From an applied perspective, it shows how statistical evidence can be translated into actionable insights for digital marketing strategy, thereby reinforcing the role of statistics as a foundational component of data-driven marketing decision-making. The remainder of the paper is organized as follows. Section 2 describes the data and the statistical methods adopted. Section 3 presents the empirical applications and discusses the main results. Section 4 concludes with remarks and suggestions for future research. Section 5 outlines managerial implications, while Section 6 discusses the findings in relation to existing literature. 2. Theoretical background and literature review on digital marketing 2.1 Origins and conceptual evolution of digital marketing The concept of digital marketing originated in the academic and managerial literature during the 1990s, in conjunction with the diffusion of the Internet and the progressive digitalization of communication and exchange processes. Early contributions referred predominantly to internet marketing , online marketing or electronic marketing , emphasizing the use of digital channels as alternative means for promotion, distribution and transaction (Hoffman & Novak, 1996; Peterson, Balasubramanian & Bronnenberg, 1997). In this initial phase, digital marketing was largely conceptualized as an extension of traditional marketing activities into electronic environments, with a strong focus on efficiency gains, disintermediation and expanded market reach. The analytical emphasis was placed on firm-level adoption of websites, electronic marketplaces and e-commerce platforms, often framed within technology adoption and innovation diffusion theories (Rogers, 2003; Varadarajan & Yadav, 2002). During the early 2000s, the consolidation of web-based business models and the growing strategic relevance of digital channels contributed to a broader and more integrated understanding of digital marketing. Scholars increasingly recognized that digital technologies were not merely operational tools, but drivers of structural changes in customer relationships, information asymmetries and value creation mechanisms (Porter, 2001; Wind & Rangaswamy, 2001). As a result, digital marketing began to be framed as a strategic function embedded within firms’ overall competitive positioning. A further conceptual shift occurred with the diffusion of social media, mobile technologies and data analytics in the late 2000s and 2010s. In this phase, digital marketing came to be associated with interactivity, co-creation, personalization and real-time engagement (Kaplan & Haenlein, 2010; Kannan & Li, 2017). The increasing availability of customer data and analytical tools reinforced the view of digital marketing as a data-driven and capability-intensive domain, extending beyond the mere presence of digital touchpoints. 2.2 Main research streams in the digital marketing literature The growing body of literature on digital marketing can be organized into several interconnected research streams. A first stream focuses on digital channel adoption and diffusion , examining the determinants and patterns of firms’ use of websites, social media, e-commerce and digital communication tools. Studies in this area typically analyze the role of firm size, sectoral characteristics, technological readiness and organizational resources, often employing cross-sectional and comparative designs (Zhu & Kraemer, 2005; Trainor et al., 2014). A second stream investigates consumer behavior and digital interaction , emphasizing how digital environments reshape information search, decision-making processes and customer engagement. This literature highlights the active role of consumers in generating content, interacting with brands and influencing other users, thereby challenging traditional one-way communication models (Hennig-Thurau et al., 2010; Lemon & Verhoef, 2016). A third stream addresses the strategic and organizational implications of digital marketing. Contributions in this area stress that digital marketing effectiveness depends not only on the adoption of digital tools, but also on their integration within organizational structures, routines and capabilities. Digital marketing is thus increasingly linked to concepts such as market orientation, dynamic capabilities and strategic alignment (Day, 2011; Wedel & Kannan, 2016). More recent research has focused explicitly on data analytics and performance measurement in digital marketing. This line of inquiry underscores the growing importance of customer data, metrics and analytical models in guiding marketing decisions and evaluating outcomes. Scholars have pointed out that traditional performance indicators may be insufficient to capture the complexity of digital marketing activities and have called for more sophisticated, multidimensional measurement frameworks (Wedel & Kannan, 2016; Verhoef, Kooge & Walk, 2016). 2.3 Digital marketing between quantity and quality: measurement challenges Despite the richness of existing contributions, the literature reveals persistent challenges in the empirical measurement of digital marketing. A recurring limitation concerns the predominance of quantitative and tool-based indicators , such as the number of digital channels adopted or the presence of specific technologies. While these measures provide insights into the extent of digital adoption, they often overlook qualitative differences in strategic orientation, integration and analytical sophistication (Chaffey & Ellis-Chadwick, 2019). Recent studies have therefore emphasized the distinction between quantity and quality dimensions of digital marketing. Quantity-related aspects refer to the breadth and intensity of digital tool usage, whereas quality-related aspects capture the depth of integration, the use of analytics and the development of digital capabilities. However, empirical evidence on how these dimensions interact, evolve over time and differ across firms and sectors remains limited. Moreover, much of the empirical literature relies on single-method approaches and univariate indicators, which may obscure latent structures and heterogeneous adoption patterns. This issue is particularly relevant in longitudinal and comparative settings, where digital marketing trajectories may diverge rather than converge toward a homogeneous model. Against this background, the present study contributes to the literature by adopting a multivariate and longitudinal perspective that explicitly addresses the quality–quantity trade-off in digital marketing adoption. By integrating complementary statistical techniques, the analysis responds to recent methodological calls for more rigorous and multidimensional approaches to the empirical study of digital marketing practices. 3. MATERIALS AND METHODS 3.1 Data structure, variables and empirical setting The empirical analysis is based on secondary data describing firms’ adoption of digital technologies relevant to marketing activities. The dataset refers to two distinct time periods, corresponding to the years 2019 and 2023, and allows for a structured comparative analysis of digital marketing practices before and after a phase of accelerated digital transformation. The choice of these two observation points is methodologically motivated by the substantial changes in digital infrastructures, platform diffusion and analytics capabilities that have characterized the intervening period, making the temporal comparison particularly informative. The variables included in the analysis capture multiple and conceptually distinct dimensions of digital marketing adoption. Specifically, the dataset encompasses indicators related to: digital presence, such as the availability of corporate websites; digital interaction, including the use of social media and online communication channels; transactional capabilities, such as e-commerce adoption and online sales functionalities; and analytical support, referring to the use of data analytics tools and digital systems to inform marketing decisions. This selection reflects a deliberate conceptualization of digital marketing as a multidimensional construct, in which observable practices represent manifestations of deeper strategic and organizational orientations. Rather than treating digital adoption as a binary or additive phenomenon, the analysis assumes that different dimensions interact and jointly shape firms’ digital marketing profiles. From a statistical standpoint, the indicators exhibit substantial heterogeneity in scale, distribution and interdependence. For this reason, the analysis does not rely on univariate descriptive statistics as the primary inferential tool. Instead, it adopts a multivariate perspective aimed at uncovering latent structures and systematic patterns that are not directly observable from individual indicators. This approach is consistent with recent methodological recommendations in digital transformation research, which emphasize the need for analytical frameworks capable of capturing complexity and heterogeneity. 3.2 Integrated multivariate analytical framework To address the multidimensional and heterogeneous nature of digital marketing adoption, the study adopts an integrated multivariate analytical framework combining principal component analysis (PCA), hierarchical cluster analysis (HCA) and the Non-Parametric Combination (NPC) test and ranking procedure. These techniques are well established in the statistical literature as effective tools for dimensionality reduction, classification and synthesis of multivariate information (Backhaus et al., 2016; Hair et al., 2019; Pesarin & Salmaso, 2010). The rationale for integrating these methods lies in their complementary analytical roles. Principal component analysis is employed as an exploratory technique to reduce dimensionality and identify latent factors underlying the observed indicators. By transforming correlated variables into a reduced set of orthogonal components, PCA provides a parsimonious representation of the digital marketing space and facilitates the identification of underlying strategic dimensions (Jolliffe & Cadima, 2016). Formally, let \\(\\:\\mathbf{X}=\\left({x}_{ij}\\right)\\) denote the standardized data matrix, where \\(\\:i=1,\\dots\\:,n\\) indexes economic sectors and \\(\\:j=1,\\dots\\:,p\\) digital marketing indicators. PCA is based on the eigenvalue decomposition of the covariance (or correlation) matrix \\(\\:\\mathbf{S}\\) , obtained by solving: $$\\:\\mathbf{S}{\\mathbf{a}}_{k}={\\lambda\\:}_{k}{\\mathbf{a}}_{k},$$ where \\(\\:{\\lambda\\:}_{k}\\) denotes the eigenvalue associated with the \\(\\:k\\) -th principal component and \\(\\:{\\mathbf{a}}_{k}\\) the corresponding eigenvector. The score of the \\(\\:k\\) -th component for sector \\(\\:i\\) is given by: $$\\:{z}_{ik}=\\sum\\:_{j=1}^{p}{a}_{jk}{x}_{ij}.$$ The analysis is conducted separately for each time period in order to assess the stability and evolution of the latent structure over time. Component retention is guided by standard criteria, including eigenvalues and explained variance, while substantive interpretation is based on factor loadings and their relevance to digital marketing practices. This procedure allows for the identification of both stable and emerging dimensions of digital marketing adoption. Building on the PCA results, hierarchical cluster analysis is applied to classify economic sectors into homogeneous groups based on their scores on the principal components. Using component scores rather than original variables reduces noise and ensures that classification is driven by the most informative latent dimensions rather than by idiosyncratic variation (Hair et al., 2014). The clustering objective can be formalized in terms of within-cluster homogeneity as the minimization of total within-cluster variance: $$\\:\\text{m}\\text{i}\\text{n}\\sum\\:_{g=1}^{G}\\sum\\:_{i\\in\\:{C}_{g}}\\parallel\\:{\\mathbf{z}}_{i}-{\\varvec{\\mu\\:}}_{g}{\\parallel\\:}^{2},$$ where \\(\\:{C}_{g}\\) denotes cluster \\(\\:g\\) , \\(\\:{\\varvec{\\mu\\:}}_{g}\\) its centroid and \\(\\:{\\mathbf{z}}_{i}\\) the vector of principal component scores for sector \\(\\:i\\) . The resulting clusters represent distinct digital marketing profiles, reflecting different configurations and levels of digital adoption. Comparing clustering solutions across the two time periods enables the analysis of persistence, differentiation and structural change in digital marketing strategies. Finally, the Non-Parametric Combination (NPC) test and ranking procedure is employed to provide a synthetic and robust comparative assessment of digital marketing adoption across sectors. The NPC framework allows for the aggregation of information across multiple indicators and latent dimensions without imposing parametric assumptions on the underlying data-generating process, making it particularly suitable for heterogeneous and non-normal datasets (Pesarin & Salmaso, 2010; Pesarin et al., 2017). Let \\(\\:{T}_{h}\\) denote the partial test statistic associated with the \\(\\:h\\) -th indicator or dimension. These statistics are combined into a global statistic according to a suitable combining function \\(\\:\\varphi\\:(\\cdot\\:)\\) : $$\\:T=\\varphi\\:({T}_{1},{T}_{2},\\dots\\:,{T}_{H}).$$ The resulting NPC ranking provides a distribution-free ordering of sectors that accounts for the multidimensional nature of digital marketing adoption and complements the exploratory and classificatory analyses with a comparative perspective. Taken together, the integration of PCA, cluster analysis and NPC ranking constitutes a coherent analytical pipeline that moves systematically from dimensionality reduction to classification and comparative assessment. This unified framework enhances analytical rigor and interpretability and enables a structured examination of digital marketing adoption patterns that would not be achievable through isolated applications of individual methods. 4. Applications This section presents the empirical application of the integrated statistical framework described in Section 2 . The results are articulated according to the three analytical steps—principal component analysis, cluster analysis and NPC ranking—each contributing to a progressively structured interpretation of digital marketing adoption. Throughout the section, particular attention is devoted to linking statistical outcomes to substantive insights relevant for digital marketing research and practice. 4.1 Principal components and latent drivers of digital marketing adoption Principal component analysis (PCA) was applied separately to the two periods under investigation (2019 and 2023) with the objective of identifying the latent structure underlying digital marketing adoption and assessing its temporal evolution. Given the inherently multidimensional and interdependent nature of digital marketing indicators—spanning digital presence, transactional capabilities and analytics usage—PCA represents an appropriate and well-established method for dimensionality reduction and latent structure identification (Jolliffe & Cadima, 2016; Hair et al., 2019). 1. Explained variance of the first three principal components derived from PCA for 2019 and 2023. The analysis reveals a highly structured variance decomposition in both periods. As illustrated in Fig. 1, the first principal component explains a substantial proportion of total variance in both 2019 and 2023, capturing the dominant latent dimension of digital marketing adoption. The stability of the variance explained by this component across time provides strong empirical evidence for the existence of a persistent underlying factor characterizing firms’ overall engagement with digital marketing. Substantively, this dominant dimension can be interpreted as a general digital marketing intensity factor , reflecting the breadth and systematicity with which digital technologies are incorporated into marketing activities. Importantly, however, the comparative analysis also reveals significant structural change. While the first component remains predominant, Fig. 1 shows a non-negligible increase in the proportion of variance explained by the second and third components in 2023. From a statistical standpoint, this pattern indicates a reduction in redundancy among observed indicators and the emergence of additional latent dimensions that contribute meaningfully to explaining digital marketing behavior. From a substantive standpoint, it signals an increasing differentiation of digital marketing strategies over time. The second principal component is primarily associated with variables capturing transactional capabilities and the use of data analytics tools. High scores on this component characterize firms that integrate digital channels with analytical processes aimed at performance monitoring, targeting and strategic decision support. The increased explanatory power of this component in 2023 provides direct empirical support for the growing relevance of analytics-driven digital marketing, in line with the literature emphasizing the centrality of data analytics in contemporary marketing strategy (Wedel & Kannan, 2016; Kumar et al., 2021; Varadarajan, 2022). The third component, although accounting for a smaller share of variance, plays a crucial role in differentiating firms according to specific configurations of digital tool usage and integration. Its increased contribution over time further reinforces the interpretation of digital marketing adoption as an increasingly multidimensional phenomenon, in which firms combine digital presence, analytics and integration in heterogeneous ways rather than converging toward a uniform adoption pattern. Taken together, the PCA results provide robust empirical evidence that digital marketing adoption is structured around stable yet evolving latent drivers . The coexistence of a dominant general factor and increasingly relevant secondary dimensions reflects a transition from relatively simple, tool-oriented adoption toward more complex, capability-oriented digital marketing strategies. Methodologically, the clear variance structure reported in Fig. 1 justifies the use of PCA scores as inputs for subsequent analyses, as they provide a parsimonious and statistically sound representation of the underlying digital marketing space. 4.2 Cluster profiles and evolution of digital marketing strategies Building on the latent dimensions identified through PCA, hierarchical cluster analysis (HCA) was employed to classify economic sectors into homogeneous groups according to their digital marketing profiles. The clustering procedure was conducted using PCA scores rather than raw indicators, ensuring that classification was grounded in latent strategic orientations rather than in surface-level similarities. This approach is consistent with best practices in multivariate analysis, particularly in high-dimensional contexts characterized by strong correlations among variables (Backhaus et al., 2016; Hair et al., 2014). 2 Hierarchical clustering dendrogram of economic sectors based on PCA scores, 2019. As shown in Fig. 2, the hierarchical clustering solution for 2019 identifies a two-cluster structure at the selected cut-off level. The first cluster groups sectors characterized by lower scores on the principal components associated with digital marketing adoption. These sectors exhibit limited engagement with digital channels and, crucially, a weak integration of analytics-oriented practices, suggesting that digital marketing plays a marginal, fragmented or exploratory role within their strategic configurations. The second cluster comprises sectors displaying higher scores on the dominant latent dimensions of adoption, indicating a comparatively more structured digital marketing profile. In this group, the adoption of digital tools tends to be accompanied by greater alignment with integration- and analytics-related dimensions, pointing to a more capability-oriented configuration rather than a mere expansion of digital presence. This dichotomy is consistent with the view that early-stage digital marketing adoption is primarily driven by differences in the depth of digital integration and analytical capability, not only by the availability of visible digital touchpoints (Tiago & Veríssimo, 2014; Hofacker et al., 2020; Wedel & Kannan, 2016). Importantly, the two-cluster structure observed in 2019 provides evidence of a macro-segmentation between lower- and higher-adoption sectoral configurations. This segmentation constitutes a baseline against which the 2023 clustering structure can be evaluated in terms of continuity and increased differentiation. 3 Hierarchical clustering dendrogram of economic sectors based on PCA scores, 2023. The hierarchical clustering structure observed in 2023, reported in Fig. 3, exhibits a marked increase in internal differentiation with respect to the two-cluster configuration identified for 2019. While the earlier period was characterized by a broad dichotomy between lower- and higher-adoption sectors, the 2023 dendrogram reveals a more articulated cluster structure, indicating that digital marketing adoption has evolved from a relatively binary segmentation toward a more nuanced configuration. At the selected cut-off level, the clustering solution for 2023 identifies multiple clusters, reflecting differentiated combinations of digital presence, transactional capabilities and analytics integration. Importantly, this increased granularity does not imply a disruption of the underlying structure observed in 2019; rather, it represents a process of internal stratification within the group of higher-adoption sectors, while a cluster of sectors characterized by persistently low levels of digital marketing adoption continues to be observed. In particular, sectors that in 2019 belonged to the higher-adoption cluster become distributed across distinct clusters in 2023, indicating divergent evolutionary trajectories. Some sectors exhibit a clear transition toward analytics-intensive and highly integrated digital marketing configurations, characterized by strong alignment between digital channels and data-driven decision-making processes. Other sectors, while maintaining an expanded digital presence, remain relatively closer to communication-oriented or transaction-focused practices, displaying a more limited degree of analytical integration. This differentiation is consistent with the PCA results discussed in Section 3.1 , which show an increased explanatory relevance of secondary latent dimensions associated with analytics and integration in 2023. The clustering outcomes therefore provide a classificatory translation of the latent structural changes identified at the dimensional level, reinforcing the interpretation of digital marketing adoption as a capability-based and path-dependent process rather than as a uniform diffusion of tools. From a longitudinal perspective, the comparison between Figs. 2 and 3 highlights an important dynamic: while the boundary between low- and higher-adoption sectors remains structurally stable, the internal composition of the higher-adoption group becomes increasingly heterogeneous. This suggests that digital marketing transformation does not eliminate disparities but instead redistributes them along more refined strategic dimensions, amplifying differentiation among sectors that have already crossed a minimum threshold of digital adoption. From a methodological standpoint, the emergence of additional clusters in 2023 confirms the suitability of PCA-based hierarchical clustering for capturing temporal evolution in complex, high-dimensional contexts. The results indicate that the clustering procedure is sensitive not only to cross-sectional differences but also to structural changes in the latent configuration of digital marketing practices over time. Overall, the 2023 clustering results provide strong empirical evidence that digital marketing adoption evolves through progressive differentiation rather than convergence. The transition from a two-cluster structure in 2019 to a more articulated clustering configuration in 2023 underscores the increasing strategic importance of analytics integration as a discriminating factor and highlights the need for analytical frameworks capable of capturing such nuanced dynamics. 4.3 NPC ranking and comparative sectoral assessment To complement the exploratory and classificatory analyses developed in the previous subsections, a non-parametric combination (NPC) ranking procedure was applied in order to obtain a synthetic and robust comparative assessment of digital marketing adoption across economic sectors. The NPC framework is particularly suited to this context, as it allows the aggregation of information across multiple indicators and latent dimensions without imposing restrictive distributional assumptions or requiring arbitrary weighting schemes (Pesarin & Salmaso, 2010; Pesarin et al., 2017). This feature is especially relevant in digital marketing research, where data are often heterogeneous, skewed and characterized by complex interdependencies. The adoption of an NPC-based ranking responds to a key analytical need: moving beyond isolated or dimension-specific comparisons toward an integrated evaluation of relative positioning. While PCA and cluster analysis provide insights into latent structures and strategic archetypes, they do not directly offer a synthetic measure of comparative performance. The NPC ranking fills this gap by combining partial pieces of evidence into a global ordering that reflects the overall configuration of digital marketing adoption. 4 Non-parametric combination (NPC) ranking of economic sectors based on multidimensional digital marketing adoption indicators, 2019 and 2023. As shown in Fig. 4, the NPC ranking reveals pronounced heterogeneity in digital marketing adoption across economic sectors in both periods considered. In 2019, sectors characterized by high information intensity, frequent digital interaction with customers and greater reliance on data-driven processes systematically occupy the highest positions in the ranking. These sectors display structured digital marketing configurations in which diversified digital channels are complemented by analytical integration and performance monitoring routines. In contrast, sectors positioned at the lower end of the ranking rely predominantly on fragmented or basic digital tools, exhibiting limited analytical maturity and weak integration between digital presence and strategic decision-making. The comparison between 2019 and 2023 highlights a general upward shift in NPC scores, indicating that digital marketing adoption has increased across sectors. This result reflects the diffusion of digital technologies and analytics capabilities and confirms the dynamic nature of digital marketing transformation. Importantly, however, this overall improvement does not translate into a substantial reshuffling of relative positions. Sectors that ranked highly in 2019 tend to preserve their advantage in 2023, while sectors at the bottom of the ranking display more limited progress. This persistence of relative ordering provides strong empirical evidence of path dependence in digital marketing adoption and underscores the role of structural and contextual factors—such as sectoral business models, technological requirements and skill endowments—in shaping digital transformation trajectories (Hanelt et al., 2021; Kraus et al., 2022). At the same time, the ranking reveals non-negligible cases of upward mobility. A subset of sectors improves its relative position between the two periods, signaling successful transitions toward more analytics-intensive and integrated digital marketing strategies. These movements are not random but appear systematically associated with improvements along the latent dimensions identified by PCA and with transitions toward more advanced cluster profiles. From a substantive perspective, this evidence reinforces the argument that investments in data analytics capabilities, integration of digital tools and cross-functional coordination represent critical levers for advancing digital marketing maturity (Wedel & Kannan, 2016; Kumar et al., 2021). From a methodological standpoint, the NPC ranking represents a crucial complement to PCA and cluster analysis. While PCA uncovers latent drivers and cluster analysis translates them into discrete strategic configurations, the NPC ranking provides a continuous and interpretable measure of relative sectoral performance. The integration of these techniques results in a coherent analytical pipeline in which exploratory, classificatory and comparative perspectives mutually reinforce one another, enhancing both robustness and interpretability of the empirical findings. 4.4 Integrative interpretation and implications for digital marketing analysis Taken together, the results of the three analytical steps provide a comprehensive and internally consistent representation of digital marketing adoption as a structured, heterogeneous and evolving phenomenon. Each method contributes a distinct layer of insight: PCA identifies the latent dimensions underlying observed indicators, cluster analysis reveals discrete strategic profiles based on these dimensions, and NPC ranking synthesizes multidimensional information into a comparative assessment of relative positioning. From a methodological perspective, this integrative approach demonstrates the value of multivariate statistical analysis for addressing the intrinsic complexity of digital marketing phenomena. By moving beyond descriptive indicators and single-method analyses, the proposed framework captures both structural regularities—such as the persistence of dominant latent drivers—and differentiated trajectories, reflected in cluster stratification and ranking mobility. This layered analytical design mitigates the risk of oversimplification and allows for a more nuanced interpretation of digital marketing adoption dynamics. From a substantive perspective, the results consistently underscore the central role of analytics and integration in contemporary digital marketing strategies. Firms and sectors that successfully embed data analytics into their marketing processes exhibit more advanced, coherent and strategically oriented digital profiles. Conversely, reliance on basic digital presence without analytical integration is systematically associated with lower-value configurations and persistent disadvantage. These findings reinforce the view that digital marketing effectiveness increasingly depends on the development of analytical capabilities rather than on the mere expansion of digital touchpoints. More broadly, the integrative interpretation highlights that digital marketing adoption cannot be understood as a linear or uniform process. Instead, it emerges as a capability-based and path-dependent transformation, characterized by persistent heterogeneity and selective upgrading. In this sense, the statistical framework does not merely describe digital marketing practices but contributes to their conceptualization, showing how quantitative analysis can illuminate the structural mechanisms underlying digital transformation. Overall, the combined evidence provided by PCA, cluster analysis and NPC ranking supports the argument that statistics and data analytics constitute foundational components of effective digital marketing strategy. By enabling the identification of latent drivers, strategic profiles and relative positioning, the proposed framework illustrates how advanced statistical methods can enhance both empirical understanding and analytical rigor in digital marketing research. 5. Concluding remarks and suggestions for future research 5.1 concluding remarks and contributions This study provides empirical and methodological evidence supporting the interpretation of digital marketing adoption as a complex, multidimensional and heterogeneous phenomenon that cannot be adequately captured through isolated indicators or purely descriptive approaches. By adopting an integrated statistical framework combining principal component analysis, hierarchical cluster analysis and non-parametric combination (NPC) ranking, the paper offers a structured and rigorous assessment of digital marketing practices and their evolution over time. From an empirical standpoint, the results demonstrate that digital marketing adoption is primarily driven by latent dimensions reflecting distinct strategic orientations rather than by the mere presence of individual digital tools. In particular, the empirical distinction between components associated with basic digital presence and those related to analytics intensity and integration highlights a progressive shift from tool-based adoption toward capability-based digital marketing strategies. This evidence is consistent with recent theoretical contributions emphasizing that digital marketing maturity increasingly depends on firms’ analytical capabilities and their ability to transform data into actionable strategic insights (Wedel & Kannan, 2016; Varadarajan, 2022). The longitudinal comparison further reveals that, despite a general increase in the adoption of digital marketing practices, the process remains characterized by persistent heterogeneity across firms and sectors. Rather than converging toward a single dominant model, digital marketing strategies evolve along differentiated trajectories shaped by structural, organizational and sector-specific factors. This finding reinforces the view that digital transformation—and digital marketing in particular—should be interpreted as a non-linear and path-dependent process, in line with broader evidence in the digital transformation literature (Hanelt et al., 2021; Kraus et al., 2022). From a methodological perspective, the study contributes to the quantitative analysis of digital marketing by empirically validating an integrated multivariate framework tailored to high-dimensional and heterogeneous data. While techniques such as PCA and cluster analysis are well established in the statistical literature, their joint application in digital marketing research has remained relatively fragmented. By explicitly integrating these methods with a non-parametric ranking procedure, the paper demonstrates how complementary statistical tools can be orchestrated within a coherent analytical design to address dimensionality reduction, classification and comparative assessment simultaneously. This contribution is particularly aligned with the aims of Quality & Quantity , as it illustrates how advanced statistical methods can be applied to substantively rich phenomena without compromising interpretability. Overall, the findings underscore that digital marketing adoption should not be conceptualized as a binary or uniform process, but rather as a continuum shaped by multiple interrelated dimensions. This insight challenges simplified representations of digitalization and supports the adoption of more nuanced and statistically grounded analytical approaches in both academic research and policy-oriented evaluations. 5.2 Directions for future research While the study provides several contributions, it also opens up multiple avenues for future research. First, the proposed framework could be extended by incorporating firm-level performance indicators, such as sales growth, profitability or customer engagement metrics. Linking digital marketing adoption profiles to performance outcomes would enable a more direct assessment of the strategic effectiveness of different digital configurations and strengthen the connection between statistical evidence and managerial relevance. Second, future research could apply the framework to different geographical or institutional contexts. Cross-country or cross-regional analyses would allow researchers to investigate how institutional environments, regulatory frameworks and cultural factors influence digital marketing adoption patterns, thereby enhancing the external validity and generalizability of the results. Third, the integration of the current multivariate framework with predictive and machine learning techniques represents a promising direction for methodological development. While the present study focuses on exploratory and classificatory analysis, combining these approaches with predictive models could support forecasting, scenario analysis and decision-support applications, further expanding the contribution of statistical methods to digital marketing research. Finally, longitudinal designs incorporating additional observation points would allow for a more refined analysis of transition dynamics between digital marketing profiles. Such extensions would provide insights into the stability, persistence or volatility of digital marketing strategies over time and contribute to a deeper understanding of the dynamics underlying digital transformation processes. 6. Management implications 6.1 statistical evidence as an operational backbone for digital marketing strategy The empirical evidence presented in Section 3 indicates that digital marketing adoption should be modelled and managed as a multidimensional configuration of interdependent practices rather than as the additive outcome of isolated tools (e.g., “having a website” or “being active on social media”). This has direct managerial consequences: in complex digital environments, single indicators provide only partial—and potentially misleading—signals, because they ignore latent complementarities among technologies, organizational routines and analytics capabilities. A strategy premised on isolated metrics risks overestimating digital maturity and underestimating capability gaps. The integrated statistical framework adopted in this study provides a formally grounded pathway for converting heterogeneous digital marketing signals into structured strategic knowledge. PCA yields a parsimonious representation of the digital marketing space by extracting orthogonal latent drivers, cluster analysis maps these drivers into a discrete set of interpretable strategic archetypes, and NPC ranking provides a distribution-free comparative synthesis at the sectoral level. In combination, these outputs can be used as a decision-support system enabling firms to shift from descriptive monitoring to diagnostic and prescriptive reasoning (Wedel & Kannan, 2016; Hanssens, 2019). A first managerial implication concerns strategic prioritisation . The variance structure observed in the PCA results (Section 3.1 ) suggests that a dominant latent dimension captures a general intensity factor, while secondary components increasingly reflect differentiation related to analytics and integration. In practice, this implies that digital marketing “scale” (presence across channels) and digital marketing “capability” (analytics-driven integration, measurement routines, cross-functional coordination) are not equivalent. Consequently, investment priorities should be aligned with the most discriminating latent dimensions, i.e., those associated with analytical maturity and integration rather than with basic channel adoption. This is consistent with the view that competitive advantage in digital marketing is progressively determined by data-driven capability building and by the transformation of data into actionable insights (Varadarajan, 2022; Kumar et al., 2021). A second implication concerns resource allocation under constraint . The empirical structure supports a staged investment logic: (i) foundational digital presence (necessary but not sufficient), (ii) integration of transactional and customer-interaction capabilities, and (iii) institutionalisation of analytics routines (measurement systems, attribution logic, experimentation protocols, dashboard governance). In this framework, spending to expand the number of channels without a commensurate increase in analytics capability may generate “digital surface area” without strategic control—an outcome that typically increases operational complexity and decreases decision quality. By contrast, investments in analytics infrastructure and measurement discipline increase the marginal return of existing digital channels by improving targeting, personalization and performance management (Wedel & Kannan, 2016; Hanssens, 2019). Third, the cluster analysis provides an immediately actionable managerial artefact: cluster membership as strategic diagnosis . A firm (or sector) can interpret its cluster position as a “maturity state,” and the distance to more advanced clusters as an estimate of the capability gap. Importantly, this is not a generic maturity model: the clusters are empirically derived from multivariate structure rather than imposed a priori. For firms in low- or intermediate-adoption clusters, the managerial use is twofold: (a) benchmarking against peer groups with comparable structural constraints; and (b) designing targeted transition strategies to move toward analytics-intensive profiles. This transition is not merely about acquiring tools; it is about establishing governance mechanisms for data quality, analytical workflows and decision rights, consistent with the capability-based interpretation of digital marketing maturity (Varadarajan, 2022). Finally, the integrated pipeline supports strategic monitoring over time . Changes in PCA scores provide continuous signals on latent drivers, cluster transitions indicate discrete strategic shifts, and NPC position changes capture relative performance in a competitive landscape. This multi-layer monitoring architecture reduces the risk of “metric gaming” and enhances interpretability: firms can explain why a ranking improved (latent driver changes), how strategic profile shifted (cluster migration), and whether improvement is meaningful in relative terms (NPC movement). In short, statistics function as an operational backbone for strategic control in digital marketing, rather than as an ex-post analytical add-on. 6.2 Governance architecture, benchmarking regimes, and policy-relevant implications Beyond strategic prioritisation, the findings carry implications for the governance and organisational design of digital marketing, with specific attention to how firms structure decision rights, coordinate functions and institutionalise analytics capabilities. The emergence of analytics-related latent dimensions indicates that digital marketing effectiveness depends on organisational routines and cross-functional integration, not only on technology adoption. In governance terms, this implies that digitally advanced profiles are likely associated with tighter coupling among marketing, IT, data engineering and analytics units, enabling faster data-to-decision cycles and more consistent performance accountability. A key implication is that firms should move from tool governance to capability governance . Tool governance focuses on channel-level outputs (e.g., content frequency, traffic, clicks), whereas capability governance focuses on measurement quality, analytical validity and decision traceability. In practice, this shift requires: (i) data governance policies (definitions, quality rules, lineage), (ii) analytics governance (model validation, KPI hierarchies, attribution assumptions), and (iii) decision governance (who decides, on which evidence, with what review cadence). Within the logic of the proposed framework, governance maturity can be proxied by the relative weight of analytics/integration dimensions in PCA scores and by membership in analytics-intensive clusters. The NPC ranking provides a robust benchmarking regime that is particularly valuable for governance because it synthesises multidimensional evidence without strong parametric assumptions and without requiring arbitrary weights. For managers, NPC-based benchmarking supports a “relative competitiveness” lens: performance is assessed in relation to the evolving distribution of peers rather than against static thresholds. This is crucial in rapidly changing digital environments, where absolute benchmarks can become obsolete. Moreover, NPC rankings can be embedded in periodic strategic reviews to evaluate whether the firm’s digital marketing transformation is progressing faster, slower or in line with its reference group. At the sectoral level, the ranking results can inform competitive intelligence and strategic positioning choices. Firms in lower-ranked sectors may face structural constraints (skills availability, technology diffusion, regulatory frictions, business model rigidity) that limit the pace of adoption. In such cases, the managerial implication is to combine internal capability building with ecosystem strategies, such as partnerships with data providers, platform actors or specialised analytics vendors, to reduce fixed costs and accelerate learning curves. Conversely, firms in higher-ranked sectors can use ranking stability as evidence of sustained advantage but should also monitor internal differentiation (cluster sub-structures) to avoid complacency and to anticipate strategic shifts among close competitors. The results also support policy-relevant implications. Persistent heterogeneity suggests that “uniform” digital transformation policies may have limited effectiveness. Institutions can leverage the integrated statistical framework as an evaluative instrument to identify where marginal public support is likely to yield the highest returns. For example, sectors persistently located in low-adoption clusters and at the bottom of NPC rankings may require targeted interventions focused on analytics skills, data infrastructure access, or incentives for experimentation and measurement adoption. Conversely, sectors with intermediate profiles might benefit more from programs aimed at integration (interoperability, standardisation, shared data platforms) rather than from generic technology adoption subsidies. Importantly, the proposed framework can be operationalised as a policy monitoring dashboard . PCA trends provide signals of structural shifts in adoption drivers, cluster composition changes indicate whether heterogeneity is shrinking or expanding, and NPC ranking mobility provides a direct measure of sectoral catching-up or divergence. This monitoring logic reduces informational asymmetries in policy evaluation and enables adaptive interventions, i.e., policies that evolve in response to statistically grounded evidence rather than to ex-post narrative assessments. Overall, the management implications converge on a central point: the strategic value of digital marketing increasingly depends on the statistical capacity to (i) diagnose latent capability structures, (ii) classify strategic profiles, and (iii) benchmark relative positioning under uncertainty. In this sense, multivariate analysis and non-parametric ranking are not merely research techniques but components of an actionable governance toolkit for data-driven digital marketing (Wedel & Kannan, 2016; Hanssens, 2019). 7. Discussion 7.1 digital marketing adoption as a multidimensional, latent and capability-based phenomenon The empirical findings of this study provide robust support for the interpretation of digital marketing adoption as a multidimensional and latent phenomenon, whose underlying structure cannot be adequately inferred from observable practices alone. This perspective aligns with recent strands of the digital transformation and digital marketing literature, which increasingly conceptualize digital adoption as the outcome of interdependent technological, organizational and analytical dimensions rather than as a linear accumulation of tools (Hanelt et al., 2021; Cioppi et al., 2023). The principal component analysis offers clear empirical evidence that commonly used indicators—such as the presence of corporate websites, social media usage or e-commerce functionalities—capture only partial and surface-level aspects of digital marketing adoption. The identification of latent components associated with analytics intensity, integration and measurement routines indicates that digital marketing maturity is progressively defined by firms’ ability to generate, process and strategically exploit data. This finding empirically substantiates the distinction between tool-based and capability-based digital marketing strategies advanced in the marketing analytics literature (Wedel & Kannan, 2016; Varadarajan, 2022). Importantly, the temporal comparison reveals that while a dominant latent dimension persists across periods, secondary dimensions gain explanatory relevance over time. This pattern suggests that digital marketing adoption does not merely scale up in intensity but becomes structurally more differentiated. In theoretical terms, this evolution supports a configurational interpretation of digital marketing, in which maturity is associated not only with the breadth of adoption but also with the coherence and complementarity among digital tools, analytics capabilities and organizational routines. The persistence of latent structures across time further implies that digital marketing adoption follows systematic and path-dependent trajectories. Rather than being driven by short-term experimentation or opportunistic responses to technological change, digital marketing strategies appear anchored to deeper organizational orientations and capabilities. This insight challenges diffusion-based models of digitalization and reinforces the relevance of capability-based and resource-based perspectives in explaining digital marketing evolution. 7.2 Heterogeneity, strategic archetypes and differentiated trajectories across firms and sectors A second major contribution of the study lies in its empirical documentation of persistent heterogeneity in digital marketing adoption across firms and sectors. The cluster analysis reveals that economic actors are distributed across distinct strategic archetypes characterized by different configurations of digital presence, analytics usage and integration intensity. This finding stands in contrast to narratives of inevitable convergence toward a homogeneous digital marketing model. The identification of clusters corresponding to marginal, transitional and analytics-intensive digital marketing strategies provides a structured interpretation of adoption dynamics. In particular, the persistence of low-adoption clusters over time suggests that digital transformation processes may exacerbate structural disparities rather than reduce them. Firms and sectors endowed with stronger analytical capabilities and organizational readiness are better positioned to internalize the strategic value of digital marketing, while others remain confined to basic or fragmented configurations. This evidence is consistent with the broader digital transformation literature, which highlights differentiated and path-dependent trajectories shaped by sectoral characteristics, organizational resources and institutional environments (Kraus et al., 2022). However, the present study advances this literature by offering a statistically grounded classification based on latent dimensions rather than on qualitative typologies or descriptive categorizations. By relying on PCA-derived component scores, the cluster analysis ensures that strategic archetypes reflect underlying capability structures rather than superficial similarities. The NPC ranking analysis further enriches this interpretation by providing a macro-level comparative perspective. While overall digital marketing adoption increases over time, relative sectoral positions remain largely stable, indicating that early adopters tend to preserve their advantage. This stability suggests that digital marketing capabilities exhibit cumulative properties, reinforcing existing competitive hierarchies rather than dissolving them. Taken together, the cluster and ranking results support a multilevel interpretation of digital marketing adoption. At the micro level, firms adopt differentiated strategic configurations; at the meso and macro levels, sectoral structures shape and constrain the distribution of digital marketing capabilities. This multilevel perspective contributes to a more comprehensive understanding of digital marketing dynamics and highlights the limitations of analyses that focus exclusively on either firm-level or sector-level determinants. 7.3 Methodological integration, theory-building and limitations Beyond its substantive findings, the study makes a significant methodological contribution by demonstrating the analytical value of integrating complementary statistical techniques within a unified framework. While principal component analysis, cluster analysis and non-parametric ranking methods are individually well established, their combined application in digital marketing research remains limited. The present study shows that such integration enhances both analytical depth and interpretability. PCA provides a parsimonious representation of complex, high-dimensional datasets by uncovering latent drivers of digital marketing adoption. Cluster analysis translates these latent dimensions into discrete and interpretable strategic archetypes, facilitating the identification of heterogeneous configurations. The NPC ranking synthesizes multidimensional information into a robust comparative assessment that avoids strong distributional assumptions and arbitrary weighting schemes. Together, these methods form a coherent analytical pipeline that moves systematically from data reduction to classification and comparison. From a theoretical standpoint, the findings illustrate that statistical analysis can actively contribute to theory development rather than merely serving as a tool for empirical validation. By revealing latent structures and systematic patterns, multivariate analysis informs the conceptualization of digital marketing adoption as a structured, differentiated and capability-based process. This perspective reinforces the reciprocal relationship between quantitative methods and substantive theory, in line with the interdisciplinary mission of Quality & Quantity . Several limitations should nevertheless be acknowledged. The reliance on secondary data restricts the ability to capture qualitative dimensions of digital marketing strategies, such as managerial cognition, organizational culture or decision-making routines. Moreover, the sectoral and contextual focus of the analysis limits the generalizability of the results. These limitations point to promising avenues for future research, including the integration of qualitative evidence with multivariate statistical analysis, cross-country comparative studies and the extension of the framework toward predictive and longitudinal designs capable of capturing transition dynamics more explicitly. Overall, the discussion reinforces the central argument of the paper: understanding digital marketing adoption requires analytically rigorous and theoretically informed approaches capable of capturing multidimensionality, heterogeneity and temporal dynamics. By integrating advanced statistical techniques with substantive interpretation, the study contributes to bridging quantitative methodology and digital marketing theory, offering insights that are both methodologically robust and conceptually meaningful. Declarations Author Contribution E.B., M.G., G.P. contributed equally to:conceptualization;methodology;data curation;formal analysis;investigation;writing;review and editing;visualization;supervision;validation of the study.All authors have read and agreed to the published version of the manuscript. References Backhaus, K., Erichson, B., Plinke, W., & Weiber, R. (2016). Multivariate Analysis: An Application-Oriented Introduction . Springer. Berman, S. J. (2012). 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3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":92367,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eHierarchical clustering dendrogram of economic sectors based on PCA scores, 2023.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8407297/v1/417adaeedadc06fba3a8af0d.png\"},{\"id\":99311564,\"identity\":\"45026e28-0a53-4599-aadf-264e5c56faa8\",\"added_by\":\"auto\",\"created_at\":\"2025-12-31 16:16:06\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":201603,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eNon-parametric combination (NPC) ranking of economic sectors based on multidimensional digital marketing adoption indicators, 2019 and 2023.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8407297/v1/0945b481ba74654076ce06ee.png\"},{\"id\":104117291,\"identity\":\"749276fc-7c90-46ea-bb3f-0fc81f994a63\",\"added_by\":\"auto\",\"created_at\":\"2026-03-07 05:54:00\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1648717,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8407297/v1/f2aca3cf-d7c3-43c9-a7d5-9723a46ce460.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Quality versus Quantity in Digital Marketing: Evidence from Italian Companies\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eDigital transformation has emerged as one of the most influential processes shaping contemporary economic systems, profoundly altering organizational structures, production processes and competitive dynamics. Within this broad transformation, marketing activities have experienced a particularly significant evolution, driven by the diffusion of digital technologies and the exponential growth of data availability. As firms increasingly interact with customers through digital channels, marketing has progressively shifted from a predominantly communication-oriented function to a data-intensive and analytically driven strategic domain (Wedel \\u0026amp; Kannan, 2016; Varadarajan, 2022).\\u003c/p\\u003e \\u003cp\\u003eThe increasing availability of digital traces generated by online interactions\\u0026mdash;such as website visits, social media engagement, e-commerce transactions and customer relationship management systems\\u0026mdash;has expanded firms\\u0026rsquo; potential to monitor, measure and influence consumer behavior in real time. This development has fostered the emergence of what is commonly referred to as \\u003cem\\u003edata-driven digital marketing\\u003c/em\\u003e, a paradigm in which strategic and tactical decisions are informed by systematic data analysis rather than intuition or experience alone (Kumar et al., 2021). In this context, analytics capabilities are no longer considered auxiliary tools but core strategic resources that enable firms to personalize offerings, optimize channel selection and enhance customer engagement.\\u003c/p\\u003e \\u003cp\\u003eDespite the widespread recognition of the strategic relevance of digital marketing, empirical evidence suggests that firms\\u0026rsquo; adoption of digital marketing practices remains highly heterogeneous. Previous research highlights substantial differences across firms and sectors in terms of technological intensity, analytical capabilities and strategic integration of digital tools (Hanelt et al., 2021; Kraus et al., 2022). Such heterogeneity indicates that digital marketing adoption is not a linear or uniform process but rather a complex phenomenon shaped by multiple interrelated dimensions.\\u003c/p\\u003e \\u003cp\\u003eA growing body of literature has attempted to measure digital marketing adoption using descriptive indicators, such as the presence of corporate websites, the use of social media platforms or the implementation of e-commerce solutions (Tiago \\u0026amp; Ver\\u0026iacute;ssimo, 2014). While these indicators provide useful information on the diffusion of digital tools, they suffer from important limitations. First, they fail to account for the multidimensional structure of digital marketing strategies, which encompass not only technological adoption but also organizational and analytical components. Second, descriptive measures are often unable to capture latent patterns and interdependencies among variables that may drive strategic outcomes.\\u003c/p\\u003e \\u003cp\\u003eRecent reviews of the digital marketing and digital transformation literature emphasize the need for more rigorous quantitative approaches capable of addressing these limitations (Cioppi et al., 2023). In particular, scholars have called for the integration of multivariate statistical techniques that allow researchers to reduce complexity, identify latent constructs and classify firms into meaningful groups. However, despite the availability of well-established statistical tools, their application in digital marketing research remains relatively limited and fragmented.\\u003c/p\\u003e \\u003cp\\u003eFrom a methodological standpoint, the underutilization of multivariate analysis represents a missed opportunity. Techniques such as principal component analysis (PCA), cluster analysis and non-parametric ranking procedures are particularly well suited to the analysis of complex, high-dimensional datasets typical of digital marketing contexts (Hair et al., 2019). When used in isolation, these methods provide valuable insights; when integrated within a coherent analytical framework, they have the potential to generate a structured and comprehensive understanding of digital marketing adoption patterns.\\u003c/p\\u003e \\u003cp\\u003eAgainst this background, the present study aims to contribute to both digital marketing research and quantitative methodology by proposing an integrated statistical framework for the analysis of digital marketing adoption. Specifically, the study combines PCA, cluster analysis and the Non-Parametric Combination (NPC) test to investigate firms\\u0026rsquo; digital marketing practices across two distinct time periods. This longitudinal perspective enables the assessment of changes in latent structures, firm profiles and sectoral rankings over time.\\u003c/p\\u003e \\u003cp\\u003eThe contribution of this paper is twofold. From a methodological perspective, it demonstrates how complementary multivariate techniques can be jointly employed to enhance interpretability and robustness in the analysis of digital marketing phenomena. From an applied perspective, it shows how statistical evidence can be translated into actionable insights for digital marketing strategy, thereby reinforcing the role of statistics as a foundational component of data-driven marketing decision-making.\\u003c/p\\u003e \\u003cp\\u003eThe remainder of the paper is organized as follows. Section \\u003cspan refid=\\\"Sec2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e describes the data and the statistical methods adopted. Section \\u003cspan refid=\\\"Sec6\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e presents the empirical applications and discusses the main results. Section \\u003cspan refid=\\\"Sec9\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e concludes with remarks and suggestions for future research. Section \\u003cspan refid=\\\"Sec17\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e outlines managerial implications, while Section \\u003cspan refid=\\\"Sec20\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e discusses the findings in relation to existing literature.\\u003c/p\\u003e\"},{\"header\":\"2. Theoretical background and literature review on digital marketing\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1 Origins and conceptual evolution of digital marketing\\u003c/h2\\u003e \\u003cp\\u003eThe concept of \\u003cem\\u003edigital marketing\\u003c/em\\u003e originated in the academic and managerial literature during the 1990s, in conjunction with the diffusion of the Internet and the progressive digitalization of communication and exchange processes. Early contributions referred predominantly to \\u003cem\\u003einternet marketing\\u003c/em\\u003e, \\u003cem\\u003eonline marketing\\u003c/em\\u003e or \\u003cem\\u003eelectronic marketing\\u003c/em\\u003e, emphasizing the use of digital channels as alternative means for promotion, distribution and transaction (Hoffman \\u0026amp; Novak, 1996; Peterson, Balasubramanian \\u0026amp; Bronnenberg, 1997).\\u003c/p\\u003e \\u003cp\\u003eIn this initial phase, digital marketing was largely conceptualized as an extension of traditional marketing activities into electronic environments, with a strong focus on efficiency gains, disintermediation and expanded market reach. The analytical emphasis was placed on firm-level adoption of websites, electronic marketplaces and e-commerce platforms, often framed within technology adoption and innovation diffusion theories (Rogers, 2003; Varadarajan \\u0026amp; Yadav, 2002).\\u003c/p\\u003e \\u003cp\\u003eDuring the early 2000s, the consolidation of web-based business models and the growing strategic relevance of digital channels contributed to a broader and more integrated understanding of digital marketing. Scholars increasingly recognized that digital technologies were not merely operational tools, but drivers of structural changes in customer relationships, information asymmetries and value creation mechanisms (Porter, 2001; Wind \\u0026amp; Rangaswamy, 2001). As a result, digital marketing began to be framed as a strategic function embedded within firms\\u0026rsquo; overall competitive positioning.\\u003c/p\\u003e \\u003cp\\u003eA further conceptual shift occurred with the diffusion of social media, mobile technologies and data analytics in the late 2000s and 2010s. In this phase, digital marketing came to be associated with interactivity, co-creation, personalization and real-time engagement (Kaplan \\u0026amp; Haenlein, 2010; Kannan \\u0026amp; Li, 2017). The increasing availability of customer data and analytical tools reinforced the view of digital marketing as a data-driven and capability-intensive domain, extending beyond the mere presence of digital touchpoints.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2 Main research streams in the digital marketing literature\\u003c/h2\\u003e \\u003cp\\u003eThe growing body of literature on digital marketing can be organized into several interconnected research streams. A first stream focuses on \\u003cb\\u003edigital channel adoption and diffusion\\u003c/b\\u003e, examining the determinants and patterns of firms\\u0026rsquo; use of websites, social media, e-commerce and digital communication tools. Studies in this area typically analyze the role of firm size, sectoral characteristics, technological readiness and organizational resources, often employing cross-sectional and comparative designs (Zhu \\u0026amp; Kraemer, 2005; Trainor et al., 2014).\\u003c/p\\u003e \\u003cp\\u003eA second stream investigates \\u003cb\\u003econsumer behavior and digital interaction\\u003c/b\\u003e, emphasizing how digital environments reshape information search, decision-making processes and customer engagement. This literature highlights the active role of consumers in generating content, interacting with brands and influencing other users, thereby challenging traditional one-way communication models (Hennig-Thurau et al., 2010; Lemon \\u0026amp; Verhoef, 2016).\\u003c/p\\u003e \\u003cp\\u003eA third stream addresses the \\u003cb\\u003estrategic and organizational implications\\u003c/b\\u003e of digital marketing. Contributions in this area stress that digital marketing effectiveness depends not only on the adoption of digital tools, but also on their integration within organizational structures, routines and capabilities. Digital marketing is thus increasingly linked to concepts such as market orientation, dynamic capabilities and strategic alignment (Day, 2011; Wedel \\u0026amp; Kannan, 2016).\\u003c/p\\u003e \\u003cp\\u003eMore recent research has focused explicitly on \\u003cb\\u003edata analytics and performance measurement\\u003c/b\\u003e in digital marketing. This line of inquiry underscores the growing importance of customer data, metrics and analytical models in guiding marketing decisions and evaluating outcomes. Scholars have pointed out that traditional performance indicators may be insufficient to capture the complexity of digital marketing activities and have called for more sophisticated, multidimensional measurement frameworks (Wedel \\u0026amp; Kannan, 2016; Verhoef, Kooge \\u0026amp; Walk, 2016).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3 Digital marketing between quantity and quality: measurement challenges\\u003c/h2\\u003e \\u003cp\\u003eDespite the richness of existing contributions, the literature reveals persistent challenges in the empirical measurement of digital marketing. A recurring limitation concerns the predominance of \\u003cb\\u003equantitative and tool-based indicators\\u003c/b\\u003e, such as the number of digital channels adopted or the presence of specific technologies. While these measures provide insights into the extent of digital adoption, they often overlook qualitative differences in strategic orientation, integration and analytical sophistication (Chaffey \\u0026amp; Ellis-Chadwick, 2019).\\u003c/p\\u003e \\u003cp\\u003eRecent studies have therefore emphasized the distinction between \\u003cem\\u003equantity\\u003c/em\\u003e and \\u003cem\\u003equality\\u003c/em\\u003e dimensions of digital marketing. Quantity-related aspects refer to the breadth and intensity of digital tool usage, whereas quality-related aspects capture the depth of integration, the use of analytics and the development of digital capabilities. However, empirical evidence on how these dimensions interact, evolve over time and differ across firms and sectors remains limited.\\u003c/p\\u003e \\u003cp\\u003eMoreover, much of the empirical literature relies on single-method approaches and univariate indicators, which may obscure latent structures and heterogeneous adoption patterns. This issue is particularly relevant in longitudinal and comparative settings, where digital marketing trajectories may diverge rather than converge toward a homogeneous model.\\u003c/p\\u003e \\u003cp\\u003eAgainst this background, the present study contributes to the literature by adopting a multivariate and longitudinal perspective that explicitly addresses the quality\\u0026ndash;quantity trade-off in digital marketing adoption. By integrating complementary statistical techniques, the analysis responds to recent methodological calls for more rigorous and multidimensional approaches to the empirical study of digital marketing practices.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. MATERIALS AND METHODS\",\"content\":\"\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 Data structure, variables and empirical setting\\u003c/h2\\u003e \\u003cp\\u003eThe empirical analysis is based on secondary data describing firms\\u0026rsquo; adoption of digital technologies relevant to marketing activities. The dataset refers to two distinct time periods, corresponding to the years 2019 and 2023, and allows for a structured comparative analysis of digital marketing practices before and after a phase of accelerated digital transformation. The choice of these two observation points is methodologically motivated by the substantial changes in digital infrastructures, platform diffusion and analytics capabilities that have characterized the intervening period, making the temporal comparison particularly informative.\\u003c/p\\u003e \\u003cp\\u003eThe variables included in the analysis capture multiple and conceptually distinct dimensions of digital marketing adoption. Specifically, the dataset encompasses indicators related to:\\u003c/p\\u003e \\u003cp\\u003e \\u003col style=\\\"list-style-type:lower-roman;\\\"\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003edigital presence, such as the availability of corporate websites;\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003edigital interaction, including the use of social media and online communication channels;\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003etransactional capabilities, such as e-commerce adoption and online sales functionalities; and\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eanalytical support, referring to the use of data analytics tools and digital systems to inform marketing decisions.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003c/ol\\u003e \\u003c/p\\u003e \\u003cp\\u003eThis selection reflects a deliberate conceptualization of digital marketing as a multidimensional construct, in which observable practices represent manifestations of deeper strategic and organizational orientations. Rather than treating digital adoption as a binary or additive phenomenon, the analysis assumes that different dimensions interact and jointly shape firms\\u0026rsquo; digital marketing profiles.\\u003c/p\\u003e \\u003cp\\u003eFrom a statistical standpoint, the indicators exhibit substantial heterogeneity in scale, distribution and interdependence. For this reason, the analysis does not rely on univariate descriptive statistics as the primary inferential tool. Instead, it adopts a multivariate perspective aimed at uncovering latent structures and systematic patterns that are not directly observable from individual indicators. This approach is consistent with recent methodological recommendations in digital transformation research, which emphasize the need for analytical frameworks capable of capturing complexity and heterogeneity.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 Integrated multivariate analytical framework\\u003c/h2\\u003e \\u003cp\\u003eTo address the multidimensional and heterogeneous nature of digital marketing adoption, the study adopts an integrated multivariate analytical framework combining principal component analysis (PCA), hierarchical cluster analysis (HCA) and the Non-Parametric Combination (NPC) test and ranking procedure. These techniques are well established in the statistical literature as effective tools for dimensionality reduction, classification and synthesis of multivariate information (Backhaus et al., 2016; Hair et al., 2019; Pesarin \\u0026amp; Salmaso, 2010).\\u003c/p\\u003e \\u003cp\\u003eThe rationale for integrating these methods lies in their complementary analytical roles. Principal component analysis is employed as an exploratory technique to reduce dimensionality and identify latent factors underlying the observed indicators. By transforming correlated variables into a reduced set of orthogonal components, PCA provides a parsimonious representation of the digital marketing space and facilitates the identification of underlying strategic dimensions (Jolliffe \\u0026amp; Cadima, 2016).\\u003c/p\\u003e \\u003cp\\u003eFormally, let \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\mathbf{X}=\\\\left({x}_{ij}\\\\right)\\\\)\\u003c/span\\u003e\\u003c/span\\u003e denote the standardized data matrix, where \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:i=1,\\\\dots\\\\:,n\\\\)\\u003c/span\\u003e\\u003c/span\\u003e indexes economic sectors and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:j=1,\\\\dots\\\\:,p\\\\)\\u003c/span\\u003e\\u003c/span\\u003e digital marketing indicators. PCA is based on the eigenvalue decomposition of the covariance (or correlation) matrix \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\mathbf{S}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e, obtained by solving:\\u003cdiv id=\\\"Equa\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equa\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:\\\\mathbf{S}{\\\\mathbf{a}}_{k}={\\\\lambda\\\\:}_{k}{\\\\mathbf{a}}_{k},$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003ewhere \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\lambda\\\\:}_{k}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e denotes the eigenvalue associated with the \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:k\\\\)\\u003c/span\\u003e\\u003c/span\\u003e-th principal component and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\mathbf{a}}_{k}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e the corresponding eigenvector. The score of the \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:k\\\\)\\u003c/span\\u003e\\u003c/span\\u003e-th component for sector \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:i\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is given by:\\u003cdiv id=\\\"Equb\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equb\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:{z}_{ik}=\\\\sum\\\\:_{j=1}^{p}{a}_{jk}{x}_{ij}.$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003eThe analysis is conducted separately for each time period in order to assess the stability and evolution of the latent structure over time. Component retention is guided by standard criteria, including eigenvalues and explained variance, while substantive interpretation is based on factor loadings and their relevance to digital marketing practices. This procedure allows for the identification of both stable and emerging dimensions of digital marketing adoption.\\u003c/p\\u003e \\u003cp\\u003eBuilding on the PCA results, hierarchical cluster analysis is applied to classify economic sectors into homogeneous groups based on their scores on the principal components. Using component scores rather than original variables reduces noise and ensures that classification is driven by the most informative latent dimensions rather than by idiosyncratic variation (Hair et al., 2014).\\u003c/p\\u003e \\u003cp\\u003eThe clustering objective can be formalized in terms of within-cluster homogeneity as the minimization of total within-cluster variance:\\u003cdiv id=\\\"Equc\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equc\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:\\\\text{m}\\\\text{i}\\\\text{n}\\\\sum\\\\:_{g=1}^{G}\\\\sum\\\\:_{i\\\\in\\\\:{C}_{g}}\\\\parallel\\\\:{\\\\mathbf{z}}_{i}-{\\\\varvec{\\\\mu\\\\:}}_{g}{\\\\parallel\\\\:}^{2},$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003ewhere \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{C}_{g}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e denotes cluster \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:g\\\\)\\u003c/span\\u003e\\u003c/span\\u003e, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\varvec{\\\\mu\\\\:}}_{g}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e its centroid and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\mathbf{z}}_{i}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e the vector of principal component scores for sector \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:i\\\\)\\u003c/span\\u003e\\u003c/span\\u003e. The resulting clusters represent distinct digital marketing profiles, reflecting different configurations and levels of digital adoption. Comparing clustering solutions across the two time periods enables the analysis of persistence, differentiation and structural change in digital marketing strategies.\\u003c/p\\u003e \\u003cp\\u003eFinally, the Non-Parametric Combination (NPC) test and ranking procedure is employed to provide a synthetic and robust comparative assessment of digital marketing adoption across sectors. The NPC framework allows for the aggregation of information across multiple indicators and latent dimensions without imposing parametric assumptions on the underlying data-generating process, making it particularly suitable for heterogeneous and non-normal datasets (Pesarin \\u0026amp; Salmaso, 2010; Pesarin et al., 2017).\\u003c/p\\u003e \\u003cp\\u003eLet \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{T}_{h}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e denote the partial test statistic associated with the \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:h\\\\)\\u003c/span\\u003e\\u003c/span\\u003e-th indicator or dimension. These statistics are combined into a global statistic according to a suitable combining function \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\varphi\\\\:(\\\\cdot\\\\:)\\\\)\\u003c/span\\u003e\\u003c/span\\u003e:\\u003cdiv id=\\\"Equd\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equd\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:T=\\\\varphi\\\\:({T}_{1},{T}_{2},\\\\dots\\\\:,{T}_{H}).$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003eThe resulting NPC ranking provides a distribution-free ordering of sectors that accounts for the multidimensional nature of digital marketing adoption and complements the exploratory and classificatory analyses with a comparative perspective.\\u003c/p\\u003e \\u003cp\\u003eTaken together, the integration of PCA, cluster analysis and NPC ranking constitutes a coherent analytical pipeline that moves systematically from dimensionality reduction to classification and comparative assessment. This unified framework enhances analytical rigor and interpretability and enables a structured examination of digital marketing adoption patterns that would not be achievable through isolated applications of individual methods.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4. Applications\",\"content\":\"\\u003cp\\u003eThis section presents the empirical application of the integrated statistical framework described in Section \\u003cspan refid=\\\"Sec2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. The results are articulated according to the three analytical steps\\u0026mdash;principal component analysis, cluster analysis and NPC ranking\\u0026mdash;each contributing to a progressively structured interpretation of digital marketing adoption. Throughout the section, particular attention is devoted to linking statistical outcomes to substantive insights relevant for digital marketing research and practice.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.1 Principal components and latent drivers of digital marketing adoption\\u003c/h2\\u003e \\u003cp\\u003ePrincipal component analysis (PCA) was applied separately to the two periods under investigation (2019 and 2023) with the objective of identifying the latent structure underlying digital marketing adoption and assessing its temporal evolution. Given the inherently multidimensional and interdependent nature of digital marketing indicators\\u0026mdash;spanning digital presence, transactional capabilities and analytics usage\\u0026mdash;PCA represents an appropriate and well-established method for dimensionality reduction and latent structure identification (Jolliffe \\u0026amp; Cadima, 2016; Hair et al., 2019).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003e1. Explained variance of the first three principal components derived from PCA for 2019 and 2023.\\u003c/h3\\u003e\\n\\u003cp\\u003eThe analysis reveals a highly structured variance decomposition in both periods. As illustrated in Fig.\\u0026nbsp;1, the first principal component explains a substantial proportion of total variance in both 2019 and 2023, capturing the dominant latent dimension of digital marketing adoption. The stability of the variance explained by this component across time provides strong empirical evidence for the existence of a persistent underlying factor characterizing firms\\u0026rsquo; overall engagement with digital marketing. Substantively, this dominant dimension can be interpreted as a \\u003cem\\u003egeneral digital marketing intensity factor\\u003c/em\\u003e, reflecting the breadth and systematicity with which digital technologies are incorporated into marketing activities.\\u003c/p\\u003e \\u003cp\\u003eImportantly, however, the comparative analysis also reveals significant structural change. While the first component remains predominant, Fig.\\u0026nbsp;1 shows a non-negligible increase in the proportion of variance explained by the second and third components in 2023. From a statistical standpoint, this pattern indicates a reduction in redundancy among observed indicators and the emergence of additional latent dimensions that contribute meaningfully to explaining digital marketing behavior. From a substantive standpoint, it signals an increasing differentiation of digital marketing strategies over time.\\u003c/p\\u003e \\u003cp\\u003eThe second principal component is primarily associated with variables capturing transactional capabilities and the use of data analytics tools. High scores on this component characterize firms that integrate digital channels with analytical processes aimed at performance monitoring, targeting and strategic decision support. The increased explanatory power of this component in 2023 provides direct empirical support for the growing relevance of analytics-driven digital marketing, in line with the literature emphasizing the centrality of data analytics in contemporary marketing strategy (Wedel \\u0026amp; Kannan, 2016; Kumar et al., 2021; Varadarajan, 2022).\\u003c/p\\u003e \\u003cp\\u003eThe third component, although accounting for a smaller share of variance, plays a crucial role in differentiating firms according to specific configurations of digital tool usage and integration. Its increased contribution over time further reinforces the interpretation of digital marketing adoption as an increasingly multidimensional phenomenon, in which firms combine digital presence, analytics and integration in heterogeneous ways rather than converging toward a uniform adoption pattern.\\u003c/p\\u003e \\u003cp\\u003eTaken together, the PCA results provide robust empirical evidence that digital marketing adoption is structured around \\u003cem\\u003estable yet evolving latent drivers\\u003c/em\\u003e. The coexistence of a dominant general factor and increasingly relevant secondary dimensions reflects a transition from relatively simple, tool-oriented adoption toward more complex, capability-oriented digital marketing strategies. Methodologically, the clear variance structure reported in Fig.\\u0026nbsp;1 justifies the use of PCA scores as inputs for subsequent analyses, as they provide a parsimonious and statistically sound representation of the underlying digital marketing space.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2 Cluster profiles and evolution of digital marketing strategies\\u003c/h2\\u003e \\u003cp\\u003eBuilding on the latent dimensions identified through PCA, hierarchical cluster analysis (HCA) was employed to classify economic sectors into homogeneous groups according to their digital marketing profiles. The clustering procedure was conducted using PCA scores rather than raw indicators, ensuring that classification was grounded in latent strategic orientations rather than in surface-level similarities. This approach is consistent with best practices in multivariate analysis, particularly in high-dimensional contexts characterized by strong correlations among variables (Backhaus et al., 2016; Hair et al., 2014).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003e2 Hierarchical clustering dendrogram of economic sectors based on PCA scores, 2019.\\u003c/h3\\u003e\\n\\u003cp\\u003eAs shown in Fig.\\u0026nbsp;2, the hierarchical clustering solution for 2019 identifies a two-cluster structure at the selected cut-off level. The first cluster groups sectors characterized by lower scores on the principal components associated with digital marketing adoption. These sectors exhibit limited engagement with digital channels and, crucially, a weak integration of analytics-oriented practices, suggesting that digital marketing plays a marginal, fragmented or exploratory role within their strategic configurations.\\u003c/p\\u003e \\u003cp\\u003eThe second cluster comprises sectors displaying higher scores on the dominant latent dimensions of adoption, indicating a comparatively more structured digital marketing profile. In this group, the adoption of digital tools tends to be accompanied by greater alignment with integration- and analytics-related dimensions, pointing to a more capability-oriented configuration rather than a mere expansion of digital presence. This dichotomy is consistent with the view that early-stage digital marketing adoption is primarily driven by differences in the \\u003cem\\u003edepth\\u003c/em\\u003e of digital integration and analytical capability, not only by the availability of visible digital touchpoints (Tiago \\u0026amp; Ver\\u0026iacute;ssimo, 2014; Hofacker et al., 2020; Wedel \\u0026amp; Kannan, 2016).\\u003c/p\\u003e \\u003cp\\u003eImportantly, the two-cluster structure observed in 2019 provides evidence of a macro-segmentation between lower- and higher-adoption sectoral configurations. This segmentation constitutes a baseline against which the 2023 clustering structure can be evaluated in terms of continuity and increased differentiation.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003e3 Hierarchical clustering dendrogram of economic sectors based on PCA scores, 2023.\\u003c/h3\\u003e\\n\\u003cp\\u003eThe hierarchical clustering structure observed in 2023, reported in Fig.\\u0026nbsp;3, exhibits a marked increase in internal differentiation with respect to the two-cluster configuration identified for 2019. While the earlier period was characterized by a broad dichotomy between lower- and higher-adoption sectors, the 2023 dendrogram reveals a more articulated cluster structure, indicating that digital marketing adoption has evolved from a relatively binary segmentation toward a more nuanced configuration.\\u003c/p\\u003e \\u003cp\\u003eAt the selected cut-off level, the clustering solution for 2023 identifies multiple clusters, reflecting differentiated combinations of digital presence, transactional capabilities and analytics integration. Importantly, this increased granularity does not imply a disruption of the underlying structure observed in 2019; rather, it represents a process of internal stratification within the group of higher-adoption sectors, while a cluster of sectors characterized by persistently low levels of digital marketing adoption continues to be observed.\\u003c/p\\u003e \\u003cp\\u003eIn particular, sectors that in 2019 belonged to the higher-adoption cluster become distributed across distinct clusters in 2023, indicating divergent evolutionary trajectories. Some sectors exhibit a clear transition toward analytics-intensive and highly integrated digital marketing configurations, characterized by strong alignment between digital channels and data-driven decision-making processes. Other sectors, while maintaining an expanded digital presence, remain relatively closer to communication-oriented or transaction-focused practices, displaying a more limited degree of analytical integration.\\u003c/p\\u003e \\u003cp\\u003eThis differentiation is consistent with the PCA results discussed in Section \\u003cspan refid=\\\"Sec7\\\" class=\\\"InternalRef\\\"\\u003e3.1\\u003c/span\\u003e, which show an increased explanatory relevance of secondary latent dimensions associated with analytics and integration in 2023. The clustering outcomes therefore provide a classificatory translation of the latent structural changes identified at the dimensional level, reinforcing the interpretation of digital marketing adoption as a capability-based and path-dependent process rather than as a uniform diffusion of tools.\\u003c/p\\u003e \\u003cp\\u003eFrom a longitudinal perspective, the comparison between Figs.\\u0026nbsp;2 and 3 highlights an important dynamic: while the boundary between low- and higher-adoption sectors remains structurally stable, the internal composition of the higher-adoption group becomes increasingly heterogeneous. This suggests that digital marketing transformation does not eliminate disparities but instead redistributes them along more refined strategic dimensions, amplifying differentiation among sectors that have already crossed a minimum threshold of digital adoption.\\u003c/p\\u003e \\u003cp\\u003eFrom a methodological standpoint, the emergence of additional clusters in 2023 confirms the suitability of PCA-based hierarchical clustering for capturing temporal evolution in complex, high-dimensional contexts. The results indicate that the clustering procedure is sensitive not only to cross-sectional differences but also to structural changes in the latent configuration of digital marketing practices over time.\\u003c/p\\u003e \\u003cp\\u003eOverall, the 2023 clustering results provide strong empirical evidence that digital marketing adoption evolves through progressive differentiation rather than convergence. The transition from a two-cluster structure in 2019 to a more articulated clustering configuration in 2023 underscores the increasing strategic importance of analytics integration as a discriminating factor and highlights the need for analytical frameworks capable of capturing such nuanced dynamics.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.3 NPC ranking and comparative sectoral assessment\\u003c/h2\\u003e \\u003cp\\u003eTo complement the exploratory and classificatory analyses developed in the previous subsections, a non-parametric combination (NPC) ranking procedure was applied in order to obtain a synthetic and robust comparative assessment of digital marketing adoption across economic sectors. The NPC framework is particularly suited to this context, as it allows the aggregation of information across multiple indicators and latent dimensions without imposing restrictive distributional assumptions or requiring arbitrary weighting schemes (Pesarin \\u0026amp; Salmaso, 2010; Pesarin et al., 2017). This feature is especially relevant in digital marketing research, where data are often heterogeneous, skewed and characterized by complex interdependencies.\\u003c/p\\u003e \\u003cp\\u003eThe adoption of an NPC-based ranking responds to a key analytical need: moving beyond isolated or dimension-specific comparisons toward an integrated evaluation of relative positioning. While PCA and cluster analysis provide insights into latent structures and strategic archetypes, they do not directly offer a synthetic measure of comparative performance. The NPC ranking fills this gap by combining partial pieces of evidence into a global ordering that reflects the overall configuration of digital marketing adoption.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003e4 Non-parametric combination (NPC) ranking of economic sectors based on multidimensional digital marketing adoption indicators, 2019 and 2023.\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003eAs shown in Fig.\\u0026nbsp;4, the NPC ranking reveals pronounced heterogeneity in digital marketing adoption across economic sectors in both periods considered. In 2019, sectors characterized by high information intensity, frequent digital interaction with customers and greater reliance on data-driven processes systematically occupy the highest positions in the ranking. These sectors display structured digital marketing configurations in which diversified digital channels are complemented by analytical integration and performance monitoring routines. In contrast, sectors positioned at the lower end of the ranking rely predominantly on fragmented or basic digital tools, exhibiting limited analytical maturity and weak integration between digital presence and strategic decision-making.\\u003c/p\\u003e \\u003cp\\u003eThe comparison between 2019 and 2023 highlights a general upward shift in NPC scores, indicating that digital marketing adoption has increased across sectors. This result reflects the diffusion of digital technologies and analytics capabilities and confirms the dynamic nature of digital marketing transformation. Importantly, however, this overall improvement does not translate into a substantial reshuffling of relative positions. Sectors that ranked highly in 2019 tend to preserve their advantage in 2023, while sectors at the bottom of the ranking display more limited progress. This persistence of relative ordering provides strong empirical evidence of path dependence in digital marketing adoption and underscores the role of structural and contextual factors\\u0026mdash;such as sectoral business models, technological requirements and skill endowments\\u0026mdash;in shaping digital transformation trajectories (Hanelt et al., 2021; Kraus et al., 2022).\\u003c/p\\u003e \\u003cp\\u003eAt the same time, the ranking reveals non-negligible cases of upward mobility. A subset of sectors improves its relative position between the two periods, signaling successful transitions toward more analytics-intensive and integrated digital marketing strategies. These movements are not random but appear systematically associated with improvements along the latent dimensions identified by PCA and with transitions toward more advanced cluster profiles. From a substantive perspective, this evidence reinforces the argument that investments in data analytics capabilities, integration of digital tools and cross-functional coordination represent critical levers for advancing digital marketing maturity (Wedel \\u0026amp; Kannan, 2016; Kumar et al., 2021).\\u003c/p\\u003e \\u003cp\\u003eFrom a methodological standpoint, the NPC ranking represents a crucial complement to PCA and cluster analysis. While PCA uncovers latent drivers and cluster analysis translates them into discrete strategic configurations, the NPC ranking provides a continuous and interpretable measure of relative sectoral performance. The integration of these techniques results in a coherent analytical pipeline in which exploratory, classificatory and comparative perspectives mutually reinforce one another, enhancing both robustness and interpretability of the empirical findings.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.4 Integrative interpretation and implications for digital marketing analysis\\u003c/h2\\u003e \\u003cp\\u003eTaken together, the results of the three analytical steps provide a comprehensive and internally consistent representation of digital marketing adoption as a structured, heterogeneous and evolving phenomenon. Each method contributes a distinct layer of insight: PCA identifies the latent dimensions underlying observed indicators, cluster analysis reveals discrete strategic profiles based on these dimensions, and NPC ranking synthesizes multidimensional information into a comparative assessment of relative positioning.\\u003c/p\\u003e \\u003cp\\u003eFrom a methodological perspective, this integrative approach demonstrates the value of multivariate statistical analysis for addressing the intrinsic complexity of digital marketing phenomena. By moving beyond descriptive indicators and single-method analyses, the proposed framework captures both structural regularities\\u0026mdash;such as the persistence of dominant latent drivers\\u0026mdash;and differentiated trajectories, reflected in cluster stratification and ranking mobility. This layered analytical design mitigates the risk of oversimplification and allows for a more nuanced interpretation of digital marketing adoption dynamics.\\u003c/p\\u003e \\u003cp\\u003eFrom a substantive perspective, the results consistently underscore the central role of analytics and integration in contemporary digital marketing strategies. Firms and sectors that successfully embed data analytics into their marketing processes exhibit more advanced, coherent and strategically oriented digital profiles. Conversely, reliance on basic digital presence without analytical integration is systematically associated with lower-value configurations and persistent disadvantage. These findings reinforce the view that digital marketing effectiveness increasingly depends on the development of analytical capabilities rather than on the mere expansion of digital touchpoints.\\u003c/p\\u003e \\u003cp\\u003eMore broadly, the integrative interpretation highlights that digital marketing adoption cannot be understood as a linear or uniform process. Instead, it emerges as a capability-based and path-dependent transformation, characterized by persistent heterogeneity and selective upgrading. In this sense, the statistical framework does not merely describe digital marketing practices but contributes to their conceptualization, showing how quantitative analysis can illuminate the structural mechanisms underlying digital transformation.\\u003c/p\\u003e \\u003cp\\u003eOverall, the combined evidence provided by PCA, cluster analysis and NPC ranking supports the argument that statistics and data analytics constitute foundational components of effective digital marketing strategy. By enabling the identification of latent drivers, strategic profiles and relative positioning, the proposed framework illustrates how advanced statistical methods can enhance both empirical understanding and analytical rigor in digital marketing research.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"5. Concluding remarks and suggestions for future research\",\"content\":\"\\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.1 concluding remarks and contributions\\u003c/h2\\u003e \\u003cp\\u003eThis study provides empirical and methodological evidence supporting the interpretation of digital marketing adoption as a complex, multidimensional and heterogeneous phenomenon that cannot be adequately captured through isolated indicators or purely descriptive approaches. By adopting an integrated statistical framework combining principal component analysis, hierarchical cluster analysis and non-parametric combination (NPC) ranking, the paper offers a structured and rigorous assessment of digital marketing practices and their evolution over time.\\u003c/p\\u003e \\u003cp\\u003eFrom an empirical standpoint, the results demonstrate that digital marketing adoption is primarily driven by latent dimensions reflecting distinct strategic orientations rather than by the mere presence of individual digital tools. In particular, the empirical distinction between components associated with basic digital presence and those related to analytics intensity and integration highlights a progressive shift from tool-based adoption toward capability-based digital marketing strategies. This evidence is consistent with recent theoretical contributions emphasizing that digital marketing maturity increasingly depends on firms\\u0026rsquo; analytical capabilities and their ability to transform data into actionable strategic insights (Wedel \\u0026amp; Kannan, 2016; Varadarajan, 2022).\\u003c/p\\u003e \\u003cp\\u003eThe longitudinal comparison further reveals that, despite a general increase in the adoption of digital marketing practices, the process remains characterized by persistent heterogeneity across firms and sectors. Rather than converging toward a single dominant model, digital marketing strategies evolve along differentiated trajectories shaped by structural, organizational and sector-specific factors. This finding reinforces the view that digital transformation\\u0026mdash;and digital marketing in particular\\u0026mdash;should be interpreted as a non-linear and path-dependent process, in line with broader evidence in the digital transformation literature (Hanelt et al., 2021; Kraus et al., 2022).\\u003c/p\\u003e \\u003cp\\u003eFrom a methodological perspective, the study contributes to the quantitative analysis of digital marketing by empirically validating an integrated multivariate framework tailored to high-dimensional and heterogeneous data. While techniques such as PCA and cluster analysis are well established in the statistical literature, their joint application in digital marketing research has remained relatively fragmented. By explicitly integrating these methods with a non-parametric ranking procedure, the paper demonstrates how complementary statistical tools can be orchestrated within a coherent analytical design to address dimensionality reduction, classification and comparative assessment simultaneously. This contribution is particularly aligned with the aims of \\u003cem\\u003eQuality \\u0026amp; Quantity\\u003c/em\\u003e, as it illustrates how advanced statistical methods can be applied to substantively rich phenomena without compromising interpretability.\\u003c/p\\u003e \\u003cp\\u003eOverall, the findings underscore that digital marketing adoption should not be conceptualized as a binary or uniform process, but rather as a continuum shaped by multiple interrelated dimensions. This insight challenges simplified representations of digitalization and supports the adoption of more nuanced and statistically grounded analytical approaches in both academic research and policy-oriented evaluations.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.2 Directions for future research\\u003c/h2\\u003e \\u003cp\\u003eWhile the study provides several contributions, it also opens up multiple avenues for future research. First, the proposed framework could be extended by incorporating firm-level performance indicators, such as sales growth, profitability or customer engagement metrics. Linking digital marketing adoption profiles to performance outcomes would enable a more direct assessment of the strategic effectiveness of different digital configurations and strengthen the connection between statistical evidence and managerial relevance.\\u003c/p\\u003e \\u003cp\\u003eSecond, future research could apply the framework to different geographical or institutional contexts. Cross-country or cross-regional analyses would allow researchers to investigate how institutional environments, regulatory frameworks and cultural factors influence digital marketing adoption patterns, thereby enhancing the external validity and generalizability of the results.\\u003c/p\\u003e \\u003cp\\u003eThird, the integration of the current multivariate framework with predictive and machine learning techniques represents a promising direction for methodological development. While the present study focuses on exploratory and classificatory analysis, combining these approaches with predictive models could support forecasting, scenario analysis and decision-support applications, further expanding the contribution of statistical methods to digital marketing research.\\u003c/p\\u003e \\u003cp\\u003eFinally, longitudinal designs incorporating additional observation points would allow for a more refined analysis of transition dynamics between digital marketing profiles. Such extensions would provide insights into the stability, persistence or volatility of digital marketing strategies over time and contribute to a deeper understanding of the dynamics underlying digital transformation processes.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"6. Management implications\",\"content\":\"\\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e6.1 statistical evidence as an operational backbone for digital marketing strategy\\u003c/h2\\u003e \\u003cp\\u003eThe empirical evidence presented in Section \\u003cspan refid=\\\"Sec6\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e indicates that digital marketing adoption should be modelled and managed as a \\u003cem\\u003emultidimensional configuration\\u003c/em\\u003e of interdependent practices rather than as the additive outcome of isolated tools (e.g., \\u0026ldquo;having a website\\u0026rdquo; or \\u0026ldquo;being active on social media\\u0026rdquo;). This has direct managerial consequences: in complex digital environments, single indicators provide only partial\\u0026mdash;and potentially misleading\\u0026mdash;signals, because they ignore latent complementarities among technologies, organizational routines and analytics capabilities. A strategy premised on isolated metrics risks overestimating digital maturity and underestimating capability gaps.\\u003c/p\\u003e \\u003cp\\u003eThe integrated statistical framework adopted in this study provides a formally grounded pathway for converting heterogeneous digital marketing signals into structured strategic knowledge. PCA yields a parsimonious representation of the digital marketing space by extracting orthogonal latent drivers, cluster analysis maps these drivers into a discrete set of interpretable strategic archetypes, and NPC ranking provides a distribution-free comparative synthesis at the sectoral level. In combination, these outputs can be used as a decision-support system enabling firms to shift from descriptive monitoring to diagnostic and prescriptive reasoning (Wedel \\u0026amp; Kannan, 2016; Hanssens, 2019).\\u003c/p\\u003e \\u003cp\\u003eA first managerial implication concerns \\u003cem\\u003estrategic prioritisation\\u003c/em\\u003e. The variance structure observed in the PCA results (Section \\u003cspan refid=\\\"Sec7\\\" class=\\\"InternalRef\\\"\\u003e3.1\\u003c/span\\u003e) suggests that a dominant latent dimension captures a general intensity factor, while secondary components increasingly reflect differentiation related to analytics and integration. In practice, this implies that digital marketing \\u0026ldquo;scale\\u0026rdquo; (presence across channels) and digital marketing \\u0026ldquo;capability\\u0026rdquo; (analytics-driven integration, measurement routines, cross-functional coordination) are not equivalent. Consequently, investment priorities should be aligned with the most discriminating latent dimensions, i.e., those associated with analytical maturity and integration rather than with basic channel adoption. This is consistent with the view that competitive advantage in digital marketing is progressively determined by data-driven capability building and by the transformation of data into actionable insights (Varadarajan, 2022; Kumar et al., 2021).\\u003c/p\\u003e \\u003cp\\u003eA second implication concerns \\u003cem\\u003eresource allocation under constraint\\u003c/em\\u003e. The empirical structure supports a staged investment logic: (i) foundational digital presence (necessary but not sufficient), (ii) integration of transactional and customer-interaction capabilities, and (iii) institutionalisation of analytics routines (measurement systems, attribution logic, experimentation protocols, dashboard governance). In this framework, spending to expand the number of channels without a commensurate increase in analytics capability may generate \\u0026ldquo;digital surface area\\u0026rdquo; without strategic control\\u0026mdash;an outcome that typically increases operational complexity and decreases decision quality. By contrast, investments in analytics infrastructure and measurement discipline increase the marginal return of existing digital channels by improving targeting, personalization and performance management (Wedel \\u0026amp; Kannan, 2016; Hanssens, 2019).\\u003c/p\\u003e \\u003cp\\u003eThird, the cluster analysis provides an immediately actionable managerial artefact: \\u003cem\\u003ecluster membership as strategic diagnosis\\u003c/em\\u003e. A firm (or sector) can interpret its cluster position as a \\u0026ldquo;maturity state,\\u0026rdquo; and the distance to more advanced clusters as an estimate of the capability gap. Importantly, this is not a generic maturity model: the clusters are empirically derived from multivariate structure rather than imposed a priori. For firms in low- or intermediate-adoption clusters, the managerial use is twofold: (a) benchmarking against peer groups with comparable structural constraints; and (b) designing targeted transition strategies to move toward analytics-intensive profiles. This transition is not merely about acquiring tools; it is about establishing governance mechanisms for data quality, analytical workflows and decision rights, consistent with the capability-based interpretation of digital marketing maturity (Varadarajan, 2022).\\u003c/p\\u003e \\u003cp\\u003eFinally, the integrated pipeline supports \\u003cem\\u003estrategic monitoring over time\\u003c/em\\u003e. Changes in PCA scores provide continuous signals on latent drivers, cluster transitions indicate discrete strategic shifts, and NPC position changes capture relative performance in a competitive landscape. This multi-layer monitoring architecture reduces the risk of \\u0026ldquo;metric gaming\\u0026rdquo; and enhances interpretability: firms can explain \\u003cem\\u003ewhy\\u003c/em\\u003e a ranking improved (latent driver changes), \\u003cem\\u003ehow\\u003c/em\\u003e strategic profile shifted (cluster migration), and \\u003cem\\u003ewhether\\u003c/em\\u003e improvement is meaningful in relative terms (NPC movement). In short, statistics function as an operational backbone for strategic control in digital marketing, rather than as an ex-post analytical add-on.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e6.2 Governance architecture, benchmarking regimes, and policy-relevant implications\\u003c/h2\\u003e \\u003cp\\u003eBeyond strategic prioritisation, the findings carry implications for the governance and organisational design of digital marketing, with specific attention to how firms structure decision rights, coordinate functions and institutionalise analytics capabilities. The emergence of analytics-related latent dimensions indicates that digital marketing effectiveness depends on organisational routines and cross-functional integration, not only on technology adoption. In governance terms, this implies that digitally advanced profiles are likely associated with tighter coupling among marketing, IT, data engineering and analytics units, enabling faster data-to-decision cycles and more consistent performance accountability.\\u003c/p\\u003e \\u003cp\\u003eA key implication is that firms should move from \\u003cem\\u003etool governance\\u003c/em\\u003e to \\u003cem\\u003ecapability governance\\u003c/em\\u003e. Tool governance focuses on channel-level outputs (e.g., content frequency, traffic, clicks), whereas capability governance focuses on measurement quality, analytical validity and decision traceability. In practice, this shift requires: (i) data governance policies (definitions, quality rules, lineage), (ii) analytics governance (model validation, KPI hierarchies, attribution assumptions), and (iii) decision governance (who decides, on which evidence, with what review cadence). Within the logic of the proposed framework, governance maturity can be proxied by the relative weight of analytics/integration dimensions in PCA scores and by membership in analytics-intensive clusters.\\u003c/p\\u003e \\u003cp\\u003eThe NPC ranking provides a robust \\u003cem\\u003ebenchmarking regime\\u003c/em\\u003e that is particularly valuable for governance because it synthesises multidimensional evidence without strong parametric assumptions and without requiring arbitrary weights. For managers, NPC-based benchmarking supports a \\u0026ldquo;relative competitiveness\\u0026rdquo; lens: performance is assessed in relation to the evolving distribution of peers rather than against static thresholds. This is crucial in rapidly changing digital environments, where absolute benchmarks can become obsolete. Moreover, NPC rankings can be embedded in periodic strategic reviews to evaluate whether the firm\\u0026rsquo;s digital marketing transformation is progressing faster, slower or in line with its reference group.\\u003c/p\\u003e \\u003cp\\u003eAt the sectoral level, the ranking results can inform \\u003cem\\u003ecompetitive intelligence\\u003c/em\\u003e and strategic positioning choices. Firms in lower-ranked sectors may face structural constraints (skills availability, technology diffusion, regulatory frictions, business model rigidity) that limit the pace of adoption. In such cases, the managerial implication is to combine internal capability building with ecosystem strategies, such as partnerships with data providers, platform actors or specialised analytics vendors, to reduce fixed costs and accelerate learning curves. Conversely, firms in higher-ranked sectors can use ranking stability as evidence of sustained advantage but should also monitor internal differentiation (cluster sub-structures) to avoid complacency and to anticipate strategic shifts among close competitors.\\u003c/p\\u003e \\u003cp\\u003eThe results also support policy-relevant implications. Persistent heterogeneity suggests that \\u0026ldquo;uniform\\u0026rdquo; digital transformation policies may have limited effectiveness. Institutions can leverage the integrated statistical framework as an evaluative instrument to identify where marginal public support is likely to yield the highest returns. For example, sectors persistently located in low-adoption clusters and at the bottom of NPC rankings may require targeted interventions focused on analytics skills, data infrastructure access, or incentives for experimentation and measurement adoption. Conversely, sectors with intermediate profiles might benefit more from programs aimed at integration (interoperability, standardisation, shared data platforms) rather than from generic technology adoption subsidies.\\u003c/p\\u003e \\u003cp\\u003eImportantly, the proposed framework can be operationalised as a \\u003cem\\u003epolicy monitoring dashboard\\u003c/em\\u003e. PCA trends provide signals of structural shifts in adoption drivers, cluster composition changes indicate whether heterogeneity is shrinking or expanding, and NPC ranking mobility provides a direct measure of sectoral catching-up or divergence. This monitoring logic reduces informational asymmetries in policy evaluation and enables adaptive interventions, i.e., policies that evolve in response to statistically grounded evidence rather than to ex-post narrative assessments.\\u003c/p\\u003e \\u003cp\\u003eOverall, the management implications converge on a central point: the strategic value of digital marketing increasingly depends on the statistical capacity to (i) diagnose latent capability structures, (ii) classify strategic profiles, and (iii) benchmark relative positioning under uncertainty. In this sense, multivariate analysis and non-parametric ranking are not merely research techniques but components of an actionable governance toolkit for data-driven digital marketing (Wedel \\u0026amp; Kannan, 2016; Hanssens, 2019).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"7. Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec24\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e7.1 digital marketing adoption as a multidimensional, latent and capability-based phenomenon\\u003c/h2\\u003e \\u003cp\\u003eThe empirical findings of this study provide robust support for the interpretation of digital marketing adoption as a multidimensional and latent phenomenon, whose underlying structure cannot be adequately inferred from observable practices alone. This perspective aligns with recent strands of the digital transformation and digital marketing literature, which increasingly conceptualize digital adoption as the outcome of interdependent technological, organizational and analytical dimensions rather than as a linear accumulation of tools (Hanelt et al., 2021; Cioppi et al., 2023).\\u003c/p\\u003e \\u003cp\\u003eThe principal component analysis offers clear empirical evidence that commonly used indicators\\u0026mdash;such as the presence of corporate websites, social media usage or e-commerce functionalities\\u0026mdash;capture only partial and surface-level aspects of digital marketing adoption. The identification of latent components associated with analytics intensity, integration and measurement routines indicates that digital marketing maturity is progressively defined by firms\\u0026rsquo; ability to generate, process and strategically exploit data. This finding empirically substantiates the distinction between \\u003cem\\u003etool-based\\u003c/em\\u003e and \\u003cem\\u003ecapability-based\\u003c/em\\u003e digital marketing strategies advanced in the marketing analytics literature (Wedel \\u0026amp; Kannan, 2016; Varadarajan, 2022).\\u003c/p\\u003e \\u003cp\\u003eImportantly, the temporal comparison reveals that while a dominant latent dimension persists across periods, secondary dimensions gain explanatory relevance over time. This pattern suggests that digital marketing adoption does not merely scale up in intensity but becomes structurally more differentiated. In theoretical terms, this evolution supports a configurational interpretation of digital marketing, in which maturity is associated not only with the breadth of adoption but also with the coherence and complementarity among digital tools, analytics capabilities and organizational routines.\\u003c/p\\u003e \\u003cp\\u003eThe persistence of latent structures across time further implies that digital marketing adoption follows systematic and path-dependent trajectories. Rather than being driven by short-term experimentation or opportunistic responses to technological change, digital marketing strategies appear anchored to deeper organizational orientations and capabilities. This insight challenges diffusion-based models of digitalization and reinforces the relevance of capability-based and resource-based perspectives in explaining digital marketing evolution.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec25\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e7.2 Heterogeneity, strategic archetypes and differentiated trajectories across firms and sectors\\u003c/h2\\u003e \\u003cp\\u003eA second major contribution of the study lies in its empirical documentation of persistent heterogeneity in digital marketing adoption across firms and sectors. The cluster analysis reveals that economic actors are distributed across distinct strategic archetypes characterized by different configurations of digital presence, analytics usage and integration intensity. This finding stands in contrast to narratives of inevitable convergence toward a homogeneous digital marketing model.\\u003c/p\\u003e \\u003cp\\u003eThe identification of clusters corresponding to marginal, transitional and analytics-intensive digital marketing strategies provides a structured interpretation of adoption dynamics. In particular, the persistence of low-adoption clusters over time suggests that digital transformation processes may exacerbate structural disparities rather than reduce them. Firms and sectors endowed with stronger analytical capabilities and organizational readiness are better positioned to internalize the strategic value of digital marketing, while others remain confined to basic or fragmented configurations.\\u003c/p\\u003e \\u003cp\\u003eThis evidence is consistent with the broader digital transformation literature, which highlights differentiated and path-dependent trajectories shaped by sectoral characteristics, organizational resources and institutional environments (Kraus et al., 2022). However, the present study advances this literature by offering a statistically grounded classification based on latent dimensions rather than on qualitative typologies or descriptive categorizations. By relying on PCA-derived component scores, the cluster analysis ensures that strategic archetypes reflect underlying capability structures rather than superficial similarities.\\u003c/p\\u003e \\u003cp\\u003eThe NPC ranking analysis further enriches this interpretation by providing a macro-level comparative perspective. While overall digital marketing adoption increases over time, relative sectoral positions remain largely stable, indicating that early adopters tend to preserve their advantage. This stability suggests that digital marketing capabilities exhibit cumulative properties, reinforcing existing competitive hierarchies rather than dissolving them.\\u003c/p\\u003e \\u003cp\\u003eTaken together, the cluster and ranking results support a multilevel interpretation of digital marketing adoption. At the micro level, firms adopt differentiated strategic configurations; at the meso and macro levels, sectoral structures shape and constrain the distribution of digital marketing capabilities. This multilevel perspective contributes to a more comprehensive understanding of digital marketing dynamics and highlights the limitations of analyses that focus exclusively on either firm-level or sector-level determinants.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec26\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e7.3 Methodological integration, theory-building and limitations\\u003c/h2\\u003e \\u003cp\\u003eBeyond its substantive findings, the study makes a significant methodological contribution by demonstrating the analytical value of integrating complementary statistical techniques within a unified framework. While principal component analysis, cluster analysis and non-parametric ranking methods are individually well established, their combined application in digital marketing research remains limited. The present study shows that such integration enhances both analytical depth and interpretability.\\u003c/p\\u003e \\u003cp\\u003ePCA provides a parsimonious representation of complex, high-dimensional datasets by uncovering latent drivers of digital marketing adoption. Cluster analysis translates these latent dimensions into discrete and interpretable strategic archetypes, facilitating the identification of heterogeneous configurations. The NPC ranking synthesizes multidimensional information into a robust comparative assessment that avoids strong distributional assumptions and arbitrary weighting schemes. Together, these methods form a coherent analytical pipeline that moves systematically from data reduction to classification and comparison.\\u003c/p\\u003e \\u003cp\\u003eFrom a theoretical standpoint, the findings illustrate that statistical analysis can actively contribute to theory development rather than merely serving as a tool for empirical validation. By revealing latent structures and systematic patterns, multivariate analysis informs the conceptualization of digital marketing adoption as a structured, differentiated and capability-based process. This perspective reinforces the reciprocal relationship between quantitative methods and substantive theory, in line with the interdisciplinary mission of \\u003cem\\u003eQuality \\u0026amp; Quantity\\u003c/em\\u003e.\\u003c/p\\u003e \\u003cp\\u003eSeveral limitations should nevertheless be acknowledged. The reliance on secondary data restricts the ability to capture qualitative dimensions of digital marketing strategies, such as managerial cognition, organizational culture or decision-making routines. Moreover, the sectoral and contextual focus of the analysis limits the generalizability of the results. These limitations point to promising avenues for future research, including the integration of qualitative evidence with multivariate statistical analysis, cross-country comparative studies and the extension of the framework toward predictive and longitudinal designs capable of capturing transition dynamics more explicitly.\\u003c/p\\u003e \\u003cp\\u003eOverall, the discussion reinforces the central argument of the paper: understanding digital marketing adoption requires analytically rigorous and theoretically informed approaches capable of capturing multidimensionality, heterogeneity and temporal dynamics. By integrating advanced statistical techniques with substantive interpretation, the study contributes to bridging quantitative methodology and digital marketing theory, offering insights that are both methodologically robust and conceptually meaningful.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eE.B., M.G., G.P. contributed equally to:conceptualization;methodology;data curation;formal analysis;investigation;writing;review and editing;visualization;supervision;validation of the study.All authors have read and agreed to the published version of the manuscript.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eBackhaus, K., Erichson, B., Plinke, W., \\u0026amp; Weiber, R. (2016). \\u003cem\\u003eMultivariate Analysis: An Application-Oriented Introduction\\u003c/em\\u003e. 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Putting consumers to work. \\u003cem\\u003eJournal of Consumer Culture\\u003c/em\\u003e, 8(2), 163\\u0026ndash;196.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Digital marketing, Multivariate statistical analysis, Principal component analysis, Data-driven decision-making\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8407297/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8407297/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThe diffusion of digital technologies has reshaped marketing activities, fostering data-driven digital marketing strategies. However, empirical research often relies on descriptive indicators that fail to capture the latent and multidimensional nature of digital marketing adoption. This study addresses this limitation by proposing an integrated statistical framework for the analysis of digital marketing practices.\\u003c/p\\u003e \\u003cp\\u003eThe framework combines principal component analysis, cluster analysis and non-parametric combination (NPC) ranking to examine adoption patterns across firms and sectors over two time periods. The empirical results reveal persistent heterogeneity in digital marketing strategies and a gradual shift from tool-oriented adoption toward analytics-intensive and capability-based approaches. Although overall adoption increases over time, firms and sectors follow differentiated trajectories rather than converging toward a single model.\\u003c/p\\u003e \\u003cp\\u003eMethodologically, the study highlights the benefits of integrating complementary multivariate techniques to enhance interpretability and robustness. 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