Trade integration in digitally enabled economies: implications for brand scalability in the EU27

In recent years, brand equity research has increasingly focused on service-dominant contexts, where high repeated interactions, consistency across touchpoints and co-creation processes shape brand value (Keller, 1993; Moon and Iacobucci, 2022; Vargo and Lusch, 2008). This stream of research has substantially advanced understanding of how brand equity is built at the consumer and firm levels, particularly experience-based and relational mechanisms. However, it has remained predominantly grounded in consumer- and firm-level analysis, raising broader questions about the structural conditions under which brand value can be sustained and scaled across markets.

In parallel, international marketing research has increasingly examined how digitalisation reshapes global branding, with particular attention to artificial intelligence, social media, big data and cloud computing (Leonidou et al., 2018; Sheth, 2020). While these technologies are widely recognised as central to contemporary branding strategies, existing research has predominantly explored their effects on consumer engagement, personalisation, and firm-level brand performance (Mustak et al., 2021; van Esch and Black, 2021). Brand equity research explains how value is created and managed at the firm and consumer levels, but it typically presupposes the existence of integrated markets, feasible coordination, and repeated interactions.

Despite the recognised importance of digital technologies in branding, their relationship with global market integration has rarely been examined within a unified empirical framework. Existing studies focus either on firm-level branding outcomes or on trade and digitalisation independently, offering limited insight into how digital capabilities condition the environments in which brands can scale internationally. As a result, the macro-structural conditions under which digital branding processes become scalable across national markets remain under-examined. Additionally, the role of broader economic and demographic context, such as GDP and population size, has received limited empirical attention.

This paper addresses this gap by building on international marketing research that examines how digitalisation reshapes global branding and export strategies (Leonidou et al., 2018; Sheth, 2020), while shifting the level of analysis from firm-level outcomes to macro-structural conditions. Specifically, the study examines how key digital transformation tools (artificial intelligence (AI), big data, social media, and cloud computing) related to countries' degree of global market integration across the EU27. Trade intensity is modelled as a macro-level indicator of global market integration and interpreted as a structural condition that may facilitate the international scalability of brands. In doing so, the analysis situates digital capabilities within a broader national context by accounting for economic size (GDP) and demographic scale (population) as control variables, with the following research question:

The study adopts a quantitative, country-level empirical approach that integrates insights from brand equity theory, international marketing, and digitalisation research. The following section develops the theoretical framework, clarifying how different digital capabilities relate to global market integration and why these matter for brand scalability. Section 3 describes the research design, data and model specifications. Section 4 presents the results. Section 5 discusses the findings considering the theoretical framework. Section 6 elaborates the implications and future research directions.

By bringing together brand equity theory, international marketing, and digitalisation research, this study reframes global branding as a structurally conditioned process rather than a purely firm-driven endeavour. It contributes to ongoing debates on how digital transformation reshapes international marketing by identifying a differentiated digital architecture of global market integration, in which artificial intelligence operates as an integrative capability, big data and cloud computing function as enabling infrastructures, and social media plays a more limited, engagement-oriented role. In doing so, the study highlights the role of national digital infrastructures and market integration in shaping the structural conditions under which brand scalability becomes possible.

Brand equity has traditionally been conceptualised at the consumer and firm levels, most prominently through Keller's (1993) Consumer-Based Brand Equity (CBBE) model, which frames brand value as arising from brand knowledge, associations, and resonance. Subsequent perspectives, including Service-Dominant Logic (Vargo and Lusch, 2008), further emphasise the relational and co-created nature of brand value, highlighting interaction, experience, and meaning-making as central mechanisms.

While these frameworks remain foundational for understanding how brand equity is formed and sustained, they presuppose conditions of organisational coordination and market integration that are not always present in global contexts. Before brand meaning can be developed and leveraged consistently across borders, firms should operate coherently across heterogeneous markets. Building on international marketing research on cross-border coordination, standardisation–adaptation, and global market integration, we refer to this precondition as brand scalability: the structural capacity to deploy, coordinate, and adapt branding activities across multiple markets under conditions of economic integration and digital connectivity (Cavusgil et al., 2014; Levitt, 1983; Zou and Cavusgil, 2002). This perspective aligns with macro-level debates on trade openness, digital globalisation, and national digital readiness, which emphasise the role of structural conditions in enabling cross-border economic activity. In this context, digital infrastructures are increasingly understood as foundational elements of global market integration, shaping the capacity of economies to coordinate and operate across borders.

Brand scalability is distinct from brand equity, referring to the macro-structural conditions that enable cross-market deployment, rather than to consumer-level value creation. This distinction reflects a separation between macro-structural conditions and firm-level processes. While the former define the environments within which cross-border branding becomes feasible, the latter concern how firms enact branding strategies within those environments. The present study focuses on the former, treating firm-level outcomes as downstream processes that are not directly modelled.

Digital technologies play a central role in shaping these conditions. At the macro level, digital infrastructures enable cross-border coordination, information processing, and system interoperability, which are essential for operating across integrated markets. Artificial intelligence, big data, social media, and cloud computing contribute differently to these structural conditions, supporting distinct but interrelated functions within digitally enabled economies. In this sense, digitalisation provides the infrastructural and analytical foundations upon which global market integration can occur, thereby creating the conditions under which brand scalability may emerge.

In the present study, countries' globalisation degree refers to the extent to which EU27 economies are integrated into international trade. It is conceptualised as a macro-structural condition associated with brand scalability and operationalised through the trade intensity indicator (imports plus exports relative to GDP). The rationale is presented in Section 3.2.

Scholarly work in international marketing and global branding has consistently identified AI as a particularly influential digital tool, especially through its capacity for advanced analytics, decision support and adaptive coordination (Vlačić et al., 2021). At the firm level, prior research has shown the transformative potential of AI in reshaping international business strategies, given AI supports personalisation, predictive analytics and aligns market-facing activities, contributing to more consistent and responsive decision-making in complex environments (Huang and Rust, 2020; Menzies et al., 2024).

Early work has identified critical clusters of AI applications relevant to branding, including neural networks and brand choice, chatbots and brand interactions, algorithmic recommendations and e-brand experiences (Varsha et al., 2021). These studies demonstrate that AI enables firms to process complex data patterns and to tailor interactions across multiple touchpoints. While much of this literature focuses on consumer- and firm-level outcomes, it also points to AI's broader role in facilitating coordination and scalability across markets.

Recent studies further highlight AI's capacity to support adaptive alignment between market offerings and heterogeneous market contexts. For example, Makosa (2023) shows that AI-driven alignment of messaging with consumer expectations can strengthen consistency across diverse cultural environments. Building on this, AI's predictive capabilities enable real-time responsiveness and behavioural insights (D'Souza et al., 2023).

At a macro-structural level, these capabilities are particularly relevant in trade-integrated economies, where coordination, adaptation, and large-scale information processing are prerequisites for effective cross-border operations. In this sense, AI contributes to global market integration by integrating large-scale data processing with adaptive coordination across markets, supporting the alignment of market-facing activities in trade-integrated environments. These integrative capabilities position AI as a central contributor to global market integration relative to other digital capabilities (Huang and Rust, 2020; D'Souza et al., 2023; Varsha and Kapoor, 2021; Van Esch and Black, 2021). While AI emerges as the most salient digital capability associated with global market integration, its effectiveness does not operate in isolation. The deployment of AI at scale presupposes the availability of complementary digital infrastructures that support data availability, processing capacity, and system interoperability. In this respect, big data and cloud computing play enabling roles by providing the informational and infrastructural foundations upon which advanced AI-driven capabilities can be developed and deployed across markets. This leads to the first hypothesis:

While big data's direct impact on brand equity is less prominent than AI's, it plays a critical supporting role as an enabling data infrastructure. Big data facilitates market-facing activities by providing detailed consumer and market insights, which serve as a foundation for effective coordination, personalisation and customer engagement across markets (van Esch and Black, 2021). Big data enhances analytical capacity by leveraging the “5 Vs” of big data (volume, velocity, variety, veracity, and value) to develop comprehensive data profiles that support large-scale information processing (Anshari et al., 2019). These capabilities allow firms operating across borders to manage heterogeneous market environments more effectively.

However, big data's role is primarily indirect, as it underpins analytical and predictive systems rather than independently coordinating cross-market activities. In this sense, big data provides the informational foundation required for large-scale coordination and advanced analytics in trade-integrated economies, allowing the deployment and effectiveness of AI and other digital capabilities (Anshari et al., 2019; van Esch and Black, 2021; Davenport et al., 2020; D'Souza et al., 2023). If higher levels of trade integration require more sophisticated data systems to manage cross-border complexity the following hypothesis is proposed:

Cloud computing functions as a foundational digital infrastructure that supports scalability, storage, and interoperability across digital systems. It provides cost-effective and scalable processing capacity, supporting real-time analytics and the integration of digital tools across geographically dispersed contexts (Mustak et al., 2021). These capabilities are particularly relevant in international settings, where digital systems must operate consistently across markets. However, cloud computing does not directly coordinate cross-market activities; rather, it is linked to the deployment and functioning of more advanced digital capabilities, particularly AI and big data. In this sense, cloud computing provides the infrastructural foundation required for scaling data-intensive operations and supporting coordination in trade-integrated environments (Mustak et al., 2021; Anshari et al., 2019). Accordingly, the following hypothesis is proposed:

Scholars have consistently highlighted social media's role in enhancing brand visibility and fostering customer engagement; however, its influence operates primarily through awareness building and real-time interaction rather than through integrative coordination mechanisms (Davenport et al., 2020; Dropulić et al., 2022; Sharifirad, 2023). Social media platforms enable rapid dissemination of information and facilitate ongoing interaction with geographically dispersed audiences. While these dynamics are typically examined in relation to firm- and consumer-level branding outcomes, they also point to social media's broader contribution to cross-border visibility.

This real-time engagement helps brands to respond promptly to customer enquiries and feedback, which strengthens customer relationships, builds brand loyalty and ensures customer satisfaction, all of which are critical components of brand resonance (Tasci, 2020; De Oliveira Santini et al., 2020; Sürücü et al., 2019). While these mechanisms are well documented at the firm and consumer levels, their implications at the macro-structural level are primarily related to cross-border visibility, information diffusion, and engagement rather than to integrative coordination across markets.

Despite this largely indirect role, some authors have emphasised the importance of social media analytics (SMA) in extracting actionable insights from user interactions and behavioural data. This AAVG framework (Acquisition, Analysis, Value, Feedback) illustrates how SMA can be leveraged to structure information flows and support adaptive responses across digital touchpoints (Moon and Iacobucci, 2022) although such analytical capabilities are conceptually distinct from social media usage itself.

At a macro-structural level, social media usage is therefore expected to contribute to countries' globalisation degree primarily by enhancing firms' cross-border visibility rather than through the large-scale coordination or analytical integration associated with AI, big data, and cloud computing. While social media platforms facilitate interaction and connectivity across geographically dispersed audiences, their role in enabling global market integration is more limited and complementary when compared to integrative capabilities such as AI or enabling infrastructures such as big data. In this sense, social media contributes to global market integration primarily by increasing firms' visibility across markets, particularly when embedded within broader digital ecosystems that include more advanced analytical and infrastructural capabilities (Moon and Iacobucci, 2022; Dropulić et al., 2022; Tasci, 2020; Raji et al., 2019; Davenport et al., 2020; Sharifirad, 2023). Accordingly, this final hypothesis is proposed:

Taken together, the conceptual framework specifies differentiated roles for artificial intelligence, big data, cloud computing, and social media in relation to countries' globalisation degree, conceptualised as a macro-structural condition of global market integration. Artificial intelligence is positioned as an integrative capability, while big data and cloud computing function as enabling infrastructures, and social media as a complementary engagement-oriented capability. While the framework is conceptually informed by brand equity theory (Keller, 1993), the empirical analysis is conducted at the country level, with globalisation degree operationalised through trade intensity. This positions brand scalability as a downstream implication of macro-structural conditions rather than as the primary unit of analysis. The next section outlines the data sources, variable operationalisation, and analytical procedures employed in the study.

The data used in this study were sourced from three key repositories: the Digital Decade DESI Visualisation Tool (European Commission, 2024), for indicators of digital adoption (social media, big data, cloud and AI); the World Bank Open Data (World Bank Open Data, 2024) for economic and demographic variables (Gross Domestic Product and population data); and the Observatory of Economic Complexityfor trade data (country imports and exports; The Observatory of Economic Complexity, 2024). The sample consists of 27 EU member states, with all variables corresponding to the year 2023 to ensure temporal consistency.

Specifically, data on social media usage, big data usage, cloud usage, and AI usage were collected from DESI and are presented as percentages of enterprise adoption. GDP and population figures were obtained from the World Bank and reported in absolute terms. Trade data (imports and exports) were sourced from the Observatory of Economic Complexity and also reported in absolute monetary values.

All countries in the EU27 are included, ensuring full regional coverage. The unit of analysis is the country. This cross-sectional dataset was selected to examine macro-level associations between national digital adoption patterns and trade intensity, which in this study is interpreted as a macro-structural indicator of global market integration that conditions the environments in which brand scalability may emerge.

The analytical strategy is designed to examine macro-level associations between national digital adoption patterns and trade-based global market integration across the EU27. The analysis unfolded in four main steps. The first step consisted in quantifying each country's level of global market integration. For this purpose, we calculated a “globalisation degree” indicator based on the well-established trade intensity formula:

This ratio has been extensively employed in international economics and trade research literature as a proxy for economic openness and integration with global markets (Birdsall and Hamoudi, 2002). It captures the relative importance of cross-border trade within national economies and is widely used as a proxy for global market integration. Although it reflects primarily the economic dimension of globalisation, the trade-to-GDP ratio is particularly suitable for cross-national comparison within the European Union, where trade and GDP statistics are harmonised and consistently reported.

In the context of this study, the globalisation degree does not operationalise customer-based brand equity. Instead, it serves as a macro-structural proxy for country-level brand scalability, understood as the extent to which national economies are structurally embedded in global trade flows that enable brands originating in those economies to operate and scale internationally. This interpretation necessarily excludes meso- and micro-level branding outcomes such as brand attachment, resonance, or intimacy.

The second step was data transformation. To mitigate scale heterogeneity across variables and enhance cross-country comparability, all variables were transformed into rankings. Each country received a relative rank (1–27) for every variable, with higher values reflecting stronger presence (e.g. higher AI usage, higher GDP, greater trade intensity). This ranking approach aligns with precedent in cross-national comparative research and reduces the influence of outliers and skewed distributions. While ranking improves cross-country comparability and mitigates the influence of extreme values in small samples, it removes information about the absolute distance between observations. As a result, the estimated coefficients should be interpreted as reflecting relative positional changes rather than substantive magnitude differences.

The third step was to construct a regression model based on the Ordinary Least Squares (here in after OLS) methodology (Kartelj and Djukanović, 2023) to examine associational relationships between digital adoption variables and the globalisation degree ranking. The dependent variable is the globalisation ranking, while social media usage, big data usage, cloud usage, AI usage, GDP, and population are control variables. All variables are derived from the Digital Decade DESI Visualisation Tool, the World Bank and the Observatory of Economic Complexity (Table 1).

The choice of OLS was guided by three considerations: the nature of the data, the objective of the study and interpretability and parsimony. First, the sample comprises a complete population (EU27) for a single year and is therefore cross-sectional rather than longitudinal, making time-dynamic or fixed-effects models inappropriate. Second, the aim of the study is not causal inference but rather the identification of macro-level associations between digital tools and trade intensity as a proxy for brand scalability. Third, OLS offers a transparent and interpretable baseline approach suited to exploratory macro-level analysis with modest sample size (n = 27), where more complex methods (e.g. PLS-SEM) could obscure interpretation.

The fourth step explored enabling relationships among digital tools. Two additional OLS models were estimated to examine whether big data and cloud computing adoption are associated with the uptake of social media and AI technologies. These auxiliary models do not test mediation formally; rather, they provide exploratory evidence consistent with the proposed enabling logic. Model A estimates social media usage as a function of big data and cloud usage. Model B estimates AI usage as a function of the same predictors.

Multicollinearity was assessed using Variance Inflation Factors (VIFs) for all predictors. All VIF values remained below conventional critical thresholds, indicating no severe multicollinearity concerns (Table 2).

As additional robustness checks, the model was re-estimated excluding the two countries with the highest trade-to-GDP ratios to account for potential small-country openness effects. The substantive pattern of coefficients remained unchanged.

The correlation matrix (Table 3) indicates moderate correlations among several predictors, particularly between GDP and population, consistent with the VIF diagnostics. Importantly, correlations between the digital adoption variables and the globalisation index remain below levels that would suggest redundancy or mechanical overlap, supporting their inclusion as distinct predictors in the regression models.

To mitigate potential heteroskedasticity concerns, all models were estimated using heteroskedasticity-robust standard errors as a precautionary measure. The substantive pattern of coefficients remained stable across alternative specifications.

The main OLS model estimates the association between six independent variables: social media usage, big data usage, cloud usage, AI usage, GDP, and population, and the dependent variable, globalisation degree (operationalised as a trade-to-GDP ratio, transformed into ranks). The resulting model is expressed as:

The model yields an R² of 0.49 and an adjusted R² of 0.34, indicating that approximately 49% of the variation in globalisation rankings across EU27 countries is explained by the selected predictors (Table 4).

Among the digital adoption variables, AI usage displays the strongest positive association with globalisation degree, reaching statistically significance (β = 0.616, p < 0.05). Cloud usage also shows a positive relationship (β = 0.195) but is not statistically significant. Social media usage and big data usage exhibit weak or negative associations, and neither achieves statistical significance in the main model.

GDP rank has a strong negative effect (β = −1.234, p < 0.05), suggesting that more economically powerful countries tend to exhibit lower trade-to-GDP ratios, likely due to internal market size. Population rank has a marginal positive effect (β = 0.819, p ≈ 0.1). These results provide empirical support for H1. H2 and H3 are not supported in the main model, as their coefficients are not statistically significant. However, the auxiliary analyses provide evidence consistent with their proposed enabling roles. H4 is not supported. Table 5 summarises the model coefficients and t-values.

To explore the enabling relationships among digital technologies, two additional OLS models were estimated. These auxiliary analyses are exploratory in nature and do not constitute formal mediation tests. Their purpose is to examine whether big data and cloud computing adoption are associated with the uptake of higher-order digital tools.

Model A estimates social media usage as a function of big data and cloud usage:

This model explains 61% of the variance in social media adoption (R² = 0.61). Cloud usage shows a statistically significant positive association with social media usage (β = 0.376, p < 0.01), while big data usage approaches conventional significance levels (β = 0.460, p ≈ 0.05).

Model B estimates AI usage as a function of the same predictors:

This model explains 57% of the variance in AI adoption (R² = 0.57). In this model, big data usage exhibits a strong positive association with AI usage (β = 0.333, p < 0.01), while cloud usage shows a weaker, marginal association (β = 0.101, p ≈ 0.05). Coefficient estimates for both auxiliary models are reported in Tables 6 and 7.

Both models show acceptable explanatory power, with R² values falling within typical ranges for cross-national social science research (Ozili, 2023). Taken together, these results indicate that big data and cloud computing are closely associated with the deployment of social media and AI technologies, consistent with their interpretation as enabling digital infrastructures rather than as direct predictors of globalisation degree.

These findings refine the interpretation of H2, H3, and H4. While H2 and H3 are not supported as direct associations with globalisation degree in the main model, the auxiliary analyses provide evidence consistent with their proposed enabling roles. These auxiliary findings should not be interpreted as confirmatory support of the original hypotheses, but rather as evidence consistent with an enabling relationship. H4 is not supported. Social media usage appears to function as a complementary capability whose macro-level contribution is contingent on the presence of more advanced digital infrastructures.

This pattern is consistent with a layered digital architecture in which AI is most closely associated with global market integration, while big data, cloud computing, and social media primarily fulfil indirect or enabling roles.

Results reveal a differentiated digital architecture underlying countries' capacity to participate in globally integrated markets. AI emerges as the most structurally salient digital capability associated with globalisation degree, while big data, cloud computing, and social media occupy secondary but structurally distinct and largely enabling positions within that architecture. When interpreted through a branding lens, these patterns help contextualise the macro-structural conditions under which brand-related processes may unfold across borders. Brand scalability provides the analytical logic, while brand equity offers a theoretical lens through which these structural conditions can be interpreted. From a theoretical perspective the results align with earlier work highlighting the pivotal role of digital transformation in reshaping organisational capabilities and competitive positioning (Vial, 2019; Hanelt et al., 2020). In particular, AI stands out as the most significant capability associated with globalisation degree, consistent with research that situates AI at the core of personalisation, trust formation, and emotionally resonant brand experiences at the firm–consumer level (Huang and Rust, 2020; Makosa, 2023; D'Souza et al., 2023).

Beyond its overall prominence, the findings also point to the contextual relevance of AI in trade-integrated economies. Countries with higher levels of international trade are characterised by greater coordination demands, exposure to heterogeneous markets, and reliance on scalable digital infrastructures. In such contexts, the integrative and adaptive capabilities associated with AI appear particularly consequential, supporting global market integration and the structural foundations of brand scalability. Taken together, these findings support H1, reinforcing the proposition that AI is the digital capability most strongly associated with globalisation degree of EU27 countries.

Big data and cloud computing play important, albeit secondary, roles within this digital architecture. However, the auxiliary analyses clarify their function as enabling layers that condition the effectiveness of higher-order digital capabilities.

Big data contributes to the conditions underlying brand scalability by rendering markets legible at scale. Through the aggregation and structuring of diverse data streams, it supports the analytical depth required for cross-market coordination and adaptive positioning (Anshari et al., 2019; van Esch and Black, 2021). Cloud computing, in turn, supports the infrastructural conditions required to deploy data-intensive systems consistently across geographies (Ashari et al., 2019; Mustak et al., 2021). Their contribution is therefore indirect but essential, sustaining the conditions under which AI-driven coordination becomes possible.

This configuration refines the interpretation of H2 and H3. While not supported as direct associations, the auxiliary analyses provide evidence consistent with their enabling roles. Big data and cloud computing function as infrastructural layers that condition the deployment of integrative digital capabilities, consistent with their positioning as foundational rather than expressive technologies (Dovgal and Doval, 2021).

The weak and negative association between social media usage and globalisation degree challenges assumptions that digital presence alone translates into scalable global integration. While social media remains central to awareness, engagement, and interaction at the firm level, its macro-structural contribution appears limited when considered in isolation. This pattern may also reflect the nature of the DESI indicator, which captures enterprise-level adoption and may therefore reflect domestic marketing digitalisation rather than cross-border coordination capacity.

These findings align with research suggesting that social media supports brand awareness and customer engagement, with more limited relevance for the deeper processes through which brands achieve cross-market coherence and durability (Sharifirad, 2023). At the macro level, its contribution appears contingent on integration within advanced digital infrastructures, often functioning as an amplifier of AI-driven strategies rather than as an independent driver of brand performance (Moon and Iacobucci, 2022; Davenport et al., 2020).

Social media facilitates communication across borders, but it does not resolve the integrative demands associated with operating in trade-intensive environments. The auxiliary models reinforce this interpretation by positioning social media as dependent on cloud infrastructure and complementary to AI-driven strategies rather than as a standalone capability; that is, social media's contribution appears to lie in amplification rather than integration (Dropulić et al., 2022; Sharifirad, 2023; Davenport et al., 2020). Its relevance for brand scalability is therefore contingent, enhancing reach and interaction once structural coordination mechanisms are in place, but insufficient on its own to support global market integration. Accordingly, H4 is not supported. Rather than acting as an independent driver of countries' globalisation degree, social media appears to fulfil a complementary role within broader digital ecosystems.

Together, these results point to an integrated digital architecture in which AI occupies a central, integrative position, supported by the enabling functions of big data and cloud infrastructure (Table 8).

The model explains 49.27% of the variance in globalisation degree across EU27 countries, a substantial proportion given the multidimensional nature of global integration processes. Within the social sciences, R² values in the range of 30–50% are commonly considered robust, particularly in cross-national analyses involving structural and institutional variables (Voukelatou et al., 2020). This level of explanatory power underscores the relevance of digital transformation tools, especially AI, in shaping countries' capacity for global market integration.

The analysis is necessarily bounded. Globalisation degree captures the economic dimension of integration but excludes cultural exchange, FDI, and broader digital connectivity. Likewise, while brand equity provides an interpretive lens, its emotional and psychological dimensions operate downstream from the macro-structural conditions examined here and cannot be directly captured at the country level (Makosa, 2023; Varsha et al., 2021).

This study provides a novel, integrative framework to examine how digital transformation tools (AI, big data, social media, and cloud computing) shape globalisation dynamics and the structural conditions for brand scalability across EU27 countries. Its central emphasis on AI as a structurally salient capability, supported by complementary digital enablers, responds to calls within international marketing to better integrate digitalisation and globalisation research (Leonidou et al., 2018; Sheth, 2020). In doing so, the study clarifies where digital transformation exerts influence within the branding process, upstream of firm- and consumer-level brand equity formation. More specifically, digital transformation influences branding indirectly by shaping the structural conditions within which branding activities are undertaken. At the country level, digitally mature and trade-integrated environments facilitate firms' ability to coordinate, deploy, and adapt branding activities across markets. These conditions create the context within which firm-level branding strategies and subsequent consumer-level processes, such as trust, attachment, and loyalty, are more likely to develop.

This article extends prior work by Leonidou et al. (2018), by embedding digital tools within international marketing and globalisation frameworks at the macro level. By modelling the associations between digital capabilities and countries' globalisation degree, the study shows how national digital infrastructures are associated with trade integration, interpreted as a structural precondition for brand scalability, while accounting for demographic and economic conditions such as population size and GDP.

From a theoretical perspective, the findings contribute to brand theory by situating consumer- and firm-level frameworks within a macro-structural context. Keller's (1993) CBBE framework and Service-Dominant Logic (Vargo and Lusch, 2008) explain how brand value is created through meaning, experience and interaction, but provide limited insight into the macro-structural conditions under which these branding processes can be coordinated across international markets. Our contribution lies in in identifying market integration and digital maturity as structural characteristics that shape how cross-market branding activities can be coordinated and deployed consistently. The study therefore complements rather than extends these established frameworks by clarifying the macro-level settings within which their underlying branding processes are most likely to operate, without proposing modifications to the theories themselves.

Within this framework, AI emerges as a digitally integrative capability associated with global market participation, while big data and cloud computing operate as enabling infrastructures rather than independent drivers, consistent with prior conceptualisations of layered digital architectures (Huang and Rust, 2020; Davenport et al., 2020; Mustak et al., 2021).

Importantly, the weak and negative association between social media usage and globalisation degree challenges assumptions in branding and engagement research that digital visibility translates into scalable international integration (Tasci, 2020; Dropulić et al., 2022). This finding reinforces distinctions between expressive digital tools, which enhance awareness and interaction, and integrative digital capabilities, which support coordination across heterogeneous markets showing that digitalisation contributes unevenly to global scalability.

The findings also speak to debates on AI-enabled personalisation with global brand consistency (Huang and Rust, 2020; Makosa, 2023). Rather than resolving this tension, the results indicate that its relevance varies across contexts, with AI's association with global integration more pronounced in digitally mature, trade-active environments. This reframes the debate towards understanding when AI-driven strategies are more likely to support scalable brand deployment.

Although the analysis focuses on the EU27, the conceptual logic is transferable to other regional contexts where digital infrastructure and economic openness intersect.

For international marketing leaders, these findings offer a diagnostic lens to assess where brand strategies are likely to scale structurally, and where infrastructural constraints may limit the returns to firm-level branding investments. The findings suggest that AI may represent a strategic investment for firms seeking to operate and scale brands across borders. Its influence appears associated with the structural environments within which brands create relevance, trust, and emotional attachment in diverse markets. To realise these gains, firms may need to embed AI more deeply into CRM systems, personalisation engines, and localisation strategies. However, AI cannot function in isolation. Its effectiveness is contingent on access to structured, real-time data and flexible, scalable infrastructure; highlighting the enabling roles of big data and cloud computing. This implies that investments in AI and related infrastructures should be evaluated not only in terms of immediate performance outcomes, but also in terms of their role in enabling consistent brand activity across markets.

These implications should be interpreted cautiously, as the analysis is conducted at the country level rather than at the firm level. While many large firms already treat big data and cloud infrastructure as core resources, our findings suggest that countries and regions lagging in these enablers may structurally constrain their firms' ability to compete globally, particularly in AI-intensive sectors. This stresses the importance of shared infrastructures, data-sharing initiatives, and supportive public policy.

In digitally mature environments, big data and cloud infrastructure function as background conditions shaping the feasibility of coherent cross-market branding strategies. These tools support adaptation and integration across markets, particularly in trade-active contexts. Social media remains relevant for awareness, but increasingly operates as an amplifier of AI-driven insights rather than a stand-alone strategy.

At the policy level, the study emphasises the need to frame AI as a trade and branding enabler. EU27 support of AI adoption, cloud interoperability, and data standardisation will be critical to ensuring that firms can operate cohesively across markets. In this way, digital transformation may contribute not only to economic integration but also to the conditions under which b consistent cross-market brand activity can be sustained across the European Union.

While this study provides a country-level empirical analysis linking digital transformation tools to global market integration and the structural conditions for brand scalability across EU27 countries, several areas warrant further exploration.

First, the cross-sectional design limits causal inference. Longitudinal research could examine how the role of artificial intelligence, big data, and cloud computing evolves as digital infrastructures mature, and whether enabling relationships change over time. Future research could also explore interaction effects between digital capabilities and structural factors such as economic size or population, as well as the negative association of social media usage, for example through threshold effects, substitution dynamics, or complementarities with integrative capabilities such as AI to assess whether these relationships vary across national contexts.

Second, while the present study deliberately focuses on macro-structural conditions, future research could connect these conditions to downstream brand-level phenomena in a sequenced manner. Once structural readiness is established, incorporating consumer-based brand equity measures could help examine how national digital environments condition the emergence of brand attachment, trust, or symbolic value at the firm and consumer levels, rather than treating these dimensions as directly observable outcomes of digitalisation. This would enable a more explicit multi-level articulation between macro-structural conditions and firm- and consumer-level branding processes.

A mixed-methods research agenda also appears promising. In particular, a Delphi study involving international brand strategists, digital transformation leaders, and policymakers could further examine how digital infrastructures are prioritised and coordinated in practice. Such an approach would be well suited to examining expert consensus and disagreement regarding the conditions under which AI supports cross-border brand scalability, and why engagement-oriented tools such as social media may fail to translate into structural global integration. Delphi-based insights would complement the country-level associations identified here by clarifying how structural conditions are interpreted and acted upon within real decision environments.

Finally, future research could move beyond geographic replication to examine how different configurations of digital governance, platform regulation, and data sovereignty shape the role of AI in global market integration. Comparing countries with similar trade openness but divergent digital policy regimes could offer deeper insight into the institutional conditions under which AI supports brand scalability.

This study contributes to international marketing by reframing digital transformation not as a set of firm-level branding tools, but as a macro-structural condition shaping the conditions under which global brand deployment becomes possible. By examining digital infrastructures alongside trade integration at the country level, the analysis shifts attention away from digital adoption per se towards the environments in which branding strategies become scalable, constrained, or unevenly viable across markets.

In doing so, the paper challenges a prevailing tendency in digital branding research to treat visibility, engagement, and technological sophistication as inherently scalable assets. The findings instead suggest that global brand scalability is associated with integrative digital capabilities embedded within broader economic and institutional infrastructures. This perspective reframes how branding theory relates to digitalisation, positioning brand equity formation as a downstream process that presupposes structural readiness.

Seen in this light, digital transformation emerges less as a universal accelerator of global branding and more as a selective infrastructure whose effects are contingent on national digital maturity and market integration. Recognising this contingency is essential for advancing more differentiated and layered accounts of digital globalisation within international marketing scholarship.